New semester; snorkeling; insect identification; and statistics!

A new semester; new students; and an 85 F temperature forecast for lab.  It seemed like a perfect opportunity to really experience Lake Champlain.  I bought out all of the remaining masks and snorkels from a certain local store and we were ready to go.

My customary first lab in Community Ecology has been to test the species area relationship.  It seems like a simple enough concept: bigger area - more species, but the concept underlies important conservation biology topics such as reserve design.  The lab also provides an ideal entry point to community ecology.  Bigger areas do tend to have more species, but also a mathematical inconvenience: there are also more individual organisms.  Imagine any community with some finite number of species.  We sample 1 individual and we automatically have sampled 1 species.  As we sample more individuals we also sample more species.  Therein lies the mathematical problem: do larger areas have more species for some biological reason.....or is it just because we sampled more individuals?

On cool September days we typically wade into Lake Champlain in chest waders.  But the warm weather this year was perfect for snorkeling.  So in we went!  Snorkeling or wading, the procedure is simple enough: Find submerged rocks of diverse sizes, net them, measure them, and identify all of the attached invertebrates.

The lab has all of the ingredients necessary for an excellent learning experience: field work; sampling real communities; insect identification; data generation; analysis; writing; and the opportunity to get wet in Lake Champlain on the second day of a new semester was icing on the cake.  This year yielded a bumper crop of very tiny zebra mussels along with 24 other invertebrate species.  

By now there are 22 students writing lab reports based on the data set.  They have learned how to deal with the mathematical sampling issue using rarefaction, how and why to log transform their data set, and how to use linear regression to measure the strength of the relationship between rock area and the number of species sampled.

I'm looking forward to reading the lab reports and moving on to another successful semester in Community Ecology!

Aquatic insect identification......in your pocket

Science teachers are always on the lookout for the next idea to immerse students in hands-on science.  Collecting, identifying, and analyzing macroinvertebrate communities in streams may well be one of the most accessible and fun ways to literally immerse students in science.  Streams are ubiquitous habitats and students are often surprised to see the fascinating organisms that can be see by just picking up a rock or two.  And who doesn't enjoy cooling their feet in stream water on a hot day?

Figure 1.  Screen shot from Iphone app.         

Simply sorting invertebrates into ice-cube trays by type and quickly estimating the number of species is an easy way to get started.  Number of species is the first component of diversity.  Comparing the number of species found in two streams provides an excellent ecological lesson or could be the basis for a science fair project.    Saint Michael's College Biology students in my Community Ecology class take it several steps farther than that.  And of course they should take it farther; they are in an upper-level biology course.

Many students are interested in identifying the organisms they collect and professional taxonomic keys are readily available.  Comprehensive family-level keys designed for the mid west can be downloaded for free and work reasonably well for fauna of the north east.

Vermont EPSCoR has made an even easier tool to help with identification. Most streams and ponds host only a small subset of the organisms found in the comprehensive keys.  Using comprehensive keys requires long hours of reading detailed descriptions of arcane morphological descriptions to eliminate the large majority of organsism found in the key....but absent in your specific samples. The new tool focuses only on the organisms previously found in specific streams.  This means that a high school student collecting macroinvertebrates for the first time can identify them more accurately and in a fraction of the time it would take using traditional keys.

Figure 2.  An organism found in Brewster River.

We started by making web sites tailored to the few streams and ponds where we and our collaborating high schools sampled.  The list of streams and ponds has since expanded to include 78 sites in Vermont, New York, and Puerto Rico.  Each web site contains roughly 10 to 20 organisms making it possible to identify 95% of the organisms found at a particular site.  

Computer Science students at the University of Vermont have taken the tool one step further by developing an Iphone app.  The app can be synchronized before field work to load images from the web directly onto an Iphone.  The app can then be used at remote fields sites with or without a cellphone signal.

Students can collect an organism from Brewster River in Jeffersonville Vermont for example, pull out their cell phones or Ipads, and compare the catch of the day to a gallery of photographs of organisms found at that site (Figure 1).  A quick click on an organism's photograph reveals it's name and identification tips (Figure 2).

The web pages and Iphone app are based on a series of 138 mini web pages called templates that represent 138 different organisms that we have found in our stream and pond study sites.  The templates are like Lego bricks that can be used and reused in different stream and pond web sites.  At this point the bulk of the work has been done and it takes little time to create a new web page for a freshly sampled stream.  We can also rapidly add organisms to existing stream sites.  All new information is passed onto Iphones by synchronizing with the web sites.

I hope that this new tool can lower the taxonomic barriers a little bit and make working with macroinvertebrates in aquatic habitats a little easier for a tech-savvy generation. 

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Making trees: how are mammals related?

Figure 1.  An 1837 sketch from Charles Darwin's notebooks.

How do scientists tell what organisms are related to other organisms?  It's not easy to go back in time and visit with the common ancestor of chimpanzees and humans for example, so we need some approaches to place organisms into natural groups.  This was precisely what the Swedish biologist Carl Linnaeus first attempted in a 12 page publication called  Systema Naturae in 1735.  By 1758, the tenth edition included descriptions of 12,100 plants and animals.

Modern biologists are just as concerned with appropriately grouping related organisms as was Linnaeus.  Since the time of Darwin we have used tree diagrams to represented the relationships among related organisms (Figure 1).  Humans and chimpanzees for example can be placed on the tips of branches that can be followed back to a larger branch that represents the ancestor we share in common.  To see how we are related to monkeys, bats, dogs, or sharks, we'd have to track back closer and closer to tree trunk and then trace paths back out along more distant branches.  The length of that branch-tracking journey represents the distance of the relationship between any two organisms on the tree.

When I started teaching evolution at Saint Michael's College I wanted an authentic activity that students could complete to make an actual evolutionary tree.  It's quite frankly boring to simply study trees completed by others and memorizing the branching patterns seems utterly pointless.  My quest was for a prepared procedure that would walk students through the actual process used by evolutionary biologists to make the trees that we call phylogenies.

Figure 2.  Data from 2015 Saint Michael's College students.

My quest was fruitless and so I wrote my own student-accessible procedure based on the techniques used by professional biologists.  My students use a collection of real and replica mammalian skulls.  They come up with a laundry list of observable skull characteristics: canine teeth, forward facing eyes, snouts, notches, grooves, and whatever traits they can come up with and clearly communicate to their class mates.  Each skull is then scored for each trait: "1" represents "present"; "0" represents "absent".  A subset of this year's data is shown in Figure 2 and from it you can see that bobcats, and lynx have tiny molars and Y-shaped premolars that are not found in the other organisms on this subset of the larger list.  These unique traits together with many others can be used to infer that bobcats and lynx are related.

Figure 3.  The arbitrary starting tree; click to enlarge.

This year we measured 46 traits from 26 mammals and then placed the mammals on an arbitrarily drawn tree (Figure 3).  I describe the starting tree as "arbitrary" because no attempt was made to place related organisms on adjacent branches: bobcats share a branch with polar bears and sheep for example.  Obviously this is not ideal and more importantly, it suggests that tiny molars evolved bobcats, and again in lynx, and evolved independently three more times in lions, cougars, and Bengal tigers.  It makes more logical sense that tiny molars evolved in some ancient cat and that the trait was passed down to modern cats.  If this is true we should expect to see this traits in other cats and indeed we do see exactly that in the two other species we have in our collection: house cat and caracal cat.  Scientists call this "the principle of parsimony": why invoke 5 evolutionary events when just 1 is needed to explain the data.

We refer to the gain or loss of traits as "transitions" and we used software to count the 251 transitions needed to explain the arbitrary tree.  My students then move branches around on the tree and the software automatically recalculates the number of transitions.The goal is to generate the most parsimonious tree, or the tree with the smallest number of transitions.  This becomes competitive as student groups report out on their shorter and shorter trees during the lab session.  As a homework assignment, the students compare their trees to trees published by evolutionary biologists.  This places their work in the larger context and I have found that the comparisons generally fare very well.  As a result of writing this blog I think I'll ask my students to use their trees, together with published trees to write several hypotheses about the traits of skulls they have not yet seen.  Sounds like a whole new lab!

Figure 4.  A tree made based upon the observed skull traits.

What I don't tell my students is that the software has a feature that does the work automatically.  The software is unbiased; it won't place the polar bears near the brown bears just because that might make most sense.  Instead, the software makes groups that minimize the number of transitions and generates a tree based solely on the data (Figure 4).  This data-based tree requires 121 transitions and places organisms together in ways that very closely match the tree of life, a phylogeny generated by professional biologists.

Importantly, 15 students dreamed up 46 traits without consulting published work and without reference to what some other biologist might think of as a 'good trait'.  They worked in groups and the only criteria for choosing traits were: that they could communicate the trait to their peers; the trait should occur in at least 2 skulls; the trait could not occur in every skull.

This is our third year running this experiment.  We use different skulls each year to keep it interesting.  The list of traits that students come up with changes each year also.  If other teachers would like to run this exercise on skulls or on other organisms you can find all of the needed information in this short paper.

Figure 1 from Wikimedia Commons.  Other figures generated at Saint Michael's College. 

Take only pictures; camera trapping!

As a child, I remember seeing photographs of startled birds and mammals in nature magazines and National Geographic.  Knowing how hard it was to observe even common mammals like badgers near my home town of Athlone in Ireland, I was enthralled by the idea that the animals could trigger cameras and capture their own images.  In our teens, a good friend and I spent a number of nights up in trees near badger setts trying to catch glimpses of these fascinating animals.  We knew that they were there; we had looped briars around the entrances to their underground setts to catch hairs.  We failed to catch hairs because the badgers removed the briars, and we failed to capture all but the briefest glimpses that left us wondering what we had really seen.

Camera traps were out of reach for the average Irish teen, and indeed 1970s versions were not exactly user friendly.  Early traps were single-shot film cameras used by professional wildlife biologists.  They were expensive, challenging to use, and required wait time while film was developed.  Many things are different in 2014 and camera traps have dropped in price, increased in sophistication, and are very very easy to use.

This evening I removed the chip from a camera I installed in August.  It has been clicking away at passing animals without much care or attention from anyone.  Similar cameras have revolutionized wildlife biology and made wildlife photography available to a far broader range of people.  It's hard to say that my little camera trapping adventure has revolutionized much of anything, but it's a lot of fun for my favorite 8 year old to see what comes through our back yard.

The camera is installed on the corner of our deck at about 4 feet off the ground and pointing down at a 45 degree angle.  I have tried various angles and placements, and this approach seems to eliminate false triggers by windblown tree branches, and distant photos of out-of-focus birds and joggers.  Most of the photographs are actually of the animals I'd like to see. 

Birds and mammals trigger the camera by movement and photographs are taken in natural day light or using an infra red flash at night time.  It runs on a dozen rechargeable double-A batteries and stores photos on an SD card that slots right into my computer.  As I write this I'm glancing through literally thousands of photographs of backyard wildlife.  It's a far cry from the days of single shot cameras.

My daughter and I strategically place left over food in the path of the camera to attract birds and mammals.  After an October apple picking adventure, we placed a pile of apple peelings in the yard.  An opossum, who seems to be one of our most frequent visitors, spent several hours on three successive nights gorging on apple peels and shared the night shift with a cottontail rabbit.  A chipmunk and several grey squirrels took over the day shift and were joined by cardinals and blue jays.

Wildlife biologists are regular users of camera traps and an array of scientific questions have been answered using very similar cameras.  European investigators used camera traps to see how many of the baits left out for foxes were actually consumed by foxes as compared to non-target animals.  It turned out that hedgehogs were regularly consuming the baits, but simply burying the bait or placing it nearer to fox dens drastically improved success.

 Even our simple back yard setup is generating data that could easily be used to address a number of questions.  For example, the published accounts of cotton tail rabbits suggest that they are most active near dawn and dusk.  But in my back yard, I most often photograph rabbits after mid night.  Quite obviously I may well be photographing the same midnight bunny on several nights, but it does suggest that I should repeat the experiment in a different location and capture evidence from one or several other rabbits.

Our yard is visited by skunks, racoons, opossums, rabbits, squirrels, chipmunks, and several different neighborhood cats.  We have also captured photographs of several birds including catbirds, grackles, cardinals, bluejays, sparrows, juncos, and robins. Skunks have distinctive coat patterns and there are at least two that visit our yard.

I can't recommend a specific camera brand because I have only tried one.  I can say that I'm very happy with the Reconyx camera that I'm currently using.  An available metal housing makes it possible to string a cable lock through the housing and the camera reducing the possibility of theft.  However, I can strongly recommend camera trapping as a fun family activity and a way to connect to seldom seen animals that quietly slip through our back yards.  Once could very easily craft a fascinating science fair project or indeed publishable study with the budding scientist in your life.

All photographs taken using a Reconyx HC600 Hyperfire

Hard boiled selection?

On Thursday June 18th I walked from my office to a corn field on the Saint Michael's College campus. Along the way I nearly tripped over two painted turtles in the middle of the gravel road used to truck food waste to our compost facilities.  After perhaps pointlessly returning the first one to the nearest pond I recognized that the second one was digging a hole to lay eggs.

When I got as far as the corn field I found a third turtle backed up to a freshly covered hole where I presume she had deposited her eggs.  No eggs were visible and I was not about to dig to confirm my hunch.  In my days as a summer camp nature study instructor I learned that unlike bird eggs that actually require turning, turtle eggs should not be turned over during incubation.  Developing turtles attach to the inside surface of their eggs and turning can harm the growing embryo.

I did confirm that the cornfield was quite attractive turtle nesting habitat.  As I skirted the edge of the plowed and planted field I encountered no fewer than 17 turtle nests.  With the exception of the nest that was being completed, each and every nest that I found had been plundered and generally only egg shells remained.  I found intact eggs that had been dug up from just one nest but I suspect that digging up alone may have been sufficient to destroy the developing turtles.

With brief research I learned that it is not unusual for more than 90% of painted turtle nests to be plundered.  Racoons and skunks are frequent predators of turtle nests.  At first blush one might consider that all of this nest plundering would damage the turtle population.   However, turtles can reproduce each year for between ten and twenty years so there are multiple opportunities for each female to contribute to the next generation.

It is worth noting that I have no real way of knowing how many intact nests I walked past.  It seems that these turtles all lay their eggs in a relatively short time window.  This strategy is sometimes called "predator dilution".  If there is a large number of simultaneously available nests, then the chances of one particular nest being plundered is reduced.  There would therefore be selective pressure on turtles to nest at the same time as their neighbors.  Genes that favored solo nesting would be rapidly eliminated because each solo nest would be the only available target for predators.

Supporting the predator dilution hypothesis would require some field work and experimentation.  I'd first need to confirm clutch synchrony in the population; seems a worthy excuse for a regular walk to the cornfield.  An experiment to confirm that clutch synchrony was protective of the nests would be easy to design if not to implement.  I have no desire to in any way interfere with nesting turtles, but an experiment with an inexpensive substitute might do the trick.  My fake turtle nests might consist of shallow nests dug along the corn field and seeded with chicken eggs.  If predators are being satiated by availability of eggs, then I'd expect proportionally fewer eaten eggs where I placed 20 nests and proportionally more consumed eggs where I placed just 1 or 2 nests.

As with any good experiment, we should entertain alternative hypotheses.  In this case, it may be that the availability of multiple nests of eggs may attract multiple predators on a feeding binge.  One version of this hypothesis is called "local enhancement".  Hungry predators are drawn to rich food sources by the presence of other predators actively feeding.  It's far easier for a vulture to spot a flock of feeding vultures than to spot a carcass from the sky.  The same may well be true for fake turtle nests.

I'd need to replicate the experiment, collect, and analyze the data.  This how scientists know what we know: experimentation, replication, and data analysis.  This particular piece of science will remain a thought experiment for now.  But one never knows; there may just be a Saint Michael's College student in need of a viable project and this may be just the thing.  At least I'll gather the data on clutch synchrony next year and perhaps be a little fitter as a result.

All images for this post were taken on using a cell phone camera.

An asteroid strike, dinosaur extinction, and a bench-top fossil dig.

I teach a weekly hands-on laboratory as part of my evolution course each Spring at Saint Michael's College in Vermont. Fossils are essential components of the evolutionary biologist's toolbox and I wanted an engaging way to look at fossils that would generate quantitative data for student lab reports.  I'm lucky to to have access to the college's modest fossil collection.  The collection includes some fascinating examples but does not lend itself to a particularly quantitative laboratory exercise.

Show-and-tell with fossils has it's place but I had a more comparative laboratory in mind.  I wanted students to experience the sense of discovery of a fossil dig, generate quantitative data, and have a comparative question to answer using real data.  Microfossils turned out to be the answer!

Where ever marine sediments accumulate, the hard parts of microscopic organisms also accumulate.  These microfossils tend to be drawn from diverse and unrelated groups but are studied as a single category because they are all small, and are extracted from mixed sediments using the same techniques.

There certainly are abundant marine sediments right under our campus in the Champlain Valley, but they are from a limited number of time periods.  My interest in working in sediments of diverse ages brought me to one of my favorite sources of specimens: Ebay!  A fossil dealer in England offered a collection of pre-sifted fossiliferous sediments in labeled vials.  The samples come from sediments spanning a more than 200 million years of deposition.

I purchased vials that came from before and after the KT boundary that marks the time in Earth's history when the dinosaurs disappeared in a geological blink of the eye.  While various hypotheses regarding dinosaur extinction have been tested, an asteroid strike near the Yucatan Peninsula in Mexico is supported by the overwhelming force of evidence.

Dinosaurs capture the imagination and the headlines, but most other groups of organisms on the planet suffered severe declines at the time the non-avian dinosaurs vanished.  Conveniently, microfossils are drawn from several groups of organisms and so we can reasonably expect a reduction in the number of species found in microfossil samples following the KT boundary.

My students picked through the small vials of sand to isolate and count microfossils using techniques developed by Charlie Drewes. They then counted the number of distinct types of fossil in each sample and measured a diversity index.  After repeating this procedure for each vial, they ran statistical analyses to determine whether there was, as predicted, a fall in the number of species and diversity detected following the KT boundary.

I'm happy to say that the new laboratory was a success and will become part of the regularly scheduled labs in the Evolution course.

Image source: http://commons.wikimedia.org/wiki/File:Mikrofossils_hg.jpg

Artificial selection: simulating rapid evolution on a spreadsheet.

I was rereading Richard Dawkins' The Blind Watchmaker recently and became interested in one of the analogies he used to illustrate natural selection and artificial selection.  The analogy he presented was that of a monkey poking at keys on a typewriter.The chances of the monkey producing a particular phrase are vanishingly small and the time needed to repeat the process until the correct phrase was randomly typed would exceed the lifetime of the monkey and indeed several generations of monkeys.

I have access to neither a monkey nor a typewriter so I settled upon MS Excel instead.  I instructed Excel to randomly plop letters in each of 8 cells to see if I could match an 8-letter word: "CharlesD".  Not surprisingly, I never found a match despite running several thousand iterations of my experiment.  It illustrates beautifully the idea that random evolution could never work.  Expecting several random mutations to spontaneously generate an organism with traits that fitted it for a particular set of environmental conditions is similarly unlikely.

But evolution is far from a random process.  Certainly mutations can be random, but the survival and reproduction of the resulting mutants is certainly non random. If several mutants are about average, better, or worse at catching prey and therefore surviving and reproducing, then those better suited to predation will survive, reproduce, and pass those superior mutant genes in greater numbers to the next generation.  This is the process of natural selection as outlined by Darwin and Wallace in the 19th century.

Artificial selection is rather like natural selection on steroids.  A plant or animal breeder can artificially select some desirable trait over several generations.  Each time a particular plant or animal better matches that trait, it will be selected as breeding stock.  This brought me to a second simulation. Again MS Excel plopped random letters in cells in a spreadsheet row, but checked the letters against the target phrase "CharlesD".  Any letters that matched the target phrase were placed in the next row and new random letters or 'mutations' were placed in other the remaining slots.  Every time a letter matched one from the target phrase, it was retained and passed onto the next row or 'generation'.

I found that in this simulation the phrase evolves to match the target phrase in a short number of repetitions; often fewer than 100 generations.  One such simulation is presented below.  The letters plopped into the phrase are just as random as the mutant letters in the first simulation I mentioned above.  The crucial difference is that the target letters are retained in successive generations as would be the case if a cattle breeder selected for higher butterfat content of milk over generations.

Try it using your own phrase in this simple MS Excel sheet.

An example that converged on the target in 30 generations:

	Target phrase:	C	H	A	R	L	E	S	D
generation
1		H	D	A	V	R	X	P	U
2		T	K	A	L	G	L	U	N
3		G	I	A	P	P	X	B	P
4		W	S	A	W	S	U	O	J
5		I	W	A	R	X	V	O	C
6		Q	R	A	R	K	V	O	Z
7		P	W	A	R	G	X	M	L
8		C	J	A	R	U	I	G	P
9		C	P	A	R	A	E	T	A
10		C	W	A	R	I	E	M	F
11		C	H	A	R	Y	E	T	Y
12		C	H	A	R	I	E	F	I
13		C	H	A	R	Q	E	L	B
14		C	H	A	R	Q	E	D	E
15		C	H	A	R	E	E	L	W
16		C	H	A	R	J	E	Y	J
17		C	H	A	R	X	E	S	D
18		C	H	A	R	Z	E	S	D
19		C	H	A	R	Z	E	S	D
20		C	H	A	R	I	E	S	D
21		C	H	A	R	E	E	S	D
22		C	H	A	R	J	E	S	D
23		C	H	A	R	H	E	S	D
24		C	H	A	R	O	E	S	D
25		C	H	A	R	Z	E	S	D
26		C	H	A	R	M	E	S	D
27		C	H	A	R	M	E	S	D
28		C	H	A	R	P	E	S	D
29		C	H	A	R	U	E	S	D
30		C	H	A	R	L	E	S	D