I posted 3 times in August, including:
Previously in the “Best of…” series:
I posted 3 times in August, including:
Previously in the “Best of…” series:
Sharks are not known for being good at running in running wheels. Or hopping from one perch to the other in a birdcage. Which is why, unlike hamsters or sparrows, sharks were never a very popular laboratory model for circadian research.
The study of fish came late into the field of chronobiology due to technical difficulties of monitoring rhythms, at the time when comparative tradition was starting to make way to the more focused approach on choice model organisms – in this case, the zebrafish.
But the comparative tradition was always very strong in the field. Reading the old papers (especially review papers and loooong theoretical papers) by the pioneers like Jurgen Asschoff and Colin Pittendrigh, it seems like researchers at the time were just going around and saying “let me try this species…and this one…and this one…”. And there were good reasons for this early approach. At the time, it was not yet known how widespread circadian rhythms were – it is this early research that showed they are ubiqutous in all organisms that live at or close to the surface of the earth or ocean.
Another reason for such broad approach to testing many species was to find generalities – the empirical generalizations (e.g,. the Aschoff’s Rules) that allowed the field to get established, and that provided a template for the entire research program, including refining the proper experimental designs.
Finally, this was also a fishing expedition (no pun intended…oh, well, OK, intended) for the best model organisms on which to focus more energies – organisms that can be studied in great detail in both field and lab, that are easy to find, breed, care for, house and handle, and organisms in which circadian rhythms are clear, robust, and are easy to monitor with relatively cheap and simple equipment. Thus hamsters, cockroaches, and sparrows, green anoles and Japanese quail. Later, with molecular discoveries, organisms with better tools for genetic manipulation, even though perhaps not as good as circadian models, took precedence – the fruit fly, mouse, zebrafish and the like.
But it’s not that sharks were never looked at before. They may not run in wheels, but researchers can be creative and monitor the rhythms nonetheless.
Horn Shark and Swell Shark
The Nelson and Johnson 1970 paper appears to be the very first systematic study of daily rhythms in sharks. They cite a number of previous non-systematic observations in the field, all suggesting that many shark species are nocturnal (night-active). They combined field and lab studies in two species (horn shark Heterodontus francisci and the swell shark Cephaloscyllium ventriosum).
In the field, they dove at different times of day and night, counted and observed the sharks, and rated their activity levels. Both species were exclusively nocturnal, barely making any movements at all throughout the day, while actively swimming at night.
In the lab, they placed sharks in small pools, each pool in a light-tight enclosure. They controlled lighting regimes (e.g., constant dark, constant light, or various light-dark cycles) and they monitored the activity with a nifty sensor – a set of six steel rods in each pool, each rod hanging from above all the way to the bottom of the water. Whenever a fish pushed one of the rods (and they did not observe any avoidance), the rod would move and momentarily close an electrical circuit. This would be recorded as a dash line on long paper rolls by an Esterline-Angus recorder.
Afterward, they would take those paper rolls out, cut them (by hand) into strips, glue the strips (by hand) onto large pieces of cardboard, do the measurements and calculations (by hand, using rulers and compasses), and photograph the best records for publication. Yes, very manual work! In this day of computers, it’s pretty easy to just click. Our PI used to sometimes take us grad students to a back room to show us the old equipment and to describe the process, just so we would appreciate how easy we have it now.
What they found is that the two species are quite different. The Horn shark readily entrained to the light-dark cycles (both 24-hour and 25-hour cycles), starting activity as soon as the lights go off, and ceasing activity the moment the light come back on. They kept swimming all the time both in constant darkness and in constant light. This suggests that their behavior is triggered directly by environmental light and not driven by an internal clock.
On the other hand, the Swell sharks showed circadian rhythms – they alternated between active and inactive periods in constant light and in constant darkness. In light-dark cycles of both durations, they showed a little bit of anticipation, starting their activity a few minutes before lights-off. This suggests that the daily alteration of behavior is driven by an internal circadian clock.
In a later study (Finstad and Nelson 1975), they changed the intensity of light of the experiment, and this time Horn sharks also exhibited internally generated circadian rhythms.
In 1979, Casterlin and Reynolds tried a different experimental setup and a different species – smooth dogfish shark, Mustelus canis. In their setup, as sharks swim through a series of chambers they break photocell-monitored light beams. Instead of simple light-dark cycles, they used light-dusk-dark-dawn cycles in which dawn and dusk light was dim, while daytime light was bright. Again, most of the activity was observed during the night:
In 1988, Nixon and Gruber took a bunch of Lemon sharks (Negaprion brevirostris) and placed them in a complex setup in order to simultaneously monitor both locomotor activity (that is: swimming around and around in circles) and the metabolic rate (oxygen consumption):
The sharks were only tested in light-dark cycles, which is not a proper test for the existence of the circadian clock, but the data were strikingly “clean”. While behavior can be strongly affected by direct influence from the environment (e.g., sudden lights-on), it is harder to explain changes in metabolic rate purely behaviorally, suggesting that an internal clock is likely driving the day-night differences in metabolism.
This big guy is hard to find. The subject of this paper was only the sixth individual known to science. It was caught, they scrambled for about a day to get all the gear in place, attached satellite telemetry radiotransmitters, and let the animal lose to swim. What they saw was a distinct pattern of diving deeper before the sunrise, and rising up closer to the surface before sundown. While nothing can be said about circadian regulation, as the pattern could just be the animal following light clues or vertical migration of its plankton food, it is nonetheless a very cool study.
It is interesting that a number of senior researchers, as they come close to retirement and are not in the rat-race for grant funding any more, abandon the standard lab models and go back to the old comparative tradition, picking unlikely species (from chipmunks to Monarch butterflies) and moving out of the lab back into the field. It’s definitely more fun to do!
One of them decided to shift his focus to juvenile hammerhead sharks. Unfortunately, Milton H. Stetson suddenly died in 2002, and I could only find one publication from that work (Okimoto and Stetson 1995), which I cannot read as it was published in a conference proceedings (if anyone can scan a copy and send me, I’ll be grateful):
Nonetheless, this paper was cited in several other places, and if they cited it correctly, what Okimoto and Stetson found was that the pineal glands of these sharks (and later the same also found in dogfish shark Squalus acanthias) does not show cycles of melatonin synthesis and release in constant light conditions (it does in light-dark cycles). This does not necessarily mean that there is no clock in the pineal, or that there is not rhythmic production of melatonin, as later work in our lab showed that culture medium can have a dramatic effect.
In Graham, Roberts and Smith 2006, nine whale sharks were tagged with archival satellite tags which provided data on water temperature, illumination and depth. What they found are three distinct types of rhythms: ultradian (short), circadian (about a day) and infradian (long) cycles.
The short cycle was about 45 minutes long, essentially the sharks swimming up an down underneath the surface, not really diving very deep.
The long cycle was a 29-day cycle, likely not generated from within the nervous system of the shark, but rather the animals following the snapper spawning events which are modulated by the moon phases.
The daily cycle was that of deep dives. The sharks made very deep dives – sometimes over a kilometer down – only during the day. Again, nothing in this experimental protocol can distinguish between internally generated rhythms and behaviors directly induced by the environment, e.g., light intensity, vertical migrations of prey, etc.
And yes, this is it, that’s all. Not much work on sharks done, for obvious reasons – they don’t do well in running wheels.
Casterlin, Martha E., and William W. Reynolds. Diel activity patterns of the smooth dogfish shark, Mustelus canis. Bulletin of Marine Science 29.3 (1979): 440-442.
Finstad WO, Nelson DR. Circadian activity rhythm in the horn shark, Heterodontus francisci: effect of light intensity. Bull. S. Calif. Acad. Sci, 1975
Graham, Rachel T., Callum M. Roberts, and James CR Smart. Diving behaviour of whale sharks in relation to a predictable food pulse. Journal of the Royal Society Interface 3.6 (2006): 109-116.
Nelson, Donald R., and Richard H. Johnson. Diel activity rhythms in the nocturnal, bottom-dwelling sharks, Heterodontus francisci and Cephaloscyllium ventriosum. Copeia (1970): 732-739.
Nelson, Donald R., et al. An acoustic tracking of a megamouth shark, Megachasma pelagios: a crepuscular vertical migrator. Environmental Biology of Fishes 49.4 (1997): 389-399.
Nixon, Asa J., and Samuel H. Gruber. Diel metabolic and activity patterns of the lemon shark (Negaprion brevirostris). Journal of experimental Zoology 248.1 (1988): 1-6.
Okimoto, D. K., and M. H. Stetson. Effect of light on melatonin secretion in vitro from the pineal of the hammerhead shark, Sphyrna lewini. Proceedings of the Fifth International Symposium on Reproductive Physiology of Fish, The University of Texas at Austin. 1995.
Images: Shark in the running wheel: shark from ClipArt Supply, wheel from Shaping Youth, photoshop by Tobias Gilk. Shark clock – ToadAndLily on Etsy (where you can actually buy the clock). Other images are figures from papers, according to the Fair Use principle.
ScienceWriters 2013 conference, organized jointly by National Association of Science Writers (NASW) and the Council for the Advancement of Science Writing (CASW), will be held this year on November 1-5, 2013, on the campus of The University of Florida in Gainesville.
You can follow the event on Twitter by following @sciencewriters and the hashtag #sciwri13.
As I have been over the past few years, I will be involved this year as well. I am a co-organizer of two sessions during the NASW professional development day:
On Saturday, November 2nd, 11:00 am to 12:15 pm:
In our new, rapidly changing media ecosystem, it is easier than ever to write about science — but harder than ever to be heard above the din, to build a reputation, and to make a living. How are science writers and journalists adapting to these shifting rules? Links, documents, data and transcripts, in addition to quotes, are expected by readers. How do today’s science writers use these ingredients to establish trust with online-only readers? How important is the brand name of the media organization vs. the byline of the writer? With researchers now able to directly communicate with the public, how has the role of the writer changed? These panelists, who occupy different niches within the Web-based media ecosystem, have successfully adapted to the new “rules,” and are helping shape the future of science communication. Twitter hashtag for this session is #vftf13.
Mollie Bloudoff-Indelicato, Editor, EverydayHealth
Kelly Poe, Reporter, Greensboro News & Record
Cassie Rodenberg, Freelance, blogger at Scientific American
Julianne Wyrick, Student, UGA Program for Health and Medical Reporting
On Saturday, November 2nd, 3:45 pm to 5:00 pm:
Science writers must produce written, audio or visual stories that capture and hold the attention of a reader/listener/viewer. With so much information just one “swipe” away, editors and consumers are demanding stories that stay fresh and relevant long after the initial post. The one-word solution to such predicaments? Statistics. In this session, science writers with deep backgrounds in mathematics will provide key takeaways attendees can use immediately to help their stories rise above the noise. The takeaways will include: necessary vocabulary for talking about statistics, a framework for understanding how numbers can be manipulated, a checklist to ensure quality data, and, not least, examples of stories built solidly with statistics. Statistics is not a “catch-phrase” for serious journalism. It is key for better reporting and better story-telling.
Hilda Bastian, Blogger & editor, National Center for Biotechnology Information (NCBI) at the National Institutes of Health
Evelyn Lamb, Mathematician & writer, Scientific American
Regina Nuzzo, Freelance journalist & associate professor of statistics, Gallaudet University
John Allen Paulos, Author & mathematics professor, Temple University
I posted 4 times in July, including:
Previously in the “Best of…” series:
Scientific papers usually don’t faithfully convey exactly how the researchers came up with the idea, or the chronological order in which the investigation proceeded. And there is a good reason for that – papers need to be standardized so other scientists can easily read them, understand them, replicate them and use them to perform further research.
But sometimes, a paper is honest about the process. It is wonderful – and shows that scientists are human, with a great sense of humor – when #OverlyHonestMethods sneak into the text of a scientific paper, surprising and rewarding the careful reader with an ‘easter egg’.
One such paper – on the effects of moon phase of sleep quality – just came out in Current Biology.
The first thing I noticed was that the data were collected in 2000-2003. Why did it take a decade to publish? Was it just sitting on a back burner of a PI for years after the student left the lab? Did it have to go through many rounds of peer review in several journals until it finally managed to get published? None of those reasons, actually! See for yourself:
We just thought of it after a drink in a local bar one evening at full moon, years after the study was completed.
But jokes aside, this is also a great example of a paper that usefully re-visits and re-analyzes old data sets. Of course, the authors emphasize the positives of this post hoc approach – nobody at the time of the study could possibly know that the data would be analyzed in this way, so there were no possible subconscious psychological effects – it was a truly triple-blind study:
Thus, the aim of exploring the influence of different lunar phases on sleep regulation was never a priori hypothesized, nor was it mentioned to the participants, technicians, and other people involved in the study.
On the other hand, a study specifically designed to test for moon-phase effects on sleep quality would have been designed differently to ensure it has just the right controls and that maximum information can be derived from the data.
Research in chronobiology is frustratingly slow. In circadian research, each day is just one data point, so each study has to keep subjects in isolation for many days. In the study of lunar rhythms, each month is a data point and the subjects need to be kept in isolation for many months.
To determine if a rhythm is generated by an internal timer (daily or monthly) as opposed to being a direct behavioral response to environmental cycles requires a whole battery of tests, which are hard and time-consuming enough in circadian research, and twenty eight times more so in circalunar rhythm research
Back in the 1960s, it was possible to keep (well compensated) human subjects in isolation rooms for long periods of time (see pioneering research by Wever and Aschoff in the underground bunker in Andechs, Germany). Likewise, animal subjects can be kept and monitored in isolation chambers for long periods of time.
As lunar rhythms are more “messy” than daily rhythms, more data over more time are necessary for the robust statistical analysis. And, due to ethics creep, it is not certain that either animal or human studies of such scope can be approved and performed any more. So, one has to be creative and get quality information out of imperfect experimental protocols (just like we cannot wait to observe multiple cycles of 17-year cicadas, but have to invent creative, short-term approaches instead).
But this time, the researchers were just lucky! Their data-set came from an old experiment which was designed well enough for this new purpose. The key is they had LOTS of data. Their subjects came in to the sleep lab many times and a number of different parameters were measured. Ideally, each subject would stay in the lab for a few months instead of just four days at a time. But having such a huge data set allowed them to weave together a patchwork of fragmented data into a large, trustworthy whole. Each first night of the test was eliminated from the data due to potential influence of the previous day (and the so-called “weekend effect”, as people tend to change sleep times on their days off). Each phase of the moon was covered by multiple subjects multiple times. So they could employ powerful statistics to tease out the effects of the moon phase on various parameters of sleep quality.
And they found some interesting stuff! My colleague Dina Fine Maron has covered the paper in greater detail here. In short, human subjects with no access to information about moon phase, or any ability to perceive the moon itself or its light intensity, nonetheless slept about 20 minutes shorter on the nights of full moon, mostly due to taking roughly 5 minutes longer to fall asleep in the evening than on a night of the new moon. Levels of melatonin, hormone released by the pineal gland during the night, were lower during full moon nights as well. Some of the age and sex differences cannot be explained at this time due to imperfect experimental design – and that is OK. I’d rather see new interesting information coming out of an old data set, than never seeing it at all just because it cannot be “just perfect”.
There are many claims around about lunar periodicities in all sorts of human behavior. For some of those, there is no evidence the claims are true. For others, there is strong evidence the claims are not true. But a few subtle effects have been documented. This paper adds another set with persuasive statistics.
Is this a demonstration that there is a working circalunar clock in humans, operating endogenously, and independently from the actual moon? It’s not possible to tell yet. Those kinds of demonstrations (just like for circadian clocks) require a battery of tests, starting with documenting multiple cycles (I’d say at least three complete monthly cycles) in complete isolation, ability of artificial moonlight to phase-shift the phase of the rhythm in a predictable manner (consistent with a Phase-Response Curve), and hopefully identification of body structures or cellular components which are devoted to generation of the rhythms, with at least some hint of the mechanism how they do it.
We are far from it yet even in animals we can manipulate in lab and field studies. Much work has been done over the decades in the study of lunar and circalunar rhythms in various animals, mostly aquatic and intertidal ones. There are documented lunar cycles (but not necessarily internal lunar clocks) in a variety of organisms, including sponges, cnidaria, polychaetes, aquatic insects, and many different crustaceans including crayfish.
In the terrestrial realm, antlions possess internal lunar clocks, but many other species show modifications of behavior during different phases of the moon, including honeybees, rattlesnakes, ratsnakes, some rodents, some lizards, and lions.
The gravitational force of the moon is so weak that it can affect only very large bodies of water on the Earth’s surface. It cannot even affect smaller lakes and rivers. There is no theoretical mechanism by which any molecule or cellular structure in a human body can be so sensitive as to detect the gravity of the moon. So that hypothesis is out.
In field studies, animals can see and synchronize to the changing night-time intensity as the moon goes through its phases. But in the lab, as in the case of this study, there are no visual clues to the moon phase for the subjects, and, since they had no idea the data would be analyzed for moon phases, they probably did not pay attention to that before they entered the light-isolation lab.
With both gravity and light eliminated as potential clues, the internal clock remains the strongest hypothesis. But it’s still a hypothesis that needs to be tested before one can state with any certainty that it is the case.
As for evolutionary explanations for the existence of a putative lunar rhythm of humans? I would be very careful about this. Demonstrating that any trait is actually an adaptation (and not an exaptation or side-effect of development, or something else) is an incredibly difficult task. Just because something seems “obviously useful” does not make it an adaptation. It is an error of hyperadaptationism to pronounce a trait an adaptation just because it exists, and then to tack on a semi-plausible scenario as to how it may have been selected for. Evolutionary biology is much more rigorous than that kind of lazy armchair speculation.
Sure, if our ancestors actually had lunar clocks as adaptations, it is possible that the mechanism for it may still remain, even if in a weak state, in at least some of today’s humans. But maybe not. And like a rudimentary organ, it does not seem to have any obviously useful function for humans living in the modern society. Twenty minutes of less sleep, that’s all. But it’s good to know. So we can find good use to those extra twenty minutes, perhaps come up with new scientific hypotheses over a pint with colleagues at a local pub.
Reference: Cajochen et al., Evidence that the Lunar Cycle Influences Human Sleep, Current Biology 23, 1–4, August 5, 2013, http://dx.doi.org/10.1016/j.cub.2013.06.029
Images: top: by NASA, bottom: from the paper.
Earlier today I was on Google Hangouts, with the host P.Z. Myers, discussing science communication, the changing media ecosystem, how to push back against anti- and pseudo-science, and more. Take a look:
The Online News Association Conference is one of the most popular events in the field these days, where prestigious ONA awards are also given to innovators in online journalism. The ONA13 will be held in Atlanta this year, on October 17-19th.
Unfortunately, there is rarely anything on the program that specifically touches on science or health journalism, despite it being a somewhat different – and difficult – area of journalism with some very specific challenges.
Luckily you, the community of science and health readers, can help out. The Program is, at least partially, built through community vote. You can see all the session proposals here. You can ‘vote’ for any of them by clicking on the little heart icon (the “Like” on Tumblr) and/or by reblogging it on your own Tumblr.
If you scroll down again and again and again, you will finally reach the only science-related proposal: Science and Health Go Social: What Journalists Need to Know. You can help this session become a part of the official program by liking and reblogging it, perhaps adding your own commentary.
The proposed panelists are:
– Barbara Glickstein, Health journalist, public health nurse and the Co-Director of The Center for Health, Media & Policy at Hunter College City of New York.
– and me.
So, just click here and ‘heart’ the proposal and help us get there and start a discussion on challenges specific to science and health reporting in the rapidly evolving new media ecosystem.