Nick Sakich here. The first paper from my MSc has just been published in the Journal of Experimental Biology. The paper is entitled, “Bearded dragons (Pogona vitticeps) with reduced scalation lose water faster but do not have substantially different thermal preferences.”
In it, we examine both “wild-type” bearded dragons and two phenotypes unique to captivity (i.e. not found naturally): animals with scales of reduced prominence (known as “leatherbacks”) and completely scaleless animals (known as “silkbacks”). The following slideshow depicts the 3 variants:
There has long been speculation as to whether or not scales play a role in reducing evaporative water loss across the skin in reptiles. The seminal studies that most point to are by Licht and Bennett (1972) and Bennett and Licht (1975). Those authors looked at aberrant partially scaleless individual snakes found living in the wild and found that they did not have higher rates of water loss than “normal” snakes. However, these studies had some methodological issues, most notably sample sizes of only one (Licht and Bennett, 1972) and two (Bennett and Licht, 1975) partially scaleless snakes, respectively.
Furthermore, can reptiles (or lizards and snakes, at least) detect their rate of evaporative water loss and respond accordingly? If they can, animals with higher rates of evaporative water loss will perhaps choose cooler temperatures compared to animals with lower rates of evaporative water loss. The rate of evaporative water loss is partially thermally dependent, so for the animals this would be a way to compensate and bring their rate of evaporative water loss down.
In this study, we set out to test two hypotheses. First, we hypothesized that scales are indeed a barrier to evaporative water loss, and so leatherbacks and silkbacks would have higher rates of evaporative water loss than wild-types. Second, we hypothesized that, because of this increased rate of evaporative water loss, leatherbacks and silkbacks would have lower thermal preferences than wild-types.
We found support for our first hypothesis: both leatherbacks and silkbacks evaporated water faster than wild-types. It is likely that most of this occurs across the skin, rather than through changes in breathing or metabolism, given the simultaneous measurements we made of metabolism. This confirms what many who keep silkbacks as pets have long suspected. However, we didn’t find a statistically significant difference in thermal preference between the three phenotypes. This suggests that either leatherbacks and silkbacks can’t tell that they’re losing water faster than wild-types, or that they can tell, but they make a strategic decision to prioritize warmth over water.
I’d like to thank Arnold Liendo and Paula Rodriguez, Mandy Peck, and Kirk Riddle for supplying us with lizards for this study. I’d also like to thank Tom Eles and Wynne Reichheld, without whom keeping up with the nuts-and-bolts of animal acquisition and care would have been impossible.
Sakich, NB and Tattersall, GJ. 2021. Bearded dragons (Pogona vitticeps) with reduced scalation lose water faster but do not have substantially different thermal preferences.Journal of Experimental Biology.224 (12): jeb234427.
A link to the pdf of the manuscript can be found here (limited to 50 clicks). Otherwise, requests for pdfs can be made on Researchgate.
Licht, P. and Bennett, A. F. (1972). A scaleless snake: tests of the role of reptilian scales in water loss and heat transfer. Copeia 1972, 702-707. doi:10.2307/ 1442730
Bennett, A. F. and Licht, P. (1975). Evaporative water loss in scaleless snakes. Comp. Biochem. Physiol. A Physiol. 52, 213-215. doi:10.1016/S0300- 9629(75)80155-1
The following is a guest blog by Dr. Joshua Robertson Tabh
In my short research career, I’ve come to accept (even relish) that there are some projects that endlessly surprise; projects with shifting objectives that find you running drive-by thermal camera hand-offs along the QEW at questionable hours. The project that I’m about to describe is one of “those”. And curiously, despite the innumerable twists and turns, it just so happened to be a project with some of the most useful outcomes I’ve helped to produce. In this guest post, I’ll describe those outcomes.
But first, let’s begin in 2016. I had just begun my PhD research in avian stress physiology, and mere months before, Paul Jerem and others had released a highly intriguing protocol which suggested that the physiological stress response could be detected, and possibly quantified, in birds by simply measuring changes in body surface temperature (https://www.jove.com/t/53184/thermal-imaging-to-study-stress-non-invasively-in-unrestrained-birds). The rationale behind their protocol was that following exposure to a stressor, the sympathetic nervous system triggers vasocontriction of blood vessels at the skin (among other things), which manifests as measurable changes in skin temperature. This idea isn’t new. Rather, it likely dates back to the early 20th century or previous (e.g. Wolff and Mittelman, 1937). However, Jerem et al’s protocol was the first to show that a stress-induced change in skin temperature could be detected at the eye region in a wild bird, using infrared thermography (see Edgar et al, 2013, for a study in chickens). A clever application of thermography.
Jerem et al’s work was exciting. But a few important questions seemed to linger:
(1) how well does this stress-induced change in eye region temperature reflect circulating changes in sympathetic nervous system markers (i.e. catecholamines, like adrenaline and noradrenaline)?
So, being nagged by these questions, a team of ecophysiologists (Glenn Tattersall, Gary Burness, and Oliver Wearing), an endocrinologist (Gaby Mastromonaco), and myself sought answers.
To do so, we required an experimental approach that would allow us to measure both body surface temperature (here, at the eye region and bill) and circulating catecholamines in “stressed” and “unstressed” birds. However, measuring circulating catecholamines requires sampling blood. And since puncturing a vein with a syringe is surely sufficient to activate a physiological stress response on its own (thus rendering “unstressed” birds “stressed”) blood sampling by this standard method simply wasn’t possible. Ideally, we would fit a sample of birds with central venous catheters to permit blood sampling without capture and venipuncture. This approach could work, however, even if blood samples were to be collected effectively, catecholamines can be a pain to quantify, even for contracted labs with high-end machinery. It’s for this latter reason that we accepted the reality of leaving our first research question unanswered.
Nevertheless, we could persist with a simple experimental design to answer research questions (2) and (3); quite simply, thermographically image birds during rest (Fig 1) and during a stress exposure (for us, handling). To answer question (2), we would then quantify and compare the magnitude of stress-induced changes at the eye region and bill. And lastly, to answer question (3), we would aim to test the effect of head angle on our ability to detect stress-induced changes in eye region and bill temperature. In theory, a nice and clean approach.
Before I get to the answers of our remaining research questions, a small note on how we estimated head angle (for the interested reader).
Estimating Head Angle from 2D Image
Estimating the orientation of a 3D object from a 2D angle has been a concern for humans since photography was invented. Among mathematicians, this challenge has since acquired a formal name: the “perspective-n-point” (or “PnP”) problem. All solutions to the PnP problem first require knowledge of where, in a 2D plane, at least 3 points in an imaged object lay. We’ll call these points “landmarks”. Of course, more than 3 landmarks are best to improve estimation accuracy, but most agree that 3 will do for a reasonable guess. Next, rough dimensions of the imaged object in 3D space are needed. Such dimensions must be sufficient for one to estimate where the chosen landmarks may lie, relative to each other, in a theoretical 3D co-ordinate system known as the “world co-ordinate system”.
Once this information is collected, several geometrical approaches may be used to calculate how the imaged object must have moved or rotated such that the landmarks in 3D space overlap with those observed in 2D space (after adjusted for lens distortion). Interestingly, there is one industry with considerable investment in creating efficient geometrical approaches: virtual reality (or “VR”) gaming. Why? Because using VR gaming requires that the system can estimate the gamer’s 3D position at all times (with, interestingly, tiny infra-red lights implanted in the headset as landmarks). Thanks to this investment by the VR industry, studies developing and comparing the accuracy of geometrical solutions to the PnP problem are flourishing. It’s a perfect time for biologists like us to start taking a peak at them.
For our study, we chose to use to an approach called the “EPnP” that was first proposed by Lepetit and others in 2009 (https://link.springer.com/content/pdf/10.1007/s11263-008-0152-6.pdf). We chose this approach because it permits one to use >4 landmarks for positional estimation (thus reducing error) with little cost to computational time relative to traditional solutions. Other approaches have been lauded for improving accuracy (e.g. P3P with RANSAC) and we encourage others to pursue those approaches. For our study, however, we were interested in balancing accuracy and efficiency.
To execute the EPnP approach, we estimated the 2D position of up to 9 landmarks on a pigeon’s head by loading our thermographic images into ImageJ (Fig 2). Building a 3D model turned out to be much less time consuming – simply draw on morphometric measurements of domestic pigeons reported in literature. From these data, and EPnP algorthims, we were thus able to estimate both a 3D translation and 3D rotation of an imaged bird’s head, relative to a virtual model of a perpendicularly facing individual.
I’ll break them down by question.
Question (1): How well does this stress-induced change in eye region temperature reflect circulating changes in sympathetic nervous system markers (i.e. catecholamines, like adrenaline and noradrenaline)?
Answer: Our results suggest that, at least in our study species, surface temperature the bill is probably a better indicator of stress physiological state. I’ll explain why by referencing what we observed from data that did not control for head position. After stress exposure, bill temperature fell significantly by ~4°C after stress exposure (handling), while eye region temperature did not significantly change (Fig 3). Rather, temporal patterns in eye region temperature appeared remarkably similar between “stressed” and “unstressed” birds. Moreover, only stress-induced changes in bill temperature showed significant inter-individual variation, suggesting that if one wishes to build a metric of “stress-responsiveness” from changes in surface temperature, doing so at the bill is likely more effective than at the eye region.
Question (3): How robust and reliably detectable are stress-induced changes in body surface temperature?
Answer: It depends on where you look. After correcting for changes in head position in our birds, a significant effect of stress-exposure on eye region temperature emerged (Fig 4). This was not the case for stress-induced changes in bill temperature, which were detectable regardless of whether head position was accounted for or not. This point, we think, is particularly important for two reasons:
(1) don’t correct for changes in object position and you risk missing out on detecting biological processes, and
(2) surface temperatures of some body regions might be better indicators of your biological process of interest than others.
Take Home Message
To conclude, drawing biological inference from thermographic images is tricky. Many sources of error can get in the way of your ability to meaningfully do so, and a common one is changes in object position. As such, biologist should always remember to correct for object position when working with their surface temperature data – perhaps by using our method or another.
Tabh, Joshua KR, Burness, G, Wearing, OH, Tattersall, GJ, Mastromonaco, GF. 2021. Infra-red thermography as a technique to measure physiological stress in birds: body region and image angle matter. Physiological Reports, Accepted.https://doi.org/10.14814/phy2.14865
Dr. Joshua Robertson Tabh is a graduate of Trent University, co-supervised by Dr. Gary Burness and Dr. Gaby Mastromonaco. This research was made possible with the cooperation of the Toronto Zoo and by the watchful eye of Oliver Wearing. Since 2016, Joshua and Glenn have shared many conversations about avian physiology, imaging, and coding and Glenn invited Joshua to guest author this post after all these efforts finally reached the publication stage.
Funding for this research was provided by the Toronto Zoo Foundation, an NSERC Collaborative Research and Training Experience Program (Grant #: CREATE 481954-2016), a Howard P. Whidden grant to OHW, and an NSERC Discovery Grant to GJT (Grant # RGPIN-2014-05814).
We’re happy to share news that our paper (shareable link) was recently accepted in Methods in Ecology and Evolution! As for the nitty gritty secrets of the study, that headline was to get your attention! I’ll point out some highlights here, with some visuals to summarize the main results.
The study resulted from a visiting PhD student (Núria Montmany-Playà, @NuriaBeachy) to the lab in January 2020, immediately prior to the pandemic and lock-down. Sadly, some of the research we planned to do was not possible due to travel restrictions and embassies immediately calling back their citizens. So, this represents about the only kind of research my lab is capable of doing during a lockdown.
So, what is the paper about? Technically, it’s a methods (thus the journal choice!) and resource paper (information on empirical measurements of emissivity which I often get from colleagues) with a warning to field thermographers to “check your distance“! (Actually, Faye et al 2016 already advised this, but given various interactions I have had over the past few years, some field thermographers might not be reading all the literature – so please read Faye et al’s paper cited at the bottom of this blog).
It has become increasing common in some animal thermography studies to capture the maximum temperature from a specific region of the body (often the face or the eye) and use this as an estimate of body temperature (it is not a good estimate!) or as a proxy for vasoreactivity related to stress (which it is a better indicator of, although depends on species and body part). This effect of distance was earlier studied by Faye et al (2016), although we focus on the challenges in animal thermography in the field where animals control how close you can get! Watch the video below for the effect in action, paying attention to the maximum temperature in the eye region and how it changes as this Trumpeter swan walks closer (starting from ~15 m) and closer (~2 m) to the camera:
Here are the results plotted over time as an animated plot:
As the bird gets closer, the maximum temperature of the same animal rises from a low of ~25C to reach closer to a more realistic value of ~33C. This is an enormous range and cannot reasonably be based on vasoreactivity, when the physics of thermal optics can explain this. Read the paper for the explanation behind how spot size and distance effects interact to produce fairly large errors in thermography. Controlling for distance and/or being aware of its influences is important to any field application of thermography. I doubt too many people are trying to image animals at 10 m away given the low resolution of many thermal cameras, but as prices come down and people take their devices into the field, I am sure researchers will run into these situations.
We measured the technical error using a blackbody calibration source placed at different distances from the camera, and tested under different conditions. Below, Tb refers to the blackbody temperature (i.e. true temperature) and Ta refers to prevailing ambient temperature. Delta T refers to how the thermal camera estimates the same black body temperature (where 0 refers to the closest distance measurement). We tested this with different camera/lens combinations to give insight into how the devices function even under well controlled conditions, far different from most field applications. You can see below that the error in temperature can be as poor as 6C below actual temperature at 10 m distance, and these results are for measurements of an electronic calibration source.
We also published some simple results on how the angle of incidence influences thermography measurements (see image above of the puffin). This is also well known by thermal imaging engineers and physicists, but not likely well appreciated by biologists, so we used a Leslie cube (see image below) and adhered various bits of biological materials to a copper surface, heated the surface up and painstakingly measured the temperature, allowing us to calculate the apparent emissivity. Objects at steep angles of incidence to the camera will have a lower emissivity, which means that the apparent radiation we measure is actually a lot of background reflected radiation, and thus is a source of possible error in field thermography.
So the main message of the paper is to keep good records of how far your camera is from your object of study. Correcting for this effect is complex and beyond the scope of our study, although we report that the potential error of being 10 meters away from an object can be as high as 4 to 6C, with similar errors for measuring objects at angles greater than 50 degrees incidence.
Many thanks to Núria for her hard work on this project. Without her visit, we would not have done this. And we also want to thank the two reviewers for their hard but fair questions but also for listening to our response.
Lockdown science had me borrowing plant material from my parents, Clifford and Brenda Tattersall, so their help was crucial to the final acceptance of the manuscript!
Montmany-Playà, N. and Tattersall, GJ. 2021. Spot size, distance, and emissivity errors in field applications of infrared thermography. Methods in Ecology and Evolution.https://doi.org/10.1111/2041-210X.13563