Bumblebees can tell each other apart using scent marks

beeBumblebees have the ability to use ‘smelly footprints’ to make the distinction between their own scent, the scent of a relative and the scent of a stranger. By using this ability, bees can improve their success at finding good sources of food and avoid flowers that have already been visited and mined of nutrients by recognising who has been there previously. A study conducted as part of Richard Pearce‘s PhD that shows this has been published in Scientific Reports today.

Bumblebees secrete a substance whenever they touch their feet to a surface, much like us leaving fingerprints on whatever we touch. Marks of this invisible substance can be detected by themselves and other bumblebees, and are referred to as scent marks.

We performed three separate experiments with bumblebees, where they were repeatedly exposed to rewarding and unrewarding flowers simultaneously that had footprints from different bees attached to them.

Each flower type either carried scent-marks from bumblebees of differing relatedness (either their own marks, sisters from their nest, or strangers from another nest), or were unmarked.

We discovered that bees were able to distinguish between these four different flower types, showing that not only can bees tell the marks of their own nest mates from strangers, but also that they can discriminate between the smell of their own footprints and those of their nest mate sisters.

This is the first time it has been shown that bumblebees can tell the difference between their scent and the scent of their family members. This ability could help them to remember which flowers they have visited recently.

Bumblebees are flexible learners and, as we have discovered, can detect whether or not it is they or a different bumblebee that has visited a flower recently. These impressive abilities allows them to be more clever in their search for food, which will help them to be more successful.

This work, published today in Scientific Reports, was funded by the EPSRC, through the Bristol Centre for Complexity Science. This blog posting is an edited version of the University of Bristol press release.

further reading

Pearce RF, Giuggioli L & Rands SA (2017). Bumblebees can discriminate between scent-marks deposited by conspecifics. Scientific Reports 7: 43872 | full text

Consensus trumps leadership and personality

sticklebacks‘Personality’ has been a big topic in behavioural ecology for well over a decade now, and work is still coming thick and fast showing that individual animals can show consistent sets of correlated behaviours in different situations, and that that different individuals can show different sets of these behaviours.  For example, many different species have been shown to have some individuals who are ‘bold’ risk-takers who are active in their response to stimuli, whilst other ‘shy’ individuals are less likely to take risks, and will be passive in their response.

However, when groups of individuals come together to behave in a social setting, it could be the case that these consistent personalities break down, as it may not be possible or suitable for every individual to follow their own personality-defined behaviour.  A recent paper in Science Advances from Christos Ioannou’s group (McDonald et al. 2016), that I was privileged to be involved with, demonstrates just this. The study looked at what happens when you put together groups of sticklebacks that have different personalities.

By testing the fish individually, we showed that there was consistency in how they emerged from a safe shelter and travelled through a ‘dangerous’ exposed area of water in order to reach a foraging site: some individuals were bolder than others.  However, when you put groups together with a range of bold and shy individuals, the shy individuals tended to lose their shyness and behave in a similar way to the bold individuals.  This effect is only temporary – once the fish became used to the test conditions and their groups, they reverted to their initial personality-defined behaviours.

This study suggests that personality isn’t necessarily consistent in individuals, and may well depend upon context. Being able to remain in a group is very likely to be important for sticklebacks, and it makes sense that shy individuals will mask their behaviour in order to maintain the protection of a group.  Whether this is ignoring the behaviour determined by their own personality, or rather another aspect of their personality that is defined by social context or some other aspect of state (see Dall et al. 2004 for discussion), it suggests that there is a lot more to be explored concerning how personalities are affected by groups.

further reading

Dall SRX, Houston AI & McNamara JM (2004). The behavioural ecology of personality: consistent individual differences from an adaptive perspective. Ecology Letters 7: 734-739 | abstract

McDonald ND, Rands SA, Hill F, Elder C & Ioannou CC (2016). Consensus and experience trump leadership, suppressing individual personality during social foraging. Science Advances 2: e1600892 | full text (open access) | pdf | blog posting from Christos Ioannou

normal service will (probably) be resumed shortly…

Test Card FOkay, maybe a few weeks until the next scientific post. This term (not quite finished) has been killer hard, and exhausting – twelve week teaching blocks without any gaps in them seem to be quite hard for the students, as well as us doing the teaching. Personally, I have about a week more of administration and marking, and some final year MSci project student talks and posters to look forward to, and then I should be mentally free to get back to some bits of research that I’m itching continue with.

Standard moaning aside, there is much to be happy about at the moment. Richard Pearce and David Lawson are deep into writing their dissertations, and both have papers I am very keen for the world to see at various stages of the review process. Ongoing shared students are also coming along nicely, and I hope to be able to say something good about several of them soon (and show off my Lego skills)!

I should be posting shortly about a paper I was happily involved with, looking at how personality and leadership decisions can be influenced by what the rest of your social group are doing.  If you’re curious about this, have a look at what Christos Ioannou says about it. There will also be another post thinking about something new and exciting we can do with bird feeder experiments, tying in with a paper I have coming out in Royal Society Open Science at some point in the next month…


Further reading

McDonald ND, Rands SA, Hill F, Elder C & Ioannou CC (2016). Consensus and experience trump leadership, suppressing individual personality during social foraging. Science Advances 2: e1600892 | full text (open access) | pdf

normal service will be resumed shortly…

Test Card FThat last blog post from over a year ago was a classic false start – it’s been a hard year for both personal and non-personal reasons (and the referendum decision by the British public to leave the European Union still weighs heavily – sigh), but the start of the academic year and some nice work in progress/review has spurred me back into action. There are a number of posts in progress at the moment, not least one looking at the recent paper by McDonald et al. in Science Advances that I am very pleased to have been involved with. More over the next few days…

more soon…

I’m currently on leave until mid-July, and have been taking extended paternity leave since January to look after a little person. It’s both great fun and very hard work, and I currently don’t intend to devote any time to these pages until I get back. However, there is a back-log of half-finished blog posts that need polishing, and that will hopefully start trickling out once I am back to work and have handled all the administrative tasks that wait for me. You have been warned!

Seasonal variation of ‘cresty neck’ in horses

guest blog by Sarah Giles

‘Cresty neck’ in horses is an abnormally large amount of nuchal neck crest fat, fat along the top of the neck. It can be seen as akin to abdominal adiposity in humans, this region specific adiposity can cause a range of metabolic disorders in both species. In horses, this metabolic changes have been associated with laminitis, a debilitating condition affecting the hoof which can cause debilitating and sometimes fatal lameness. Our paper, recently published in BMC Veterinary Research explores seasonal differences in neck crest adiposity in groups of domestic horses and ponies.


It is not yet known why neck crest fat specifically is more strongly associated with metabolic abnormalities, but our study has presented some unusual results which might warrant investigation. Our previous study highlighted the seasonal variation in body condition and obesity present in outdoor living domestic horses and ponies (see previous blog post). This second study was conducted on the same population of animals, yet crucially, showed the exact opposite pattern of seasonal variation!

Unusually, the prevalence of ‘cresty neck’ was highest at the end of winter. This is surprising, firstly because, quite obviously, there is less grass available at the end of winter for outdoor living animals. Then secondly, because it had previously been speculated, arguably quite rightly, that the role of fat stores is to aid survival during winter months when food is scarce. Why then, does cresty neck seem to be more prominent in outdoor living horses at the end of winter?

Supplementary feeding was recorded, and this did not explain the results observed. The paper therefore discusses several other possible explanations. Broadly this includes a physiological explanation, where cresty neck fat is physiologically different to fat stored elsewhere and due to a potentially different physiological role. Or alternatively, we consider whether these results are simply an anomaly with the cresty neck score itself. The score may be difficult to replicate under different conditions, or there may be something about winter conditions, such as a fluffier winter coat on the animals, or less fat elsewhere, which makes the neck crest seem more prominent.

Whatever the explanation, these results were certainly unexpected and are therefore very interesting! This paper was fun to write as it was explorative and allowed for a balanced, speculative and thought provoking discussion. Disproving a hypothesis in this case, was much more interesting than proving one.

Most of all this paper is a reminder that we really don’t know all of the answers with regards to obesity and metabolic pathways in horses. We truly hope that this paper inspires further research into these potentially unusual physiological mechanisms

Further reading

Giles SL, Nicol CJ, Rands SA & Harris PA (2015). Assessing the seasonal prevalence and risk factors for nuchal crest adiposity in domestic horses and ponies using the Cresty Neck Score. BMC Veterinary Research 11: 13 | full text | pdf

There’s additional coverage of this paper at horsetalk.co.nz

Metrics for dominance interactions 2: Fighting success, Clutton-Brock et al. (1979)

dominance_iconThis is the second in a series of blog entries exploring the metrics used for assessing dominance hierarchies: see the introductory post for the rationale behind doing this, with other metrics visible through the index page.

Clutton-Brock et al. (1979) were interested in giving a metric to fighting success in red deer stags, where individuals were studied over long periods of time. Studying any network system over longer periods of time is going to cause a problem, as the status of individuals may change during that period (see Rands 2014 for some discussion of this problem), and the authors of this paper were aware that a male’s dominance could change within a mating period as his energy levels flagged or he became injured. Simply counting the number of fights won and lost will not give a very accurate reflection of how an individual is placed within the herd, as his success is also going to depend upon the idntities of the individuals he beats: a male who consistently fights and wins against weak opponents is not necessarily going to be of similar quality to a male who consistently fights and wins against strong opponents. So, Clutton-Brock and colleagues designed a simple metric that takes account of the quality of opponents individuals win and lose against.

I’ll illustrate how this is calculated with by considering the fighting ability metric of two individuals (labelled black and blue) within the following group structure:

Figure 1: all winner/loser interactions recorded. As depicted in the box, the arrow denotes which individual is the winner (W) or loser (L) in a connected pair.
Figure 1: all winner/loser interactions within a group. As depicted in the box, the arrow denotes which individual is the winner W or loser L in a connected pair.

To gauge an individual animal’s fighting success, you need to calculate B, the number of other animals that the focal individual has won against, and note the identities of all the losers. For each of these marked losers, you also need to calculate the number of individuals that they in turn have beaten, and sum these to give Σb. Because we define one individual in an interacting pair as a winner, and the other a loser, this means that none of the summed interactions contributing to Σb are against the focal individual.

As well as assessing wins, you also need to calculate L, the number of other individuals that the focal loses against. These winning animals are marked and the summed number of animals that they themselves lose against is calculated, giving Σl.

Having collated these numbers, the fighting success of a focal individual (which I will refer to as DCB) is calculated as

DCB = (B + Σb + 1)/(L + Σl + 1),

where the “+1” term on both the top and bottom of the equation allows a meaningful metric to be calculated for individuals that are either never seen to win or lose.

Using the group interactions given in Figure 1, we calculate DCB for the individual coloured black using the following reasoning:

Figure 2: Calculating black's win (left panel) and loss (right panel) statistics
Figure 2: Calculating black’s win (left panel) and loss (right panel) statistics

Following Figure 2, we see that B = 8, Σb = 2 + 2 + 2 + 1 + 1 + 0 + 0 + 0 = 8, L = 3, and Σl = 2 + 2 + 0 = 4. So, DCB = (8 + 8 + 1)/(3 + 4 + 1) = 2.125 for the black individual. Similarly, using the reasoning given in Figure 3, DCB =1.167 for the blue individual.

Figure 3: Calculating blue's win (left panel) and loss (right panel) statistics
Figure 3: Calculating blue’s win (left panel) and loss (right panel) statistics

A larger value of DCB will notify a greater fighting ability, and the maximum size of the statistic within an observed group is going to depend on both the size of the group and the maximum number of other animals that each individual in the group interacts with.   In their original paper, Clutton-Brock and his colleagues found DCB for red deer ranged between 0 and a little over 3.

This is a simple statistic to compute, but I would caution that it should really only be used for comparing individuals within a group, given that it is dependent upon both group size and number of interactions recorded. The metric is also dependent upon observed relationships being fixed: an individual that wins an interaction will always win future interactions with the same opponent. This suggests that caution should be used if this metric were to be transferred to observed interactions where the dynamic between a dyad could change over time.

Further reading

Clutton-Brock TH, Albon SD, Gibson RM & Guinness FE (1979). The logical stag: adaptive aspects of fighting in red deer (Cervus elaphus L.). Animal Behaviour 27: 211-225 | pdf

Rands SA (2014). We must consider dynamic changes in behavior in social networks, and conduct manipulations: comment on Pinter-Wollman et al. Behavioral Ecology 25: 259-260 | full text | pdf

Technical Note: The network diagrams were drawn on a Mac with Dia Diagram Editor (open source freeware), and coerced into nice smooth images with GIMP (GNU Image Manipulation Program: open source freeware).

“I don’t think I’ve ever seen a fat horse”

guest blog by Sarah Giles

photo copyright Sarah Giles 2014
photo © Sarah Giles 2014

The usual response to the mention of equine obesity is “I don’t think I’ve ever seen a fat horse”. Followed by a long-winded explanation by me of how horses don’t necessarily ‘look’ fat in the same way as we are used to recognizing fat humans. But they are. Our new study, published yesterday in PeerJ, showed that the prevalence of obesity in outdoor living horses and ponies was a staggering 27% at the end of winter, when we would expect outdoor living animals to be at their thinnest (!) and rising to 35% during the summer months, presumably due to all that lush, green, UK pasture.

So nearly a third of UK leisure horses and ponies could be clinically obese, and other previous studies have had similar findings. That’s a very similar level of obesity to that seen in the human population. In the same way as humans, horses may experience negative health consequences of obesity,  including metabolic conditions such as insulin resistance, but also a severe and debilitating hoof condition called laminitis which can render them chronically and even fatally lame.

The risk factors for obesity in any species are fairly straightforward, an energetic intake/exercise imbalance. Eat too much, do too little. But what makes some individuals more susceptible than others? Why do some horses seem to become obese when others do not under the same, outdoor living conditions? The study considered a wide range of food, exercise and management related factors, but by far the biggest risk factor was breed. Different horse breeds appear to have very different levels of obesity susceptibility. Our native UK breeds, including Welsh breeds, such as mountain ponies and cobs, as well as Dartmoor, Exmoor and New Forest ponies all appear to be at a much higher risk than  for example the Arabian type lightweight breeds.

It might be that native UK breeds, which have evolved to live on mountains and moorland, are just very efficient at storing fat reserves! They are designed to pile on the pounds during the summer months when food is plentiful, and use these extra stores to survive cold, harsh winters. The problem in domestic animals (which have changed very little physiologically from their wild counterparts) is that this harshness never really occurs in a domesticated environment and horses do not lose their fat reserves during the winter months. Instead they become incrementally fatter and fatter, year-on-year. The study showed that once horses and ponies become obese, natural seasonal fluctuation in body condition reduces and almost disappears. As a result, these animals remain obese, year-round.

The fact that supplementary food and exercise played such a small role in explaining obesity susceptibility in predominantly outdoor living animals is key here. There is clearly a lot of work to be done in investigating risk factors for obesity in these outdoor living animals. Could social and behavioural factors play a role? This is of real interest to us: keep your eyes on the blog for more details.

Further reading

Giles SL, Rands SA, Nicol CJ & Harris PA (2014). Obesity prevalence and associated risk factors in outdoor living domestic horses and ponies. PeerJ 2: e299 | full text | pdf

Pollinator movement through fragmented landscapes

beeMany pollinators live in complex and changeable environments. The location of of their food sources changes with time of year (as plants flower), time of day (as flowers open and close or nectar flows) and physical location (for although plants may not move very much during a flowering season, the places that flowers may be found may differ dramatically with time). Natural selection has shaped the behaviours of pollinators so that they have a suite of behaviours that allow them to exploit their environment: although it is unlikely that they know exactly when and where food will be available, they are able to couple clues from the environment with a repertoire of behaviours that will allow them to find food.

Agriculture and other human-generated change is altering the enviroment that pollinators live within, and it is very likely that the rules they are following are not ideal for the changed landscape. Because pollinators are essential for crop production, agricultural policies often dictate that there is some concession to the pollinators. This could be through leaving set-aside ‘wild’ land for nests and wild flowers, or by adding corridors of uncropped land or hedgerows where beneficial species can travel between environments. However, the pollinators will still be following their evolved rule sets, which means that we need to consider whether our concessions to them are of any use. This is something that is difficult to measure, and we need to use a wide range of techniques to ask whether particular manipulations are of benefit to some or many helpful species.

As a behavioural ecologist, I’m interested in how the behavioural decisions made by individual animals allow them to interact with the environment. For most animals, the environment that they live in is highly complex and frequently unpredictable, and it is often a challenge for us to understand how a particular decision gives the animal an advantage over an alternative behaviour. As well as conducting experiments and making observations of behaviour, we can also use theoretical techniques for exploring how simple behaviours could be the best solutions for dealing with complex environments. Simulation techniques are particularly useful for understanding how particular sets of decision allow an animal to cope with changes at the landscape level (bringing together two very different disciplines: landscape ecology and behavioural ecology).

In a paper that has just been published in PeerJ (Rands 2014), I describe a series of models describe a framework for considering how landscape alterations affect the foraging success of a pollinator nesting within the environment. These models build on earlier ideas presented by Rands and Whitney (2010, 2012, discussed in an earlier blog entry), where we simulated landscapes with simple geometries and allowed pollinators to forage within them. In the new PeerJ paper, I describe how hedgerow removal and set-aside field creation may affect the movement of pollinators. The models demonstrate that decreasing either landscape connectivity (be removing hedges) or wild land availability (through having lots of fields of unusable crops) affect how often pollinators have to switch between different environmental types. This may be important for how they find and collect food: for example, swapping between habitats may lead to a temporary reduction in nectar uptake if the pollinator has to work out how to collect it from a newly-encountered shape of flower.

The models are a first step, and are presented as a means of discussing how we can manipulate the environment in a reproducible way within a model. What needs to be done now is to identify a suitable set of behavioural rules to follow (for those presented in the models are basic, and are very likely to be improved upon!). Ongoing work should be able to demonstrate whether particular environmental manipulations are of value to some of our threatened pollinator species.

Further reading

Rands SA (2014). Landscape fragmentation and pollinator movement within agricultural environments: a modelling framework for exploring foraging and movement ecology. PeerJ 2: e269 | full text | pdf

Rands SA & Whitney HM (2010). Effects of pollinator density-dependent preferences on field margin pollination in the midst of agricultural monocultures: a modelling approach. Ecological Modelling 221: 1310-1316 | abstract | pdf (postprint version)

Rands SA & Whitney HM (2011). Field margins, foraging distances and their impacts on nesting pollinator success. PLoS One 6: e25971 | full text | pdf

Hacking together a cheap but effective infra-red camera

eyesMany animals annoyingly do things differently in the dark than in the light. This causes problems for both field- and lab-based behavioural biologists, as few are blessed with the power of night vision. However, there are ways around this problem. One old-tech method is to observe them using red light, making the assumption that the beastie you’re observing can’t see at these longer wavelengths, but you can. However, a number of studies suggest that this a flawed assumption (e.g. 1, 2). So, how do you see in the dark?

Using electromagnetic radiation that has wavelengths outside the visual spectrum of the animals is one solution. Night vision and other special cameras are able to detect infrared radiation, which has a longer wavelength than visible light. This IR may be radiated as thermal energy by the animal (this is what heat-detecting cameras register), or could be being reflected by the animals and their environment (which is what many night-vision and CCTV cameras do, using an additional source of IR such as a set of IR-emitting LEDs, like the ones found in TV remote controls).

We and most other animals are unable to see these longer IR wavelengths, so these cameras are essentially detecting ‘invisible’ information that is unavailable to us. Any extra information about the environment that could improve your chances of finding food or avoiding predators is going to be useful if it can be detected, and there are of course some animals that can detect IR3: vampire bats, several families of snake and a range of butterflies, beetles and bugs have well-researched abilities to detect IR. Similarly, some prey species have evolved counteradaptations to confuse species that are able to detect IR, as can be seen4 in the anti-pit viper tail-flagging displays of ground squirrels. However, given that many animals are insensitive to IR wavelengths (we assume – it doesn’t hurt to check and double-check if you’re working with a particular species!), using an IR camera is a good first step for observing their behaviour unobtrusively, and many bits of kit are commercially available that allow you to observe nesting behaviour or remotely capture images of animals in the wild (I have no intention of recommending any of the products out there, but you should be able to find hundreds of commercial types available by running a web search for ‘camera trap’, ‘nest cam’ or ‘trail camera’).

Making your own IR-sensitive webcam

However, commercially available kit ranges in price and can be expensive. If you’re piloting a bit of work and want to test things out before you write the big grant, buying one or multiple specially designed cameras may be that little bit too expensive. It is however extremely easy to build your own IR camera using a little bit of initiative. The sensors on webcams and other digital cameras are sensitive to IR, but usually have some sort of filter between the lens and the sensor that blocks out unwanted non-visible electromagentic radiation: it is easy to remove this in some cameras, but can involve some delicate and potentially destructive scraping in others. A quick hunt online will give you access to loads of text and video tutorials on how to do this (here’s a good one from the Naked Scientists). Once removed, there’s nothing else you need to do – the sensors will be registering IR, and probably displaying it as extra purplish light if you’re able to see the camera’s output.

I’ve played with a few different cheap webcams available, and acknowledge that they do differ in both quality and ease of modification. Because sensor types and the way light is filtered onto them differs from camera to camera, I’d recommend shopping around for a camera that has a bit of depth to it as it is likely to have a separate filter that can be easily chipped out, rather than one that has to be scraped directly off the sensor (which will probably damage teh sensor). Removing the filter will also affect the depth at which incoming visible and IR light is focussed on the sensor, and I’d suggest hunting for a camera where the lens is focussed manually rather than by having to use a software interface.

I ended up settling for the Tecknet® 1080P HD Webcam, which was very cheap, is very easy to take apart and alter, has a decent pixel size and image quality, and is easy and stable to focus. These webcams are extremely easy to modify. After removing the front, the focussing lens can be unscrewed (below, A), revealing the filter (the shiny square bit in B) that blocks IR wavelengths from reaching the sensor. There’s probably a neater way of doing this, but it is easy to carefully chip this out with the end of a screw-driver (CBlue Peter warning: make sure you’re wearing eye-protection and using appropriate safety equipment while you’re doing this, and get an adult to help you). Then, simply reassemble and plug into your favourite operating system. I’m fairly certain this voids the warranty though…

removing the IR filter

Hardware for running your IR camera

It’s not just the camera that’s important if you’re trying to build cheap functional kit – you also need something to run it from. You could technically run it on anything that has the correct webcam driver installed (hint: if you’re a Mac user and can’t find the right driver, try running the camera from within Skype, which may well be able to run it). However, since we’re aiming for budget kit here, I’ll give a quick description of a system I’ve put together that runs from a Raspberry Pi – a tiny, inexpensive (GB£20, €26, US$25) computer that runs open-source LINUX-based software*, which means that the system is incredibly portable and can be altered to run off a battery in the field (useful if you’re building a cheap and effective camera traps for IR and/or visible wavelengths, like those deployed by ZSL for monitoring black rhino). Furthermore, because you’re building it yourself, it doesn’t require system administrators to install things for you (a major time-lagging factor for many researchers working in larger institutions!).

I’m assuming here that you’ve managed to get your Raspberry Pi up and running, formatting your SD card with something like Raspbian, and are happy with using a command-line tool (if you’re running a graphic-based interface, you can get at this using one of the ‘Terminal’ applications such as LXTerminal). I’m also assuming that you have managed to connect your system to the internet, as you’ll need to download some software. If you’re intending to set this up using a monitor rather than remotely, and want to be able to remove the monitor at some point during the camera’s use, it’s worthwhile getting the Model B Raspberry Pi with two USB ports, which means that you don’t need to detach the camera at any point.

Software for running your IR camera

Some bits of easily obtainable software are useful if you’re running this off a Raspberry Pi. Firstly, if you want to have a direct feed from the camera which is visible on a monitor in front of you, try something like Camorama. Assuming you have an internet connection, you can install this on your computer by entering

sudo apt-get install camorama

and answering ‘yes’ at appropriate moments. To run it, simply type ‘camorama’ into the terminal: as well as a direct feed and a point-and-click interface that allows you to play with the visual balances, you can take jpeg images too. If you want to record mpeg-format videos, you could try a program such as LUVCView instead, which you install and run in a similar manner (by replacing the word ‘camorama’ with ‘luvcview’ in the commands described).

If instead you want to either take still photos at regular intervals, or use your camera as a motion-triggered device (useful for camera traps), I recommend starting off with Motion, which you need to initially install using

sudo apt-get install motion

To run this at the default setting (where the camera is triggered by motion), just type ‘motion’ into the terminal once you have installed the software. As this package is run from the command line, you can create a text file that details exactly how you want the camera to be configured. For example, if you want to run the camera so that it doesn’t react to motion, but instead captures an image every quarter of a second, you can set up a configuration file using a word processor such as nano:

sudo nano motion.config

and typing the following:

framerate 4
output_all on

which you then save a the file ‘motion.config’ (using ctrl+x). The first command above sets the maximum number of frames per second that the device captures, and the second tells the system to turn off the motion-detection capability of the software, and instead take continuous images.

Having created the motion.config file, you then run motion by entering

motion -c motion.config

It may take a few seconds to start up, and you should then get a display whenever a file is written. To stop the program when it’s running, open another terminal window and type

killall motion

Motion is relatively simple to use, and has a good list of configurations that you can play with: simply reopen the config file you’ve created using the same commands, and alter the text. There’s the option of creating a timelapse video within this as well, but you are limited to only being able to use images that are a second or more apart.

A final word, and some caveats

A note for the coding purists out there: the description given here is written to enable non-coders to put together something that works with a minimum of poking. I am fully aware that there are other more elegant ways of doing this, and many other forms of software that can be used, but this should give a first step to enable a stressed lab/field scientist to have something functioning quickly. I am not willing to give any advice on this or similar applications, and accept no responsibility or liability if you follow these instructions and end up damaging your equipment or yourselves in any way: you follow them entirely at your own risk.

Having made your IR camera and worked out how to run the software behind it, you can then deploy it – you will need an IR light source too, and there are many available out there designed for CCTV systems (make sure you’re using them safely though). It looks like things may soon be made even easier by with the introduction of a super-cheap (US$25) IR camera specifically designed for the Raspberry Pi, which will hopefully be well supported within the Raspberry Pi user community. I’ve currently got some undergraduate project students trying out this kit in the lab, on a neat system that may be very nice for observing social and group behaviour – some more on this soon!

Further reading

1. Gibson G (1995). A behavioural test of the sensitivity of a nocturnal mosquito, Anopheles gambiae, to dim white, red and infra-red light. Physiological Ecology 20: 224-228. doi:10.1111/j.1365-3032.1995.tb00005.x

2. Heise BA (1992). Sensitivity of mayfly nymphs to red light: implications for behavioural ecology. Freshwater Biology 28: 331-336. doi:10.1111/j.1365-2427.1992.tb00591.x

3. Campbell AL, Naik RR, Sowards L & Stone MO (2002). Biological infrared imaging and sensing. Micron 33: 211-225. doi:10.1016/S0968-4328(01)00010-5

4. Rundus AS, Owings DH, Joshi SS, Chinn E & Giannini N (2007). Ground squirrels use an infrared signal to deter rattlesnake predation. Proceedings of the National Academy of Sciences of the USA 104: 14372-14376. doi:10.1073/pnas.0702599104