Category Archives: Behavioural ecology

Can we just agree on a name?

An image of a squirrel with personality

A squirrel with personality, from jpaxonreyes (used under a CC license). Because let's face it, this post needs a picture.

Looking at the email alerts I get for new journal issues, I came across a new paper by Sih et al. in Ecology Letters [1], looking at the “ecological implications of behavioural syndromes”.  And I suppose that I could talk about the content of the paper, but what I’d rather do instead is go off on a short rant about research on this topic, as is my right as a blog writer.  What’s got a bee in my bonnet (and why am I suddenly 90 years old)?  It’s the name, “behavioural syndromes”.  It drives me mad.  I’ve seen papers refer to the topic by:

  • “Animal personality”
  • “Behavioural syndromes”
  • “Coping styles”
  • “Animal temperament”
  • “Interindividual variation” – not an SEO friendly description, to be sure.

There seems to be a political aspect to this too, but I’m not 100% clear on it.  Some themes are clear, though.  My feeling is that Sih seems to be pretty stuck on “behavioural syndromes”, while others like Denis Réale (whom I know from my Ph.D. at UQÀM) and Neils Dingemanse seem to be throwing spaghetti at the wall; after trying to introduce “animal temperament” as a thing – which, as far as I can see didn’t take hold – they had the (actually quite inspired) idea of doing an end-run around the whole thing by combining personality with plasticity and coining the new phrase “behavioural reaction norms” [3].  Only time will tell if that one takes off.

Lest you think that it’s just a name problem, it seems that confusion in the names is a symptom of deeper confusion over what they’re studying and how to study it.  Hanging around at a couple of the discussions at the last ISBE made it clear that people working in this field aren’t agreeing on the name, the definition, or the methodology (statistical or experimental).  Some of this is cause for excitement, of course:  when you’re this confused, it’s probably a sign that you’re on to something good.  And don’t think that I’m writing the area off;  there’s been a lot of exciting work in personalities over the last decade or so.  Hell, I’m trying to get a paper published on the topic myself right now.  Yet, I can’t help feeling that work in this area is going to be a little bit hamstrung until it converges on clear values for each of these things.

And honestly, I just feel sorry for the next poor sod who wants to do a literature review or meta-analysis.  So, can we just agree on a name and call it a day?


[1]. Andrew Sih, Julien Cote, Mara Evans, Sean Fogarty, and Jonathan Pruitt. Ecological implications of behavioural syndromes. Ecology Letters, 15(3):278–289, 2012.

[2]. Denis Réale, Simon M. Reader, Daniel Sol, Peter T. McDougall, and Niels J. Dingemanse. Integrating animal temperament within ecology and evolution. Biological Reviews, 82(2):291–318, 2007.

[3]. Niels J. Dingemanse, Anahita J. N. Kazem, Denis Réale, and Jonathan Wright. Behavioural reaction norms: animal personality meets individual plasticity. TRENDS in Ecology and Evolution, 25(2): 81–89, 2009. 

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Parents and personality in the animal world.

Animal personality is a huge topic in behavioural ecology right now, and it seems like you can’t shake a stick in the literature without hitting another paper on the subject.  You may have heard of the term before, but if you’re asking “what is animal personality?”, I’m planning to write on the topic more extensively soon and so I ask that you bear with me and keep an eye out for that.  For now, we can go with a sensible version that defines animal personality as “consistent individual differences in behaviour, in time and/or across contexts, for both human and non-human animals” (Dall, Houston, & McNamara 2004).  In non-scientist speak, this means (broadly) that animals show personality traits in the same way we think of when we talk about humans;  when we say “he’s an aggressive person”, we mean that no matter what context you find him in, the person we are talking about expresses aggression. In a boxing ring, aggression is appropriate, but while it may not be so appropriate in the middle of a grocery store he expresses it anyways.   The study of personality traits in animals, where animals who are aggressive or exploratory or shy in one context tend to be so in others, has exploded in recent years and the signal can sometimes get lost in the noise.  To that end, I want to highlight a new paper in the Advance Access section of the journal Behavioral Ecology by Adam Reddon, entitled “Parental effects on animal personality”.

Rat image by ressaure; used under a CC license.

(Full disclosure:  Adam is a friend from my Master’s lab, and he’s scary smart.  He’s currently doing his Ph.D. at McMaster with Sigal Balshine and publishing papers at a rate that most people can only envy.  If you’re looking for young behavioural ecologists – or scientists in general – to watch, he should most certainly be on your list).

The point of this paper, an invited forum contribution, is to link the large literature on parental effects and animal personality.  Parental effects (though most work has been on maternal effects) cover “the ways parents can shape their offspring’s phenotypes over and above genetic inheritance”, as Adam puts it.  These effects can occur in many different ways, which Adam does a nice job of reviewing;  examples include nest site selection, the amount of food provided,  hormone transfer by birds into their eggs (which can, among other things, manipulate how fast the offspring grows), social interactions, and providing opportunities for social learning.  One great example that he provides is of Norway rats.  Rat mothers will lick and groom their pups after they are born, and the amount of licking and grooming that the pups experience in the first week will have big effects on how well the pups respond to stress both physiologically and behaviourally.  Pups who were interacted with less tend to be “shyer, less exploratory, less social, less aggressive, and less dominant” throughout their lifespan (p.2). This is clearly a parental effect, because pups who were cross-fostered (adopted) to other mothers had stress reactions that came from the licking and grooming of their adoptive mother and did not correlate with their genetic mother. Paternal effects are also quite widespread, having been seen across taxa, including mammals, birds, lizards, and even waterfleas (Daphnia cucullata) and radishes.

Adam’s contribution here is to draw a straight line between the two literatures by connecting developmental processes to animal personality, treating personality as an outcome instead of the starting point.  As he states (p. 2-3):  “… the parents of a developing organism are in a unique position to guide its development and alter the offspring’s personality to better match the environment it will face”.   Parents have acquired information about the environment that may be useful to the child, and if they can translate that into paternal effects that change the offspring’s personality in a way that takes advantage of that information, they may enhance the offspring’s fitness (and by extension, their own chance of seeing grand-offspring).  A speculative example might go something like this:  parents experience a poor environment because they can’t find food, and this lack of food leads them to manipulate their offspring into having a more exploratory personality so that the offspring will have a greater chance of escaping the poor conditions of the immediate area to find food.  This would be a risky strategy, but the idea of being risk-prone in poor environments has a long history in behavioural ecology (especially in foraging, e.g. Stephens 1981).

The upside of this paper is that the connection between them is obvious and powerful, at least in hindsight .  As Thomas Huxley was said to have exclaimed upon learning of Darwin’s idea of natural selection, “how extremely stupid not to have thought of that”.  The link to paternal effects gives researchers working on personality one potential explanation for the variation they see and a paradigm to test experimentally, and will hopefully energize both literatures.  I was also under the impression from my readings that fitness differences in offspring phenotypes arising from paternal effects weren’t well explored (I’m open to correction on this!), so perhaps linking maternal effects to personality variation will provide more data on how these effects affect selection over generations. The only potential downside I can see is that personality research, so far, has been characterised by some confusion over terminology and methodology (which I will touch on in a later post);  it might take researchers in this area some time to sort out the best way to combine the two approaches fruitfully.  On the other hand, the most exciting moments in science generally emerge out of areas of confusion and doubt, so I hold out hope that exploring the effects of parental decisions on offspring personality will lead to great advances in our understanding of animal behaviour.


Adam Reddon. Parental effects on animal personality (in press).  Behavioral Ecology.

Sasha R. X. Dall, Alasdair I. Houston, and John M. McNamara. The behavioural ecology of personality: consistent individual differences from an adaptive perspective. Ecology Letters, 7:734–739, 2004.

Anurag A. Agrawal, Christian Laforsch, and Ralph Tollrian. Transgenerational induction of defences in animals and plants. Nature, 401:60-63, 1999.

David W. Stephens. The logic of risk sensitive foraging preferences. Animal Behaviour, 29 (2):628–629, 1981.

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What is an animal’s “choice”?

Image by loryresearchgroup

In behavioural ecology, we face a number of limitations in trying to ferret out the relationship between behaviour and evolutionary forces.  These range from the philosophical and theoretical (e.g. what makes a behaviour adaptive or an adaptation?) to the mundane and methodological (is that experimental set up really measuring aggressive behaviour?), and solving these problems is one of the most pressing tasks facing a behavioural ecologist attempting to make useful statements about a behaviour’s evolution.  However, while some of these issues are recurrent and obvious, others are more subtle and can sometimes slip under the radar.  One such problem is the topic of a recent paper by Véronique Martel and Guy Bovin, published recently in the Journal of Insect Behaviour and entitled “Do choice tests really test choice?”  (DOI: 10.1007/s10905-011-9257-9).

The thrust of their argument is that there is a difference between “apparent choice”, and “true choice”, which is driven largely by the fact that we can’t ask animals what they would have done under different circumstances.  As Martel and Bovin point out, animals may make one choice when presented with a particular set of stimuli, or resources as they call it (which may mimic natural conditions!), but express a different preference when presented with a larger set of resources, or when the conditions of the choice are changed.  They distinguish three characteristics of a true choice, only one of which is met by an apparent choice:

  1. The choice must be non-random, i.e. that individuals must choose one resource more often than the others;  testing only this criteria means that researchers are measuring apparent choice, while this is a necessary but not sufficient criteria for true choice.  (I would add to this that the choice probability should be fairly stable if the animal is made to choose under exactly the same conditions).
  2. The choice should be the same even in the “absence of a differential response by the resource” (p. 332). The authors state this to avoid situations in which the resource (e.g. a potential mate) is manipulating the choice of the focal animal, a problem which reminds me very much of the literature on animal signalling.
  3. It should be demonstrated that every resource is perceived, to avoid issues of sensory bias and the like.  It strikes me that this criterion will be hard to meet;  for example, if while testing mate choice the researcher tries to demonstrate a lack of bias by showing responses by the focal individual to each of the potential mates in isolation, how does that prove that one or more of the potential mates aren’t being ignored when the focal individual is given the choice between all of them?
As the authors state, meeting criterion 1 is sufficient for an apparent choice, but 2 and 3 are required for a true choice.  They spend the bulk of the rest of the paper giving examples of both apparent and true choice and elaborating the differences between the two.  It should be noted that they are not claiming that one type of methodology is “better” than the other;  in fact, they take pains to point out the pros and cons of both.  Here’s an example:

The importance of distinguishing between apparent and true choices depends on the objective of a study. If the objective is to establish which resources will be exploited under natural conditions, then the apparent choice is appropriate. If the experimenter wants to know which female will be mated by a male in a natural situation, then the results of this test (the apparent choice) will provide the answer. However, if the objective of the experiment is to establish the mechanisms of this choice, then it becomes important to look more closely at the results. If a male does not perceive a mated female as a resource because she does not produce sex pheromone, the male is thus inseminating virgin females as they are the only resource perceived. In this case, an apparent choice (the virgin female) is expressed, but this choice is the result of the non-perception of the mated female, which prevents this apparent choice from being a true choice. Measuring an apparent rather than a true choice does not remove the relevance of the test, but only modifies its interpretation. Consequently, it is important for the experimenter to state a clear question before identifying the adequate experimental setup to use.

I think that it’s important to mention here that the ideas expressed in this paper aren’t terribly groundbreaking;  a number of people ranging from economics to psychology to behavioural ecology have, at one time or another, made largely the same argument or a variation thereof (one example of a related problem is raised by a really smart guy, Jeffrey Stevens, in this book chapter here).  In fact, I’m a co-author on a paper currently in press at Behavioural Ecology talking about this issue from the opposite direction, wherein we argue that the mechanisms that underlie behaviour may be constrained and that these constraints need to be taken into account when assessing the evolution of behavioural outcomes[1].  I even made an argument very much like the one in this paper during my Ph.D. synthesis exam!

Having said that, I like the paper for its laser-like focus on raising awareness about a very specific part of animal behaviour and cognition that can seriously undercut the conclusions drawn from experimental or field work if the appropriate test isn’t matched to the hypothesis the researcher wishes to explore.  I suspect that their definition of apparent and true choices is incomplete and leaves out issues that will be hashed out in future papers, but if the journey of a thousand steps has to start somewhere, it’s not a terrible first stride.
[1]. I’ll write more about this here when the paper is published.

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Got questions about inclusive fitness?

Over at his blog, Andrew Gelman briefly mentions the recent profile of E. O. Wilson in the Atlantic, and along the way mentions the dustup over inclusive fitness that I may have mentioned here before  (did I? It’s hard to remember).   At the end, he makes a throw-away comment which drove me nuts:

The article also discusses Wilson’s recent crusade against selfish-gene-style simplifications of human and animal nature. I’m with Wilson 100% on this one. “Two brothers or eight cousins” is a cute line but it doesn’t seem to come close to describing how species or societies work, and it’s always seemed a bit silly to me when people try to loop everything back to a selfish-gene story.

I’ve been trying to think of a similarly aggravating comment to make about statistics in return;  maybe “lies, damned lies, and statistics”?  “You can prove anything with statistics”?  “Bayesian statistics suck because I don’t understand where priors come from?”  It bugged me enough that I left this comment:

It doesn’t seem like you know much about inclusive fitness, a theory has been massively successful in evolutionary biology. Despite the odd and unsupported comments made by Nowak et al., it stands firm as a well-supported and useful body of theory. Here’s a link to the letter published in response to Nowak et al.’s original article, signed by 137 authors including most of the field’s brightest minds:

The appeal to authority doesn’t mean that they’re right, of course, but extraordinary claims require extraordinary evidence; Nowak et al. have done nothing but ignore the entire published literature on inclusive fitness spanning decades and comprised of hundreds if not thousands of studies, while proposing a mathematical model that adds nothing to our understanding beyond what current theory already provides.

I respect your work on statistics, have always enjoyed reading your blog, and your book (BDA) is sitting on my shelf right now, but your offhand comment above is uninformed and very aggravating; I’d like to deal with that aggravation by offering to assist you in understanding one of the most powerful explanatory mechanisms in evolutionary biology. The letter above provides a succinct summary of the evidence that Nowak et al. ignore, but it might be a bit much for a non-technical audience; I haven’t published directly in this field, but I do work in evolutionary biology and I should be able to answer any specific questions you may have if you would like to pose them. If I can’t answer them myself, I will find people who can.

I’m not going to go into a full blown recapitulation of inclusive fitness theory and then defend it, because I’d have to write several inconveniently long books to do so.  But since I made the offer over there, I’ll make it here too for any interested readers:  if you have questions burning you up about this whole “inclusive fitness” thing, ask them here in the comments and I will do my best to answer them for you.  And if I don’t know what the answer is, I’ll find it.  No question is too small, though I make no promises on how long or short my answers will be!

I’ll leave off with a quotation from a fantastic book by Andrew Bourke that I’m reading right now, Principles of Social Evolution:

Like any large and active field of investigation, the theoretical study of social evolution is not free from disagreements and unresolved issues (e.g. Taylor and Nowak, 2007; West et al. 2007a).  Paradoxically, while the potential richness of inclusive fitness theory as a general theory of social evolution is still underappreciated, the theory is sometimes perceived as an entrenched orthodoxy. A tendency therefore exists for iconoclastically-minded theoreticians to derive models of cooperation in novel ways and then announce them to be fundamental additions to existing theory (e.g. Killingback et al. 2006; Nowak 2006; Ohtsuki et al. 2006; Traulsen and Nowak 2006).  It is healthy for orthodoxies to be continually challenged by new theories and new data.  However, to date, these models have fallen short of true novelty, as other authors have shown that their results are capable of being derived from inclusive fitness theory (e.g. Grafen 2007a, 2007b; Lehmann et al. 2007a, 2007b; West et al. 2007a).  Indeed, inclusive fitness theory has a long history of successfully assimilating apparent challenges and alternatives (Grafen 1974; Queller 1992; Lehmann and Keller 2006a).  This is not surprising when one considers its deep foundations in the theory of natural selection.  Although it is premature to declare a consensus, a substantial body of opinion therefore holds that claims of fundamental extensions to inclusive fitness theory will have to be radically innovative, as well as robust, to be accepted as such (e.g. Lehmann and Keller 2006a; West et al. 2007a).  For all these reasons, Hamilton’s (1964) inclusive fitness theory will underpin the conceptual reasoning employed throughout this book (pp. 22-23).


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Death in the nest: trade-offs rule the day.

Underlying many research programs in biology is the meta question: why is there more than one type of X?  (In continuous form, why is there variation in X?)  This question recurs in many areas of animal behaviour, and indeed in the entirety of the study of evolution itself.  Some examples include:

  • Why do animals show variation in “personality” – why are some consistently more aggressive, more exploratory, bolder, etc.
  • Why are there more than one type of male that females select between?  Why are some “attractive” and others “unattractive” – why aren’t they all attractive? (Sexual selection).
  • If aggressive signals like roaring can make other animals give up a resource or back down from a fight, why don’t all animals use the aggressive signal?  Why is there variation in signal type when all animals should use the same signal, which would then lose all meaning and be ignored?
  • Why do some animals invest heavily in each offspring while others produce as many as they can and invest very little in each?
The general response to most of these questions hinges on the idea of a trade-off.  In its most basic form, a trade-off involves giving up one thing to get (or avoid) another.  In particular, animal behaviour often hinges on cost-benefit tradeoffs.  It is desirable to have some trait or perform some behaviour, but doing so may come with a cost if we have too much of the trait or perform the behaviour too often or at all.   Examples of this litter the pages of any textbook in the biological sciences, from molecular biology up to zoology and ecology;  in particular, we can begin to address the questions I listed above by appealing to trade-offs:
  • Some personality types, like aggressive or exploratory, can confer benefits – such as always winning fights or being the first to find food – but also come with costs – such as the injuries from always fighting or the cost of being eaten while you try to be the first to eat.  Some individuals will be willing to make this trade-off, others will not.
  • The answer to this question has filled entire bookshelves, but here’s one tiny example:  in 1975, Amotz Zahavi published a landmark paper proposing that attractive males are “handicapped”;  they willingly trade off the cost of the handicap for the increased number of matings of come with it.  Zahavi’s “handicap principle”  suggested that this was a reliable indicator of quality to females because only some males would have the required quality (be strong enough, fast enough, etc) to bear the cost of the handicap in order to reap the benefit.
  • One of the most well-known answers to this question began the field known as evolutionary game theory;  at the end of the 1970s, the tragic figure of George Price and the eminent John Maynard Smith answered the question by showing mathematically how frequency-dependence could lead to a trade-off between Hawks, who are aggressive, and Doves, who back down at the first sign of trouble;  when Hawks are extremely common, their aggression leads them into costly fights against each other, which reduces the benefit of aggressiveness and makes Dove-ish behaviour more attractive.  But when Doves are common, Hawks get immense benefit with no cost by bullying Doves around.  (There’s actually significant overlap between this point and the previous, but that’s a topic for another blog post!)
  • An entire branch of evolutionary biology, life history theory, deals with questions like this:  in the face of limited resources, how do individuals make choices about the timing and sequence of events in their life to maximize their fitness?

This general pattern underlies the story behind a neat new advance-access paper from the groups of Alex Kacelnik and Juan Reboreda that manages to give away the good stuff in the title:

Ros Gloag, Diego T. Tuero, Vanina D. Fiorini, Juan C. Reboreda, and Alex Kacelnik. The economics of nestmate killing in avian brood parasites: a provisions trade-off. Behavioral Ecology, 2011.

Here, the question of types and the answer of trade-offs arises in the context of brood parasitism.  Brood parasites are organisms – birds, fish, insects – that relieve themselves of the responsibility of parenthood by tricking other organisms into doing it for them.  In birds, this usually takes the form of brood parasites laying their eggs in other species’ nests, where the enterprising young tykes then pretend to be the offspring of the unlucky suckers who are to play host.  Brood parasites can be specialists that only parasitize the nests of a target host species (or small group of species); an example of this is village indigobirds, who generally parasitise fire-finches (and who also display an interesting mechanism where the young copy the songs of the host species).  Generalists, on the other hand, will parasitise a range of host species;  cowbirds, for instance, are generalists.  Brood parasites can also vary in whether they eliminate the other offspring of the host that they have colonized (nestmate killing) or whether they attempt to blend into the crowd (nestmate tolerant).  To make this more concrete, take a look at this short video showing a newly-hatched cuckoo ejecting a reed warbler chick from the host nest:

The paper I’m talking about here explores an interesting question about brood parasites, namely:  why are some brood parasites nestmate tolerant while others are nestmate killers? Gloag et al. propose a mathematical model that explains this in terms of a “provision trade-off”.  Host nestlings can help the newborn parasite by stimulating the host parents to bring more food than the parasite could solicit alone, and if the parasite can outcompete its nestmates for that additional food, then it does better to let them live.  Thus the trade-off:  when the host offspring increase the fitness of the parasite, it lets them stay, but otherwise it kills its flatmates.  Gloag et al. take the time to break this trade-off down into its constituent parts, namely (in their words, p. 2):

  • The total provisioning rate stimulated by the whole brood, and
  • The share of the provisions received by a parasite nestling.
The simple model they derive shows that when the ability of a parasite to stimulate food provisioning by the host parents is greater than its ability to compete for food with its nestmates, the parasite will do best if it is reared alone and the murder spree begins.  This relationship depends on the interaction between these two variables;  in other words, “[i]f each host nestling causes a greater increase in provisioning than the amount it consumes, then the presence of host chicks would result in higher consumption for the parasite, even if a host chick takes a bigger fraction of the extra food than the parasite.”  The model helps to predict where each scenario – nestmate killing or tolerance – is plausible as a function of this intuitive trade-off.
VIRA-BOSTA (Molothrus bonariensis)

VIRA-BOSTA (Molothrus bonariensis) by Dario Sanches, on Flickr

Gloag et al. then use this model to explain differences not only intra-specific differences between specialist species in their level of nestmate tolerance, but also inter-specific differences within generalist species as well.  This would have been a good paper even if they had stopped there, but they then go on to test their ideas in the field using a generalist parasite, the shiny cowbird (Molothrus bonariensis). Working in South America, they searched for the nests of two types of shiny cowbird hosts, chalk-browed mockingbirds and house wrens, and set up two experimental conditions.   In the “mixed group”, the a single cowbird egg was placed among host eggs, and in the “alone” group, the cowbird eggs were placed in the host nest with dummy eggs so that the cowbird young would be reared alone.  They measured the food amount and quality brought to the nest from video recordings, and measured the physical quality of the resulting offspring (weight and tarsus length).  They also compared the mortality rates of the cowbird chicks to see if there was a difference between the conditions.  Their findings?

In our field study, nestmate tolerant shiny cowbirds encountered both sides of a provisions trade-off depending on the host used. When reared by chalk-browed mockingbirds, nestling cowbirds had higher food consumption, mass gain, and survival when alone in the nest than when sharing with 2 mockingbird young. In contrast, cowbirds reared in the nests of house wrens had higher food intake and growth when reared alongside 3 or 4 host young than when reared alone. (p. 7).

The results of their work suggest strongly that there is a trade-off at work here, and that the virulence of parasite offspring will be affected by the provisioning characteristics of the host environment.  Of course, they are quick to suggest that there are other factors potentially at work in differential growth rates, such as thermoregulation (larger broods can help each other thermoregulate) or size of the nestlings.  Nestling size is an interesting issue, because as the authors mention, cowbird young are larger than house wren nestmates but equal in size to or smaller than their mockingbird counterparts.  This may the competitive ability of the young either through physical competition between nestlings where size would be important, or because parents preferentially feed larger offspring.  (As a by-product, this also raises the longer-standing question of why host parents don’t do a better job at discriminating among their young for parasites in the first place;  for an explanation in terms of yet another trade-off, I’d refer you to this letter to Nature by Arnon Lotem as a possibility).

Wilson's Warbler feeding it's Cowbird chick  "offspring"

Why are you feeding this monster? (by Alan Vernon, on Flickr)

The work on trade-offs in this paper provide a simple and intuitive model for the action of brood parasites across a wide variety of situations, and then back it up with empirical data that demonstrate this trade-off in action.  It’s hard to ask for more from a paper!  Of course, as with every paper you’ll ever read, “more research is needed” (we have to say that, or we’re straight out of a job, aren’t we).  It wil be interesting to see if this trade-off does actually hold in other species, and combining the principles in this paper with a phylogenetic analysis would make for a fascinating approach. In the meantime, though, if you’ve read this far I’d urge you to take the lesson of this paper to hear and learn to look for the trade-offs inherent in many biological systems.  As a guiding principle of biology, I guarantee that you’ll see it almost everywhere you look.

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Bees going, going, gone? Maybe so, but…

Honey Bee Colony Collapse Disorder, In Context – MYRMECOS.
Drove through a series of posts on colony collapse disorder today as linked to cell phones today following the hoopla over a report on the matter a couple of weeks ago and ended up at this awesome post with a truly eye-opening graphic.  It turns out that maybe the bees aren’t suddenly dying off like the news stories would have us believe.  It turns out that the western honey bee population has been declining since World War II!  Who knew?  It’s definitely worth reading, so have a click.

Oh, and cell phones?  Seems unlikely that they’re killing bees or giving us brain cancer.

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My Ph.D. in plain English…

I guess that this meme has been going around on Twitter for a bit – I picked it up over at Carly Tetley’s blog Wildlife Research and Training – but I thought that since I had submitted my thesis to the department for review (avant de la défense), it was a good time to write about my Ph.D. work in plain English.

My Ph.D. research has been about the evolutionary foundations of social foraging behaviour in animals.  What does that mean?  Well, social foraging is the study of foraging decisions that animals make when they’re in groups, and when the decisions that they make depend on what the other members of the group are going to do.  This is an inherently game theoretical problem.  Now, that won’t mean much to you unless you know what game theory is, so here’s an illustrative example:  imagine that you’re at a party, and you get snackish.  You look over and see the snack table loaded with all sorts of goodies, from cookies to cakes and everything in between.  Individual (a.k.a. optimal) foraging research would study your decision of what snack to take based only on what snack you prefer.  Social foraging research would consider your decision-making process when you and your two best friends head to the snack table at the same time.  If all three of you like cookies, and there’s only two cookies left on the table, then it might be a smart decision for you to switch to cake – even though you prefer cookies over cake – rather than engaging in a bare-knuckle brawl over the last piece of chocolate chip heaven.  We can apply the same logic to the study of animals foraging and interacting in groups.  (If you’re paying attention, you might notice that there’s a third possibility where individuals forage in groups but make decisions independently;  this scenario corresponds to the outcome where everyone at the party has their own plate of goodies to choose from.  You forage together, but your decisions don’t affect each other).

Birds foraging socially...

A slide from my Ph.D. seminar: birds foraging socially.

We know that a lot of species across many taxa forage socially; for instance, it has been observed in birds, fish, mammals, and there’s even evidence for insects and possibly bacteria.  In these foraging species, the most common social foraging game observed is what’s known as the “Producer-Scrounger game”.  This is a game in which individuals take one of two roles, as the name suggests:  producers or scroungers.  Producers spend their time searching for food resources, while scroungers wait for a producer to find a food resource and then they join in the discovery.  Extending the party metaphor above, if you were producing you would be searching through the room to find a table with food on it;  a scrounger would be that lazy friend who waits for you to do the work of finding the goodies before strolling over to take advantage of your effort and help themselves to whatever’s on the table.   In foraging systems, there will be an mix of these two tactics where the “fitness” (usually measured by proxy as food intake, i.e. the number of cookies you scarf) of the two are equal.  This is what’s known as an ESS, or evolutionarily stable strategy.  I don’t want to delve too deeply into evolutionary game theory here, but you can think of the ESS as the best mix of producing and scrounging for you to play given the mix that everyone else is playing.

That’s the back-story to my Ph.D.  My research has focused on the theory of these social foraging games, and how to extend them to match real foraging situations more effectively.  For instance, most of the work done on the producer-scrounger game to date has been very agnostic when it comes to representing the world spatially.  This is deeply weird to me, because if you spend more than a few seconds looking at animals foraging in the wild it becomes obvious that spatial relationships – both between foragers and between foragers and their environment – have a significant impact.  Close foragers will interact more heavily; a patchy, broken landscape will be different to forage on than a regular grid with patches spaced evenly;  and so on.  Adding these spatial components into the theory of social foraging has been a major focus for me.

The other major theme of my thesis has been information use.  In behavioural ecology, “information” has a specific meaning that relates to how animals use observations of the world around them, especially other animals, to make decisions.  In foraging terms, this often works out to “Hey, how is Bob getting along at that patch over there?  Oh, he hit the jackpot!  Let’s go get some of that!”  Anthropomorphism aside, we can ask sensible questions about how animals collect and use public and private information.  Glossing over some nuances, we can think of private information as information gathered by the animals itself and not accessible to any other observer, like information about the richness of a patch gathered by sticking your head into it.  You can see what’s in there, but no-one else can.  Public information, on the other hand, is information that is accessible to anyone who’s paying attention.  If I’m a producer who has found a food table at a party, this becomes obvious to anyone tracking my movements when I begin stuffing cookies into my mouth as fast as I can.  Scroungers rely on public information to scrounge, otherwise the game would break down;  this means that information use is central to the study of social foraging.

For historical reasons, though, behavioural ecologists haven’t spent much time thinking about the mechanisms by which animals use this information.  They’ve vaguely assumed that natural selection will have worked this out, but haven’t done much to figure out what that product will be.  In social foraging, it has always been assumed that natural selection will bring animals to the producer-scrounger ESS (the optimal foraging strategy) on its own.  But we see animals adjusting their use of the producer and scrounger tactics over their lifetime, and often on a very short time scale (seconds, not generations) as they respond to rapidly changing environments. So how do they do this?  I’ve spent a fair bit of time looking at mechanisms that will allow an animal to learn an ESS, and how natural selection might act on those mechanisms instead of fixing an ESS right off the bat.

Answering these questions, both about space and learning, has required the use of computer simulations to augment the mathematical models that currently exist;  unfortunately, creating new formal models of these processes is an extremely difficult task and I prefer to let the computer do that work for me.  Therefore, I’ve spent a lot of time creating individual-based models and genetic algorithms to study these questions;  in the interest of keeping this post to a reasonable length, I’ll refer the interested reader to the Wikipedia pages for those topics, and I would be happy to answer any questions in the comments.

And I could talk about this for hours, but I think I’ll cut off the level of detail there so that I don’t drown innocent readers in progressive elaborations.  In any case, that’s a high-level view of the type of research that I have been involved in for the past four years.  Please feel free to ask questions in the comments!

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Valuable lessons were learned…

At UQAM, one of the required activities to achieve the Ph.D. is to complete a seminar for the department, which I bused into Montréal yesterday to give today.  As I have been reading and thinking about presentations in science lately (including Presentation Zen, the great book by Garr Reynolds), I spent a long time working on the visuals for my talk and doing my best to create a talk with a coherent narrative and a sound logical structure.  Having arrived in Montréal yesterday, it occurred to me to actually – well, you know – practice the talk I had spent so long on.  Now, I don’t usually have timing troubles with my talks, but when I practiced it last night I came in way over time.  And I mean well over time – to the tune of at least a half an hour if not more.  Much panicked re-arranging and paring-down ensued, ending with me cutting nearly a quarter of my slides.

I had to practice the talk several more times over the evening and then again in the morning, and then race into the lab to give the bloody.  Of course, due to a series of awesome coincidences – including my talk apparently being scheduled during a large training session which soaked up most of my audience – I ended up giving my talk to a mostly empty room.  Add to that a MacBook which has developed a new and exciting habit of randomly rebooting without warning and a projector that flickered on and off in a stochastic fashion, and it was a pretty stressful couple of days.

But I can’t complain too much:  the talk went off okay and was received well, I passed the “course”, and my advisor tells me that my thesis is ready to submit.  So despite the issues, it’s been a good couple of days.

One thing that makes me a bit sad, however, is that many of the slides I had to cut contained illustrations from my wife;  her work greatly enhanced the visual presentation of the work, and it’s a shame that no-one got to see some of them.  With that in mind (and following up on this post), I’ll wrap this up with a few slides from the cutting room floor and a couple that were actually in the talk.

The incompatibility slide.

A slide about the incompatibility assumption in producer-scrounger games.

A slide about the patch discovery rate in producer-scrounger games.

The scrounger convergence assumption in producer-scrounger games.

And one of my favorite slides of the whole talk:

A slide I used while I was arguing for the importance of spatial processes in social foraging.

Ooh, and one final one that I can’t forget:

And a slide introducing a model I worked on about foraging and animal personality...

(Note:  she gave me permission to post these, so please don’t rip them off.  If for some reason you think that they could be useful to you, drop me an e-mail).

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From my upcoming PhD seminar..

I haven’t had a lot of time to post in the past few days as I prepare for my Ph.D. seminar at UQAM on the 15th, and I get on a bus Monday to go to Montréal (from Edmonton!), so it’s unlikely I’ll be doing anything inspiring from there either.  So, just to prove that I’m still alive, here’s a slide from my slide deck for the presentation I’ll be giving…

Note:  this is the required seminar that I have to give, not my thesis defense.  (That’s still to come, hopefully in a couple of months!)

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Overlooked classic paper: Spatial effects in group foraging.

I’ll be defending my thesis in a few months time, and I’ve been re-reading old papers recently to get myself in the right head-space. A few days ago, I came across a paper that I read at the beginning of my Ph.D., but otherwise forgot about until now – which is a shame, because it’s a good paper. Worse, it’s the sort of paper that should have gotten more attention than it did, or at least more attention than I’ve seen it get. This issue, of the endless treadmill of peer-review publications fading into the twilight (sometimes a mere weeks after publication), has been on my mind lately. But that’s the virtue of having this blog: I can dig this paper up and give it some of the attention that it does deserve.

Of course, since I’ve done this a bit clumsily, you have no idea what paper I’m talking about. So, let’s bring it out into the light:

G. D. Ruxton. Foraging in flocks: non-spatial models may neglect important costs. Ecological Modelling, 82:277–285, 1995.

Behavioural ecology is a discipline that has, to date, been largely focused on problems that can be modelled analytically by solving equations or simple games, or perhaps deriving more complicated equations that were solved through numerical methods. But some behavioural situations are not so amenable to these types of analyses, and so we tend to turn to simulation work to help bridge the gap. One such example is group foraging. Foraging models, be they optimal foraging models (where individuals forage individually, even if they do so in a group) or social foraging models (where individuals use information from other foragers to influence their own decisions), have rarely been situated in a spatially-explicit world. The reason for this is simple: spatial models are hard. Simulations are a great way to handle these types of models, and in recent years this has become more common – my own Ph.D. work is a fair example of this! – but in the early-to-mid-90s, this was just in its infancy.

Enter Dr. Graeme Ruxton, who in 1995 wrote the paper above to make the point that ignoring spatial effects in group foraging models was doing a disservice to the understanding of these behaviours. Because I think it’s an important point, and because 15 years later I’m flogging the same point in some of my own work, I’m going to spend a few minutes explaining Ruxton’s model.

Ruxton diagram 1

A schematic view of the individual-based model.

The model is what would now be called an individual-based model (IBM; also known as Agent-Based Modelling, ABM). Foragers moved across a 1000×1000 regular grid, searching for food patches that were scattered across the grid (see the diagram). When an individual found a food patch, every other searcher abandoned what they were doing and moved toward the food find until every forager was at the same patch. This may sound a little odd – what if the patch was exhausted? – but because the goal was to look at how spatial effects played out in terms of search time and movement time, Ruxton didn’t record feeding rates or any such thing (this issue is explored more thoroughly in a paper published the same year with S. J. Hall and W. S. C. Gurney). If you’re familiar with social foraging models, you may recognize this model as a simple example of an Information-Sharing game; I plan on returning to this theme in a later post.

The point of the paper was to ask what effect various movement strategies would have on the time it took for individuals to find patches. In particular, how long would it take to find the first searcher to find a patch, and how long would it take the rest of the group to converge on that patch? To do this, he explored six different movement rules, of which I’ll present four here. (You can read the paper for the other two; the point here is to discuss the importance of the paper, not replicate it in full).

  • A simple random walk. The individual moves in any of the eight possible directions, randomly chosen each turn. All eight directions are equally likely to be chosen as the direction for next turn’s movement.
  • A group biased walk: individuals would move more often in a direction chosen at the start of the simulation. For instance, they might prefer to move to the right, so more of their moves will be to the right as opposed to the other seven directions. The strength of the preference is tweakable, and the entire group has the same direction preference.
  • An individual biased walk: same as a group biased walk, but every individual has their own preferred direction.
  • Site marking: individuals mark sites that they’ve been to, something like ant pheromone trails; searches don’t enter a marked site unless they have nowhere else to go. Site markers fade over time, and the decay time is adjustable.

The paper goes on to explore the effect of these rules on the searching and convergence times, presenting results for each rule and contrasting them with the results from classical models. I’ll mention a couple of the results, but because I’m uncertain as to what I can clip from the paper in terms of figures, I can’t get too deeply into it (now there’s something I need to look into!) and I’d recommend giving the paper a read.

One of the more interesting results relates to the time taken to find a patch as a function of the number of searchers. Classical models expected that this was linear: add more searchers, and your search time goes down in a straight line fashion. But Ruxton’s results showed that adding more searchers might not have such a linear effect, because often new searchers would blindly trample over spaces that previous searchers had already covered. Thus, you might get diminishing returns to search efficiency from adding more searchers. (You can see this in Figure 1b from the paper, which I’ve reproduced here). The rest of the paper explores the individual effects of each of the movement rules, and it’s worth reading for that alone.Ruxton diagram 2

But why is this important? Well, behavioural ecologists studying the evolution of group behaviours like foraging might incorrectly predict – or have trouble explaining – the size of foraging groups based on these search efficiencies. Worse, they might not build these diminishing returns into their models as a cost of increasing group size when trying to explain why individuals form groups and why groups are of a certain size. Because these effects come directly from incorporating spatial structure into the model, classical non-spatial models can’t easily take them into account.

This paper is especially important because it was one of the first – to my knowledge – to make this point so clearly. It’s not the first to argue that spatial effects are important, by any means, but it does so in a way that highlights the importance of these effects to the study of foraging. Unfortunately, despite fits and starts over the years to follow, this lesson is only now starting to take hold in a big way in behavioural ecology (as far as I can see!). I’ll say it: I’d recommend that anyone studying the evolution of foraging behaviour should read this paper, if only because it foresees issues that we’re grappling with right now.

And hey, it’s only 9 pages. Not too bad, right?

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