Competition avoidance drives foraging plasticity

A friend of mine recently published a great article that features several interesting topics in animal behavior research right now. The lab I worked in as an undergrad works on personality of threespine stickleback fish. Dr. Kate Laskowski, who just successfully defended her thesis last week, conducted an experiment that tested models predicting that a variable environment can drive between-individual differences in the plasticity of behavior. It’s a bit complicated but the implications are really, really cool. This blog post will be about how avoiding competition can drive different personality types. Dr. Laskowski was kind enough to provide a commentary at the end of the post. If you have access to an academic database, you can download the full article here.

Article details
- Laskowski KL, Bell AM. 2013. Competition avoidance drives individual differences in response to a changing food resource in sticklebacks. Ecology Letters. 16: 746-753.
- School of Integrative Biology, University of Illinois at Urbana-Champaign

Very brief summary
Behavioral plasticity is the ability to change your behavior in response to a change in the environment. Models predict that trying to avoid competition in a heterogeneous environment can promote between-individual variation in plasticity. In other words, if competition avoidance is possible, it'll be beneficial for individuals to consistently differ from one another in how responsive they are to changes in the environment (i.e. some are very responsive, others not responsive, and others in between). This study provides empirical support for these models.

Glossary

- behavioral plasticity – the ability to change one's behavior in response to the environment. There is a cost associated with behavioral plasticity, for example the time spent traveling to a different foraging patch.

- between-individual variation – in a group, the variation between all the individuals. So,

how much one individual will differ from another individual. An increase in between-individual behavioral variation means individuals are behaving more consistently (decrease in within-individual variation) or there is a bigger variation in how the whole group acts.

In the graph on the right, you can see that by decreasing the within-individual variation (smaller bars) would increase the between-individual variation. Decreasing within-individual variation would mean the animals are behaving more consistently. (Note: in reality, this would be much harder to show as a graphic because you'd have lots of individuals, with many of the bars overlapping. This is an extremely simplified version.)  

- heterogeneous – non-uniform, patch. e.g. a salad is a heterogeneous mixture. (Contrast with homogeneous.)

- personality – more accurately referred to as “consistent individual differences in behavior.” These consistencies extend over time and across contexts. Personality has been documented in a wide range of taxa, from insects to primates.

- within-individual variation - how much one individual varies. As you take multiple measurements of a behavior, you will have variation because the animal doesn't act exactly the same every time. 

Article summary

Models on plasticity

In nature, we'd expect animals to be able to react perfectly to their environment (i.e. be perfectly plastic). Animal personality means that animals are not perfectly plastic, however. Animals don't conform to the best behavior for every context, instead differing from one another in aggressiveness, sociability, exploratory behavior, boldness/shyness, and more. This means the most aggressive fish in a group will still be the most aggressive across different contexts, like foraging, exploring, but also in the presence of a predator. The graph on the right, a reaction norm, illustrates this. Each line is a different individual.

Darwin's theory of natural selection clearly shows how variety can be beneficial. A species where everyone is identical means that the possibility exists that one flaw they all possess - susceptibility to a certain disease, for example - could wipe everyone out. Done, extinct, left only to artist's impressions in textbooks. But being different from one another in the case of personality isn't always beneficial; in an extreme example, some female fishing spiders are born so aggressive that they will kill any males before they can mate, meaning their genes never get passed on. (There's a lot of discussion on why limited plasticity should exist at all.)

Interestingly, not only are animals not perfectly plastic; they also differ in how plastic they are. In other words, individuals vary in how much they'll adjust their behavior to a change in the environment. Consider this example: a heavy rain leaves puddles of water in a forest that attract mosquitoes and other insects that lay eggs in water. According to currently-accepted behavioral plasticity models, some bats will shift their flying pattern to include these new sources of food, while other bats will stick to the spots where they've found food before. Think of it like deciding whether or not to eat at McDonald's or some new restaurant that opened somewhere a few streets over.

But now, picture that both McDonald's and the new place (let's call it Joe's) are severely limited in food supply. McDonald's only has 100 happy meals and Joe's only has 100 Joe burgers. You and 199 of your classmates have half an hour to eat lunch. Everyone used to just go to McDonald's without thinking about it, but now what do you choose? There's not enough happy meals, but who knows what a "Joe burger" even is? You're not sure where Joe's actually is, either. 

Some animal behavior models predict that this uncertainty about the environment can drive differences in plasticity. So while McDonald's was always open before (i.e. a stable environment), the appearance of a new restaurant (a change in food availability and distribution) could drive these differences between people's decisions on what you do on your lunch break (plasticity). To add an additional layer of complexity, social dynamics (who's in a group with you) have been predicted to also affect behavioral plasticity, but it's unsure how. It's like quantifying the effect of who's in the car with you when you're deciding where to eat.

Testing the models

As logical as these models (and the McDonald's example) may sound, there have been few studies on real animals to back up what the models say. Laskowski decided to test these models using threespine sticklebacks (right). Threespines are social fish commonly used in animal personality research, ideal for a study on behavioral plasticity and social dynamics. They're also easy to work with in a lab. 

Specifically, Laskowski tested these predictions:

1. When it's possible to avoid competition in a social group, the between-individual variation in plasticity will increase
 - Put simply: If everyone has the same level of responsiveness to the environment (e.g. a new foraging patch appears so everyone goes there instead of staying at the old patch), everyone will be competing for food. If it's possible to avoid this competition (e.g. the old patch is still there, so there's food for those who stay and those who leave will find food at the new patch too), there will be an increase in how responsive individuals are to a change in food availability.

 - If this is true, this should happen: 
When a new patch becomes available (and the old one remains), some individuals will consistently choose to stay and others will consistently choose to go to the other patch. (This differs slightly from the concept of ideal free distribution in that the individuals themselves will consistently make the same choice about staying or going, as opposed to just choosing where to go based on how many individuals are at each patch.)

- If this is not true, this should happen:
When a new patch becomes available (and the old one remains), individuals will just conform to ideal free distribution. Whether they choose to stay or change patches will be random and solely based on how many individuals are at each patch.

2. The social environment influences the variation in plasticity  
 - Put simply: Those around you can influence whether you consistently stay or switch to a different patch.

- If this is true, this should happen:
When a new patch becomes available (and the old one remains), an individual's tendency to stay or switch patches will depend on who is in the social group.

- If this is not true, this should happen:
The social group will not affect tendency to stay or switch.

Methods
Laskowski used two "regimes" to measure behavioral plasticity. In the "simultaneous patch regime," bloodworms were mechanically dropped into one side of a tank with six sticklebacks. After 5 min, half of the food was dropped into one side of the tank and the other half was dropped into the other side for 5 min. In the "sequential patch regime," after 5 min of food dropping into one side of the tank, the food started dropping into only the other side of the tank for 5 min.

Laskowski used six groups of six sticklebacks that were assigned to one of the two regimes. Each group was tested in two trials per day on five consecutive days to get the repeatability of behavior. The main variable she was interested in was switch delay, or latency to switch to the new patch.

To test the influence of social group, she then shuffled individuals between groups so that only two individuals from the same group were in their new one. Then, the fish were tested in the same regime as their original group. She also tested whether individual behavior in a group was related to individual behavior while alone. This was done by also testing fish from the simultaneous patch regime while alone. 

Data analysis 

Question 1: Competition avoidance
Bayesian statistics was used (as opposed to frequentist) with Markov Chain Monte Carlo simulations. Because there were multiple data for each individual - making the data  non-independent - Group and Individual (nested within Group) were included as random effects in the models. The repeatability of latency to switch patches, this experiment's measure of behavioral plasticity, was estimated.

Question 2: Effect of social group
A separate bivariate mixed model was used to estimate the covariance between switch delay in the original and shuffled groups.

Results

Result 1: The opportunity to avoid competition promotes between-individual variation in plasticity
Individuals in the simultaneous patch regime showed consistent individual differences in switch delay (repeatability = 0.18, 95% CI: 0.05, 0.38). Individuals in the sequential patch regime showed very low between-individual variation (repeatability = 0, 95% CI: 3.0 x 10^-10, 6.6 x 10^-9).

This means that in the simultaneous patch regime, which allows for variation in staying or switching foraging patches, individuals did adopt a consistent strategy to avoid competing with one another. When there was no option to change your strategy to avoid competition (i.e. the sequential patch regime), there was no point in trying to act differently than everyone else. This makes sense, because there's only one source of food.

Importantly, the repeatability in the simultaneous patch regime increased over the course of the five days of the experiment. Specifically, the within-individual consistency grew higher. This means that individuals were becoming more consistent in their strategy. This positive feedback was not observed in the sequential patch regime.  

Result 2: Individuals maintain behavioral plasticity across different social environments in the simultaneous patch regime

In the simultaneous patch regime, there was significant covariance between an individual's switch delay in the original and shuffled groups. This indicates that in an environment where competition can be avoided, the social group does not influence behavioral plasticity.

In the sequential patch regime, there was essentially zero variation in switch delay between the original and shuffled groups, making it impossible to estimate covariance. However, this also supports that there's little carryover across social contexts. This means that for behavioral plasticity in patch choice, this study found no support for social environment having an influence.

Conclusions

Recent models have predicted that individual differences in plasticity are more likely to emerge in a spatiotemporally variable environment and when opportunities to forage are limited by competitors. This study provides strong empirical support for these models. Consistent individual differences were only apparent in the simultaneous patch regime, where competition avoidance was possible. This suggests that ecological factors such as food availability and predictability might influence variation in plasticity. Also, social environment did not have a strong influence on plasticity of switch delay: while individuals differed somewhat in their behavior between the two social groups (i.e. the covariance was not perfect), there was still evidence of consistency in behavior across the two groups.

This study also showed that the presence of highly plastic individuals in a population is likely to drive ideal free distribution, as competition decreases when plastic individuals hurry to the new patch and non-plastic individuals stay at the old patch. 

If you have access to an academic database, you can download the full article here.

The author comments - Kate's thoughts: 
"Running this experiment was a serious labor of love. Just collecting the data took a long time: I had so many groups of fish and they needed to be tested twice a day for five days. At the time, I only had one feeding arena in which to test the fish, so this mean that I could only test one group a week – resulting in a total of 15 weeks of data collection! I started having dreams about dropping bloodworms into fish tanks; it seriously felt like it was never going to end. But the really hard part of the experiment came with the data analysis.  I had to learn how to use R to run the Bayesian analyses, both of which I was completely unfamiliar with. Those were long dark weeks that I don’t remember a lot about other than staring at a computer screen for hours. But finally, I figured it out and was able to apply these fancy new statistical techniques to my data, which really improved the paper. And not to mention now, I’ve gotten quite good at both R and mixed modeling techniques which is an awesome (and very marketable) skill to have. So even though it felt like it took forever, in the long run it's totally worth it."

Gap years: trying things out

Originally written June 2013 for the University of Illinois's School of Integrative Biology blog

Hi all,

In the sciences it’s easy to get in the mindset of “go to college, go to grad school, get a post doc, be a professor” for your career. While this path works, I want to talk about the crazy idea of breaking from the path for a year or two before you throw yourself into a PhD program. (Note: this applies to people applying to professional schools like medicine or law, as well.)

To preface, going into grad school straight out of college does work. There’s a lot of people who do it and do it well. If you want to make a career as a researcher, starting grad school early lets you throw yourself at the rigors of a PhD while you’re young and energetic. But grad school is a huge commitment. Do you like fieldwork enough to get up at 7am on a Sunday if you have to? How do you react to the concept of motivating yourself to read a textbook, as opposed to it being assigned reading, for (probably) the first time? Are you ok with cutting your free time to the point that it’ll be a challenge to be simultaneously in that weekly journal club and working on that novel you’ve always thought about?

If you like your subject and are motivated, none of these questions should be that intimidating. But there’s nothing forcing you to throw yourself into all that straight out of college. Dr. John Cheeseman, the former head of IB Honors at the Univ. IL, continually tried drilling into my head the idea that I don’t have to go to grad school right away, that there are literally dozens of other cool options to consider. Your post-college path can veer wildly away from research, taking you across the world and letting you try things you’ve never had the chance to do before.

Yeah, I didn’t believe him. If you’re anything like I was as an undergrad, all of that will sound like a nice what-if, the cool life of someone who’s not in your shoes. Applying to a PhD program as a senior in college meant having knowledge and stability on what would happen in the half decade (at least) after college. With unemployment as high as it is right now, let’s just take what we can get, right? And with literally the whole world open for the first time, I found myself surprisingly drawn to the idea of “settling down” into a PhD program, where I know what I’m doing in a month, this summer, next year.

The point of this post is to pull a Cheeseman and encourage you to think outside the box. The following alternatives to immediately starting grad school are applicable to new graduates looking for something to fill a few months before more permanent plans, rising seniors looking for stuff to do after graduation, and basically anyone else looking for summer plans.

1. Do research in another country on a Fulbright grant
Starting with the option that deviates the least from the research path, the Fulbright is something I highly recommend students interested in research consider. The Fulbright is funding for one year to teach English or do research with a local organization in another country (i.e. you can’t work with an American researcher stationed in Panama). If you want to do research someday, this is a fantastic opportunity to try independent research out, not have it go as you expected, and then learn how to better conduct research. When you enter grad school for real, you’ll have the knowledge of your Fulbright year as a head start on learning to do quality work. Also, the experience of living in another country for a year, especially fresh out of college, teaches you a lot about yourself.

2. Work as a field assistant / do a lab internship
Part-time work in the area of research that interests you, especially if you can find something that pays or at least breaks even (e.g. housing is paid for), is awesome. For biology fieldwork, job boards like those of University of Texas A&M, the Ornithological Societies of North America, and Partners in Amphibian and Reptile Conservation constantly have researchers looking for people to help with fieldwork.

You could study lizards in the Tucson desert, search for crabs on beaches in Washington state, or even end up in Australia watching fairy wrens through binoculars. Tasmanian devils (right) need research love, too. The options are out there! A Fulbright alumna I know did the ‘fieldwork rock star’ lifestyle for a year, where she hopped from field project to field project. She barely broke even financially, but she got to spend a year traveling the world while simultaneously advancing her career. This is a great way to get exposed to a lot of types of research, too, and see what you like the best.

3. Ditch research for a bit
As much as you might like biology, it’s only one side of you. Ever wanted to tutor kids in creative writing? Find an apartment in San Francisco and intern at 826 National. Does doing manual labor on a farm in exchange for food and a place to sleep sound awesome? Check out work exchange programs like WorkAway. Programs like Fulbright and Footprints can let you teach English or other subjects abroad. When I interviewed at Cambridge (conflicted, I applied to do a PhD in Cambridge the same year I applied for the Fulbright. I didn’t get in) and was discussing the potential for gap year(s) before grad school, my proposed adviser there said about 4-5 years without science research should be the upper limit between finishing college and starting grad school. That’s a whole lot of time to pick cocoa fruit and build hiking trails in Costa Rica.

Conclusions
Graduating college is intimidating. Not knowing what happens next is scary. But it can also be exciting. I strongly believe (finally) in the benefit of taking time off, whether it be getting experience with research or trying something totally different. If you like the crazy different path you took, awesome! If it wasn’t what you thought and you miss research, grad schools are accepting applications every year. As I mentioned in the previous SIB blog post, grad schools will care more about your experiences with research than whatever grade you got in physics. This is one of the best times of your life to try something new. Don’t be afraid of trying something different just because you don’t know how it’ll turn out.

-Matt


Image source: Wikipedia

 

Consensus decison making in animal groups

One of the aspects of animal behavior that most fascinates me is how groups function. How does a collection of individuals coherently make decisions that affect the fitness of everyone in the group? Consider a swarm of honeybees. Each bee is completely reliant on the hive; a worker can't ditch the hive if things don't go the way that individual likes. Decisions like where to build a new hive are critically important because the group can't afford to split if everyone doesn't agree. I stumbled across a great review article a while ago (available here) that talks about how animal groups of many taxa come to decisions that affect the whole group, and this blog post will be a summary of that article.

Article details

- Conradt L, Roper TJ. 2005. Consensus decision making in animals. TRENDS in Ecology and Evolution. 20: 449 - 456.

- Department of Biology and Environmental Science, University of Sussex

Very brief summary

Animals in social groups frequently need to arrive at decisions that affect the whole group. These decisions can be categorized as either consensus decisions, where members choose (e.g. by voting) between two mutually exclusive actions, and combined decisions, in which the group's behavior is the amalgamation of each individual's actions. Consensus decisions differ in 1. the degree of conflict of interest in the outcome among group members, and 2. whether communication is local or global.

Glossary

 - combined decision - members of a group individually choose between multiple actions. The behavior of the group is the amalgamation of those decisions

• e.g. the movement of a fish school where individuals are constantly joining and leaving the school, the success or failure of a human department store based on sales

- consensus cost - the cost (food, mating opportunities, etc.) of adhering to the group's consensus decision as opposed to the behavior that would be optimal for the individual at that given moment

• e.g. leaving with the group from a foraging patch while you're still hungry
  

- consensus decision - members of a group choose between multiple outcomes with the intent of reaching a consensus

• e.g. where to build a new hive, which prey to go after in cooperatively hunting species, the flight path of migrating birds

- global communication - all group members can communicate directly with all other group members. This occurs in small groups (e.g. primate troops)
  
- local communication - group members can only communicate with their neighbors. This occurs in large groups (e.g. ant colonies, ungulate herds)

Article summary-----------------------------------------------------------------------------------

Human societies depend on consensus decisions to survive, from large-scale international agreements to small gatherings of a few people. Some of the most pressing problems today stem from failures to reach a consensus, such as inability to draft effective climate change legislation, negotiations over maintenance of nuclear weapons and sustainable water usage, and pursuing alternatives to oil and gas. Studying how animals reach consensus decisions may yield insights into why humans sometimes fail at them.

The fact that animals communicate non-verbally raises interesting questions about who makes decisions that affect the whole group and what happens when outcomes differ in how beneficial they are for different members of the group (i.e. conflicts of interest).

Who makes the decisions?
While researchers have often assumed that the dominant individual in a group leads consensus decisions (e.g. the alpha male deciding the next foraging patch), leadership is actually variable and there is no correlation between leadership and dominance. Species differ in the number of contributors in a decision, with a continuum ranging from unshared (very few contributors), intermediately-shared (e.g. one demographic, such as adult males), and equally shared.

It might seem that the 'smartest' individuals should be the ones making the decisions, but surprisingly accurate decisions can emerge when everyone in the group contributes. Say there are two foraging patches to choose from, and one contains a predator. Each group member has some idea of which patch has the predator, but no one's really sure. If a dominant male is 75% sure, there's a 25% chance everyone will get eaten if he's wrong. But if that male incorporates the decisions of others in the group (who, for simplicity, also have a 75% chance of choosing the safe patch), the probability of making a mistake shrinks to 16% with three members, 10% with five opinions, 7% with seven votes, etc. because the majority of the group would have to be wrong for the group to choose the predator patch. So, even if the dominant has a better idea of the right decision, its individual error is still larger than the combined error of inexperienced group members.

(Cool side note: this type of information pooling is well-known in humans. One famous example occurred at a cattle fair in 1906, when the statistician Francis Galton asked people to estimate the weight of an ox. Of the 800 responses, which varied tremendously, the mean was off by only 0.5 kg. Basically, all of the crowds' errors cancelled one another out, leaving an estimate more accurate than any individual guess.)

In small groups, it's possible for members to vote, such as by specific vocalizations, ritualized signals, body orientation, and initiation movements. Decisions follow the majority of votes, but little is known about how animals estimate the relative number of votes. In large groups, self-organizing rules could lead to equally shared consensus decisions when only local communication is possible. For example, simple rules like 'always forage when your resources drop below a threshold or when others are foraging' or 'move in the average direction of your neighbors if the difference is small or the direction of the majority if the difference is large' can dictate the behavior of large groups.

How much conflict of interest?
In many taxa, there is little conflict between group members over what to do because the goal is similar for everyone. In migrating birds, for example, everyone has more or less the same destination. In ants and bees, there is no individual benefit in arguing over where a new nest should be built. Decisions like choosing which deer to kill in cooperatively-hunting animals like wolves, or where a primate troop should rest after foraging, also require relatively little conflict of interest.

However, following what the rest of the group does frequently requires an individual to act sub-optimally. A simple example would be if the group is deciding whether to go to a foraging patch or a drinking hole; it's very unlikely that every individual in the group has the same relative level of hunger and thirst, so some would benefit from eating while others would rather drink. The preferences can extend to the type of food itself; in groups of white-nosed coatis (left), for example, some members are better at exploiting one fruit source while others are better at exploiting another, so the choice of which foraging patch to go to is significant. These costs are especially relevant when you consider that decisions about travel destinations have to be made several times a day, every day.

While it might seem best to leave the group and just do what's best for yourself, the anti-predator benefits of group living (e.g. spotting predators from further away, and reducing risk of being eaten by diluting yourself amongst others) outweigh the huge costs of leaving the group. Predators often have a marked preference for stragglers, for example. Hence, it's usually better to compromise and stick with the group.

One way many mammalian species have evolved to best deal with consensus costs is by forming single-sex groups. Males and females often have quite different metabolic demands, with females investing in offspring through pregnancy, lactation, and raising the young while males compete with one another for access to the females. Also, as the relative energy requirements decrease with increasing body size, allowing large herbivores to subsist on lower-quality diets than small herbivores (the Jarman-Bell principle), the larger the difference in body size between males and females, the more likely the groups are to sexually segregate. Red deer (right) are an example of a sexually dimorphic ungulate species that has intersexual social segregation.

Conclusions and Future Directions
Empirical evidence and theory suggest that consensus decision making is common in a wide variety of behavioral contexts and animal taxa, including humans. However, the mechanisms through which these decisions are made still require further research, as well as whether decision making is equally shared or unshared in small groups.

Information pooling via self-organizing rules (e.g. dispersing eusocial insects, homing or migrating animals) can confer fitness advantages not available to solitary decision makers. Put simply, living in a group gives you access to others' information, which can help you survive. However, to turn the statement on its head, little is known about how the advantages of being in a group affect the sociality of the species. For example, could the benefits of information pooling factored in the evolution of social group structure, or did something else drive the evolution of social groups (e.g. defense against predators) and the benefits of information pooling were peripheral? Examining information pooling in species with differing conflict of interest in consensus decisions may also help us better understand cooperation in decision-making.

The full text of this article is available here.

Image citations:
white-nosed coati: Wikipedia
red deer: animalpictures123.org