How the differences between individuals helps shape predation
By Anne Hilborn (@AnneWHilborn)
An interaction between an animal and its predator is one of the most important relationships in nature, as it determines who lives another day and who does not. The quest to avoid predation has not only shaped animal behaviour, but even their bodies as some species use camouflage to avoid notice. At the same time, predators adapt where, when, and how they hunt in order to find vulnerable prey. Together with Clare Duncan, Nathalie Pettorelli and Sarah Durant, we recently published a contribution that discussed how differences between individual animals can impact the interactions between predators and prey.
Scientists have long understood that predation is important, but the way we think about it has changed over time. The initial approach was very basic, we considered all individuals to be the same. This meant that if you were looking at the how a predator like a jackal affects a prey species like a mouse, you assumed that all jackals killed three mice a day, all mice produced an average of 18 offspring every month and all jackals produced two offspring for every 300 mice eaten. You plugged these numbers into population dynamics models and produced nice smooth curves of how the jackal and mice populations changed over time, and you could explore how they influenced each other. All very neat. Except anyone who spends time observing nature will tell you this isn’t really how it works.
And while the patterns predicted by the models were seen in some species in the wild, they are hardly universal. This may be due to the fact that the models assumed all individuals are the same, while in reality individuals can be quite different from each other (much like humans). Some jackals are better hunters than others. Some mice are better mothers than others. The habitat and other environmental factors like weather can influence both hunting success and breeding success. For a long time ecologists knew about these differences but tended to ignore them in modeling because they made things mathematically difficult. Another issue was that although we knew there were differences between individuals, we rarely had enough data to be able to model them. It was hard enough to get enough data to show that on average jackals ate three mice a day let alone enough to compare male and female jackals, old and young, different seasons or different habitats, let alone all the possible combinations.
Age, sex and social grouping can affect chances of being eaten
However, this is slowly changing. With better data collection techniques, evidence has been accumulating that individuals are different from each other in many ways. Now the challenge is to adapt our analysis techniques to be able to incorporate that variability instead of considering it a nuisance. Individual differences can impact different aspects of predation and the three we looked at are predation risk, prey selection, and functional response. Predation risk and prey selection are like two sides of the same coin. They both help determine which prey get eaten by which predator. Because not all prey are equally vulnerable to being snaffled up and not all predators snaffle the same things at all times.
Factors like age, sex, social grouping, habitat use, and body size can affect what prey predators capture, while for prey it can affect their chances of being eaten.
For example, male Thompson’s gazelles tend to stand alone at the edges of herds and not be as vigilant as females. Cheetahs find it easier to stalk close enough to make a successful chase on less alert, isolated individuals, so male Thomson’s have a greater risk of becoming part of a cheetah’s dinner than do female gazelles.
Old elk and old big horn sheep are more likely to be predated than ones in their prime. Male deer fawns are more active than female and thus get eaten by red foxes at a much higher rate. Tadpoles that had previously been exposed to the chemical cues of their predators were much less active and thus less likely to be predated than those who had never encountered that predator before.
Environment can play a role too. In Alaska, some streams are warmer by several degrees celsius than ones just a few kilometres away. Coho salmon juveniles hatch in the spring and those in the warm streams grow fast enough that by August they are big enough to eat the rich fatty sockeye salmon eggs during the few weeks sockeye spawn in the creeks. The coho in colder streams don’t grow fast enough to fit their mouths around an egg by August, and are relegated to eating lower quality insect food until the next August when the sockeye return.
Considering individual differences has sharpened our understanding of predation and can potentially make predator management more effective (at least some of the time). For example in Canada certain cougars specialize in preying on big horn sheep, raising concerns about impact on the sheep population. In such situations, a common management method is predator control. The idea is that by killing a certain number of predators, you lower the predation pressure on the prey. This would work if all predators killed prey at the same rate. But if only a few cougars are preying on sheep, killing random cougars isn’t likely to change the pressure on the sheep and could have dramatic effects on the cougar population.
On the theoretical side, it turns out that accounting for the differences between individuals can impact predictions of how populations behave. Many models predict large oscillations in population size of both predators and prey. This is great for some species (certain rodents, salmon, arctic hares, insects like spruce bud worm or tent caterpillars) that undergo large swings in numbers that match the models. But what about species whose populations numbers are relatively stable? Turns out that if you increase certain types of individual variability of a population and account for it in the model, population oscillations dampen down. This is interesting because it suggests that individuals behaving differently from each other may be a reason why models don’t often match reality, and if we account for it, our models could be much better and more useful.
If we can make our models better, what impact could that have? With better models we can better predict what might happen as the populations of predators and their prey shrink or expand. We would be more able to understand how a bunch of individuals acting differently from each other creates a population’s response to changes in prey numbers or predation pressure. Ideally this will help us scale up from individual responses to populations and from there to better understand how multiple interacting species determine how an ecosystem operates.
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