# Is it better to run or walk in the rain?

One of the many things affecting us living in the UK is the rain! Especially when we are caught out in it without an umbrella (or when we’re too lazy to dig it out of our bag). Intuitively it seems like a good idea to run, or at least walk faster so we spend less time in the rain, however this means that the rain hits our front at a faster rate. So what’s the best thing to do in order to minimise how wet you get? This problem has actually been discussed quite a bit in the past few decades, from mathematical journals to tv shows like Mythbusters (they actually did two episodes on this, the second being a correction!).

Let’s start with the simplest model, imagine the rain coming straight down at a constant rate. In the figure to the left, you are represented as the grey rectangle (since the rain has made you sad). Suppose also that the raindrops fall uniformly and such that you cannot walk ‘into’ it, i.e. the rain is only hitting your head. In this case, regardless of the rate at which the rain is falling, your best option is to move as fast as you can to minimise your time spent in the rain.

$k$ and $c$ are just positive constants here to show that the lines in the diagram are proportional to the actual vectors

Well, what if the rain is coming down towards you at an angle due to the wind? For this we’ll want to introduce some actual maths. Again, we will assume the rain is falling uniformly with a constant velocity $\textbf{v}_r$. An important concept in this problem is the rain region. This region contains the initial positions of all raindrops that will hit you at some time. Suppose you’re moving with speed $s$, so your velocity in 2D is $\textbf{v}_u=(s,0)$, scaled such that you spend the time $1/s$ in the rain. Let’s put a point $P$ on you, and take a point $Q$ from the rain region, representing a rain drop that will hit you at time $t$. Then, this rain drop will hit you at position $Q+\textbf{v}_r t$. Your original point $P$ can be written as $P=Q+\textbf{v}_r t-\textbf{v}_u t$. Thus for every (exposed) point on you, $P$, the points $P+ (\textbf{v}_u -\textbf{v}_r)t$ are in the rain region for $t \in [0,1/s]$. The rain region is entirely made up of these lines, each with length $||\textbf{v}_u -\textbf{v}_r||/s$. It should be clear then if the rain is coming down at an angle, hitting your front, you should run as fast as you can to minimise the length of the rain region (the ‘width’ of the rain region will be fixed, proportional to your height).

What if the rain is coming from behind you? Here things get a little more complicated. The components of the rain’s velocity are $\textbf{v}_r=(v_r^1,v_r^2)$ where $v_r^1>0$ since the rain is falling in the forwards direction. A few things distinct things can happen here (with our assumption of the rain falling uniformly, at the same velocity). Recalled you move with speed $s$. If $s>v_r^1$ you will overtake the rain falling from behind you and only your top and front gets wet. If $s=v_r^1$ you are moving with the rain and only the top of you gets wet. Finally, if $s<v_r^1$ the rain hits your back and top, but you do not walk into any rain so your front remains dry. Let $A_{fb}$ be the area of your front or back exposed to the rain, and $A_{t}$ is the area of the top of your head. In our 2D case, these would just be the height and width of your ‘rectangle’, respectively. The amount of rain hitting these parts are then proportional to $R_{fb}=|v_r^1-s|A_{fb}$ and $R_{t}=|v_r^2|A_{t}$ (again, respectively). Recalling that the time spent in the rain is $1/s$, the total wetness function, $R$, is proportional to:
\begin{align} R_1(s)=\frac{1}{s}[ (v_r^1 – s) A_{fb} + |v_r^2| A_{t} ]; \quad \text{if } s\leq v_r^1 \nonumber \\
R_2(s)=\frac{1}{s}[ (s-v_r^1) A_{fb} + |v_r^2| A_{t}]; \quad \text{if }  s>v_r^1
\end{align}
where the multiplier for the proportion is just the density of the rain. Note that this function can be applied to the previous case too where the rain is falling backwards, into you ($v_r^1<0$). We find that we always have $R=R_2$ and the way this is minimised is to increase $s$ as much as possible.

For the case of the rain falling forwards we have $v_r^1>0$. Let $C=-v_r^1 A_{fb} + |v_r^2| A_{t}$ and notice that $R$ is continuous, $R_1$ is a decreasing function of $s$, whilst the behaviour of $R_2$ depends on the sign of $C$.

• If $C>0$, $R_2$ is also a decreasing function of $s$ and $R$ will be minimised when $s$ is increased to its maximum.
• If $C=0$, then $R_2$ is a constant and we can minimise $R$ by taking any $s\geq v_r^1$.
• If $C<0$, $R_2$ is an increasing function of $s$, so we can only minimise $R$ by taking $s=v_r^1$, i.e. you run at exactly the horizontal speed of the rain.

$C$ depends on your size and the velocity of the rain. If the rain is only slightly falling forwards, then your best option will still be running as fast as you can! However, if the rain is falling forwards by a decent amount (such that $C<0$), then you’re better off running at exactly the horizontal speed of the rain. This also means the rain will only be hitting your head (theoretically)!

There are many more complicated models for this, taking into account things like different shapes (other than rectangles) or gusts of wind which affect the final conclusions.  Instead, we’ll just end on a limerick by Matthew Wright (unfortunately, not the previous member of Chalkdust)!

When caught in the rain without mac,
walk as fast as the wind at your back.
But when the wind’s in your face,
the optimal pace
is fast as your legs will make track.

In many of these related articles, you’ll find this limerick as a longer poem, adjusted to include the new results! For example, here is one by Dan Kalman and Bruce Torrence (or as they called themselves, Dank Hailman and Bruce Torrents):

When you find yourself caught in the rain,
while walking exposed on a plane,
for greatest protection
move in the direction
revealed by a fair weather vane.
Moving swift as the wind we’ll concede,
for a box shape is just the right speed.
But a soul who’s more rounded
will end up less drownded
if the wind’s pace he aims to exceed.

# Debugging insect dynamics

Social dynamics are complex and have evolved over many generations. One strategy that is used is that of altruism: the act of helping someone else at a cost to yourself. In some insects, this takes on an extreme case where workers sacrifice their own fertility to help raise the queen’s eggs instead. While this may seem to go against the idea that animals want to pass on as many of their genes as possible, we’ll see why this is actually a viable strategy.

Game theory examines how the frequencies of different strategies played in a game change over time. Evolutionary game theory looks at the special case where players cannot change the strategy they play: they’re stuck with the strategy they’ve inherited from the previous generation. In each round, players are randomly matched up and play a game, leading to an outcome dependent on their strategies. This outcome is called a payoff, and it affects their fitness and thus the number of offspring they produce. In the 1970s, John Maynard Smith and George Price developed evolutionary game theory to investigate ritualistic fighting behaviour in animals. A good example is how male stags will compete for territory during the mating season. Physical contact is actually unlikely to occur and the stags can spend hours staring and roaring at each other to determine who is the strongest. If things do escalate, many species of stags have branched antlers allowing them to wrestle rather than impale each other. Species with straighter antlers will tend not to use them in fights, but will resort to biting and kicking, which is far less dangerous. This strategy of assessing your opponent first and picking your fights carefully is clearly beneficial for the species as a whole, but it wasn’t originally clear why an aggressive strategy (where you kill your opponent and pass on your genes) isn’t more common in the animal kingdom.

Smith and Price sought to examine this and they devised the hawk–dove game. A population (of the same species) is split into two groups: hawks and doves. Hawks are aggressive and will play until they win or are seriously injured. On the other hand, a dove is a pacifist and will surrender if its opponent gets aggressive (so it will never get injured). In this game, two players are matched up and compete for a resource (eg food), and the outcome depends on the strategies they play. These dynamics are shown in the following payoff matrix:

 meets hawk meets dove If hawk $(v-c)/2$ $v$ If dove $0$ $v/2$

Here, $v$ is the value of the resource, while $c$ is the cost of injury (from a hawk losing to another hawk). Typically in nature, we find that $c$ is much larger than $v$. To explain the entries of the payoff matrix: when a hawk meets another hawk, there is a 50% chance it will win, gaining $v$; but a 50% chance it will lose, losing $c$. When a hawk meets a dove, it will always win, gaining $v$, while the dove always loses without injury, receiving 0. When a dove meets another dove, there is a 50% chance it will win, gaining $v$ and, if it loses, it does not get injured but receives 0.

These dynamics can be analysed by the replicator equations. The change in proportion of a strategy $i$ ($x_i$) is given by the fitness of the strategy ($f_i$), minus the average fitness of the population, all multiplied by the proportion of strategy $i$: $\frac{\mathrm{d}x_i}{\mathrm{d}t}=x_i\Big(f_i(\mathbf{x})- \sum_{j=1}^n x_j\hspace{2pt} f_j(\mathbf{x}) \Big).$
The distribution of the population into the $n$ strategies is given by the vector $\mathbf{x}=(x_1,\ldots,x_n)$ which, since they are proportions, has entries summing to 1. Using the values from the payoff matrix above leads to a single differential equation (since $x_2=1-x_1$), with a globally stable steady state where the proportion of hawks is $v/c$, which is closer to 0 than 1. While this can explain the lack of aggressive strategies seen in nature, an extension of this is the hawk–dove–assessor game.

An assessor plays as a hawk if they are stronger than their opponent, and as a dove if they are weaker. This is precisely what we tend to see in ritualistic fights in nature. The payoff matrix is given below, which you can verify yourself.

 meets hawk meets dove meets assessor If hawk $(v-c)/2$ $v$ $(v-c)/2$ If dove $0$ $v/2$ $v/4$ If assessor $v/2$ $3v/4$ $v/2$

We find that the strategy of being an assessor is an evolutionary stable strategy. This means that if the entire population is playing as assessors, then any invasion by another strategy cannot succeed and spread. The assessors will always dominate eventually.

Many different strategies can be represented in game theory, including cooperation, spite, selfishness and altruism. Altruism is when a player does something beneficial to the recipient at a cost to itself. This can be seen in games with a memory or reputation system: if the cost is low but the benefit is high, then an individual may act altruistically in the hope of the other player returning the favour later on, leading to a net benefit for them both. Perhaps the most obvious reason for an altruistic act, though, is if the players are related, meaning that they have genes in common. This stems from the idea of inclusive fitness:\begin{align}\text{inclusive fitness}&=\text{individual fitness}+(\text{relatedness}\times\text{relative’s individual fitness}),\\
\omega_i&=f_i+\sum_{j \neq i} R_{ij}\hspace{2pt} f_j.\end{align}
This means that when examining the dynamics of gene frequencies, the fitness of an individual’s family should also play a role since some of the genes will be shared. Relatedness is defined to be the probability that a gene picked randomly from each individual at the same locus (position) is identical by descent. This works out intuitively: 0.5 between you and a sibling/parent/son/daughter (since each parent gives half of their genes to their offspring), 0.25 between you and an uncle/aunt, 0.125 between you and a cousin. Another name for inclusive fitness theory is selfish gene theory, popularised by Richard Dawkins’ book The Selfish Gene, which was influenced by ideas from fellow biologist George Williams. The term ‘selfish’ refers to how some genes may prioritise their own survival (over many generations) over that of the individual or even species. This gene-centred view of evolution helps explain altruism.

William Donald “Bill” Hamilton was an evolutionary biologist who completed his PhD at the London School of Economics and University College London. He claimed that altruistic acts are favoured (and as a strategy, can spread through the population) if the relatedness between the players is greater than the cost to benefit ratio of the act, ie $R>\frac{C}{B}.$
This became known as Hamilton’s rule and, while it can be hard to quantify and test, a simple example occurs in prairie dogs. When these rodents are above ground and spot a predator, an individual is more likely to sound an alarm call when relatives are close by: an action that is costly as the individual draws attention to itself.

## Colony bee-hive-iour

Atta cephalotes castes (Gamekeeper, CC BY-SA 3.0)

An extreme case of altruism is eusociality:  the highest level of organisation in animal social structure. This structure is what you probably think of when you consider an insect colony: a queen laying eggs and thousands of workers maintaining the nest. Some other traits of this include cooperative brood care and overlapping generations; however, its most distinct trait is the division of labour into castes. These castes are specialised in that individuals from one caste lose the ability to perform the tasks of individuals from another caste. These castes don’t just vary physically, they can express different behaviours and even instincts! In the figure on the previous page, we can see many types of workers on the left, a soldier in the middle and two queens on the right. The soldier is larger than the workers and has a stronger jaw—interestingly this is mainly used for foraging heavy objects and not to defend the nest: that job is up to the workers. Most eusocial animals can be found in the third largest order of insects, called Hymenoptera, which includes bees, wasps and ants.

Hymenopterans are also haplodiploids, ie males have one set of chromosomes (haploids) and females have the usual two (diploids). This bizarre fact means that males actually hatch from unfertilised eggs and females from fertilised eggs. Some strange relationships can come from this. For example, males have no father or sons, but have a grandfather and can have grandsons! But the most intriguing fact is that the workers (who are all female and make up the vast majority of a colony) help raise new brothers and sisters produced by the queen instead of having their own offspring. This fact puzzled Charles Darwin, as it wasn’t clear how a trait that leads to an individual not reproducing and passing on their own genes can be so prevalent in a population. This would mean that their individual fitness is zero; however, going back to inclusive fitness theory, we can see that they can still benefit from their relatives. In fact, we’ll see that this benefit outweighs that of having their own offspring.

Fig. 1: Family tree of haplodiploid insects

A mated queen can produce fertilised and unfertilised eggs for the rest of her life. From the figure to the left, we can see that female workers are more related to their sisters than any other relative (including possible offspring). Explicitly, two female workers share exactly the same set of blue chromosomes (from their father), which is already a relatedness of $0.5$. The other set (in red) can either be exactly the same or different, depending on which was inherited from the queen, giving on average another $0.25$, and making their relatedness $R=0.75$. Following Hamilton’s rule, it indeed makes sense to help raise new sisters instead of offspring, who only have a relatedness of $0.5$. In most species of eusocial Hymenoptera, the queen is aggressive and releases pheromones to discourage workers from laying eggs and, in some cases, from even ovulating. However, it seems that not all workers are happy with this arrangement, and this can lead to several different types of conflict.

## Conflict: sex allocation

The queen is equally related to male and female offspring (with a ratio of 1:1) but female workers prefer sisters to brothers (with a ratio of 3:1). Consequently, it is common for the female workers to eat or kill male larvae so that the colony’s resources are not used in raising them. This means that the queen has wasted energy producing and laying the male eggs. A common solution to this is to produce a brood of all the same sex, reducing the motivation for the males to be destroyed. In reality, the ratio is between the two ideals for the queen and female workers. For most of these species, males (AKA drones) do not help with work in the colony: their only role is to mate with a young queen—so, apart from the genetics, female workers may benefit even less from having brothers. For the queen, however, males are still important to help spread her genes even further: if the males are able to mate with a new queen who then starts her own colony, all of the new workers will carry one set of chromosomes from the original queen. In some species, it has been noticed that the queen will lay batches of male eggs when food supplies are low, as female workers’ fitnesses are more affected by food during their developmental stages. However, since males don’t even help with foraging for food, this strategy would only work in the short term.

## Conflict: male rearing

Apis mellifera: queen and workers (Jessica Lawrence, CC BY 3.0)

In honey bees, 7% of male eggs are from workers but only 0.1% of adult males are a worker’s son. Why would workers lay eggs in the first place? Well, they are more related to their own offspring ($R=0.5$) than to any brothers ($R=0.25$) that the queen produces. However, laying workers are less hard-working and the queen would rather spend resources raising new children than grandchildren (to whom she is less related), so she tries to prevent this using pheromones. These pheromones inhibit the workers’ ability to lay eggs but are less effective in large colonies, especially if the queen is old.

Worker policing occurs through workers destroying other worker-laid eggs. We can calculate from the family tree (Fig. 1) that workers are more related to new sisters ($R=0.75$) than any nephews ($R=0.33$). The calculation for the second relation works as follows: comparing a female worker to her nephew, there are three possible cases involving the three different sets of chromosomes in the family tree (the blue set of chromosomes from the queen’s mate and the two red sets of chromosomes from the queen herself). One case is where the nephew has the blue set of chromosomes, which his aunt will also have, meaning she shares 50% of her genes with him. If the nephew has a red set of chromosomes, they can either be the same set as his aunt’s (again, 50% of her genes will be shared), or they can be the ‘other’ red set originally from the queen (so his aunt will share none of her genes in this case). Thus on average, a worker has a relatedness of $R=(0.5+0.5+0)/3$ to her nephew. Workers can determine whom the eggs belong to through chemical markers. You may think that there is a selection pressure (in evolution) towards workers who can lay eggs that mimic the queen’s… and you’d be right! These are called ‘anarchic workers’ and in the Cape honey bee, this trait is affected by multiple genes; but, for various reasons, the cost of having these altered genes outweighs their benefit. Note that this conflict does not appear in every eusocial hymenopteran: in some species, the castes are so specialised that workers aren’t even capable of reproducing in the first place.

## Conflict: caste fate

Apis mellifera pupae (Waugsberg, CC BY-SA 3.0)

Why wouldn’t a worker choose to develop as a queen instead? Doing so would be selfish but it yields a much greater inclusive fitness. In many species, the determining factor as to whether a female larva develops into a queen or worker is the amount and type of food it receives. Too many queens could cause the colony to run inefficiently or break down, so to prevent this, some species raise their larvae in space-restricted cells where the food supply is controlled by the workers. This generally prevents the larvae from developing into queens. In species where this does not occur, excess queens are killed immediately after emerging.

A queen can actually mate with several males to produce offspring that may not be full siblings. A higher relatedness between workers, however, can reduce the incentive for such selfish acts. Focusing only on the genetics, an already-developed worker would benefit more from raising new sister workers than raising a new sister queen, as this could lead to the raising of nephews/nieces.

## Conflict: matricide

There are three parties in favour of the queen’s death: laying workers, non-laying workers and, surprisingly, the queen herself. Going back to Hamilton’s rule, a model was developed by Bourke quantifying the cost–benefit ratio of the queen’s death. These are based on multiple variables including the number of offspring a laying worker can have between the queen’s hypothetical death and the death of the colony. The colony wouldn’t survive long without a queen, and raising new queens isn’t always successful due to the risky mating flight, where a virgin queen flies away from the safety of the nest to mate with males. Bourke’s model applies particularly well to annual colonies where eggs are laid together once a year, after which the colony members all die (apart from young mated queens).

After ordering the cost–benefit ratios of the queen’s death, it was found that laying workers have the most to gain, followed by non-laying workers, and then the queen herself. This is intuitive as, without the queen, laying workers can raise their own offspring. As we have seen in Fig. 1, this shouldn’t happen in the case where the queen has only mated once. However, if she has mated multiple times, the new workers may not be full sisters with the previous workers, and the relatedness would be less than $0.75$. Additionally, if the queen is old and laying fewer eggs (or unfertilised eggs due to food shortages), the benefit of a worker laying their own eggs increases. Once laying workers start attacking the queen, it creates a positive feedback loop: when the queen is injured, she will produce fewer eggs and the critical ratio for the non-laying workers can be surpassed, meaning that they start attacking the queen too. When the queen is seriously injured and no longer able to lay many eggs at all, she would gain a higher inclusive fitness from allowing the workers to lay eggs, since they would all be her grandsons. Thus, when the queen’s own critical ratio is met, she allows herself to be killed by her workers.

Female naked mole rat (Jedimentat44, CC BY 2.0)

Surprisingly, eusociality is also observed in mammals: specifically, two species of mole rat. The naked mole rat is an odd species, famous for its hairless appearance and high resistance to cancer. In captivity, they can live up to 31 years — an astonishing amount of time for a rodent! They live in underground nests under the rule of an aggressive queen who releases pheromones to discourage the workers from reproducing. Unlike the insects, the queen naked mole rat wasn’t born into her position, she had to fight for it! Her reign is also unstable: she will have to defend her crown from female workers. Mole rats (along with almost all mammals) are diploids, so at first glance, it seems that they are just as related to their offspring as to their siblings. However, naked mole rats are infamous for high rates of inbreeding, causing a high level of relatedness between the workers. A few of the males will have the role of mating with the queen, while the others function as workers. It still isn’t certain whether a high level of relatedness is required for eusociality to evolve, or whether it is a consequence of it, but clearly the two are linked.

Does altruism, the act of helping others at a cost to yourself, truly exist in the animal world? In the case of eusocial insects, sacrificing your own fertility to raise the queen’s eggs seems like a noble gesture, but we have seen that the workers only do this in order to pass on more of their genes. Once this benefit decreases, conflict occurs and the workers will continue to pursue their own optimal strategy — killing the queen to lay their own eggs. Can any of this theory be applied to humans? The majority of interactions occur between ‘unrelated’ individuals. For social dynamics, game theory would explain altruistic acts in terms of good karma, expecting others to return the favour later on (which is risky). Perhaps having this expectation can be selfish; however, a common assumption in game theory is that players act ‘rationally’ in the sense that they always try to maximise their payoff. I’d like to believe, though, that people don’t actually think like this and that acts of uncalculated kindness in humans occur more often than the theory would suggest.

[Banner image: Todd Huffman, CC BY 2.0;]

# The evolutionary arms race

When discussing evolutionary arms races, it is common to think of predator-prey interactions. Over many generations, the predator tries to develop a better offence, and the prey a better defence. These selection pressures, however, can cause species to evolve some extreme traits. One drop of venom from the marbled cone sea snail is potent enough to kill over 20 humans, even though they don’t interact with species of our size. We see animals with such strong venom in nature because its prey (or predators) have a selection pressure for stronger resistance to this venom—leading to an arms race. Producing such strong venom is costly, but it clearly seems to have a net benefit for these species.

How about interactions within a species? A good example here would be peacocks. Males have a selection pressure for longer and elaborate feathers used to attract mates, however this makes them more vulnerable to predation as it is easier to be seen and get caught. Again, in this case the benefits seem to outweigh the costs—but how can we analyse the maths behind arms races such as these?

First we’ll look at a general example in evolutionary game theory (EGT). In usual game theory, you want to examine how the frequency of strategies played change in the population over time. A strategy determines how the player behaves during the game and affects the outcome of all players in the game. The main difference with EGT is that these strategies are inherited and cannot be changed by the players. This table shows the pay-off matrix of a 2 player game with 2 strategies, $S_1$ and $S_2$:

 meets $S_1$ meets $S_2$ If $S_1$ $a$ $b$ If $S_2$ $c$ $d$

So all interactions involve 2 players and, for example, if an $S_1$ player meets an $S_2$ player, the $S_1$ would get a pay-off of $b$, whilst the $S_2$ gets $c$. In EGT these pay-offs are associated with fitness (in the evolutionary sense). The dynamics of the population can be analysed using the replicator equation to produce phase planes, however we’ll just focus on information we can directly get from the matrix.

An important concept is finding an evolutionary stable strategy (ESS) – this is a strategy that, when played by the whole population, cannot be invaded by an alternate strategy that is initially rare. So an ESS is robust against invaders and the population will remain playing this strategy as time goes to infinity.

Mathematically, $S_1$ is an ESS if:
i) $a>c$. This means any invaders ($S_2$) will have a lower average fitness than the current population as everyone will be mostly playing against $S_1$, so $S_1$ remains dominant after a few generations.

ii) $a=c$ and $b>d$. In this case, when playing against $S_1$ , everyone gets the same pay-off. So additionally we would want $S_1$ to perform better against $S_2$ than $S_2$ does with itself, for $S_1$ to remain the dominant strategy.

The conditions for when $S_2$ is an ESS are found using the same reasoning [ i) $d>b$,  ii) $d=b$ and $c>a$].

Now we can finally look at an example! Suppose we have a population of beetles with two strategies, small and large – based on their size. The ‘game’ they play involves competing for a fixed amount of food. Intuitively, the large beetle will beat the smaller one in this game, however large beetles require more sustenance, so relatively the food from the game is worth less. Here are some numbers reflecting this:

 meets small meets large If small 5 1 If large 8 3

A large beetle.

From our conditions above, we find that the large strategy is an ESS and the small strategy is not an ESS. What does this mean for our beetles? If we have a population of small beetles and introduce a large one (either by mutation or migration), then this large beetle will outcompete the rest of the population and pass on more genes. As the generations go on, this population will all end up being large. Since the large strategy is an ESS, it cannot be invaded by small beetles, so the whole population will remain large.

This actually has an interesting consequence. When the population was all small, everyone had a fitness of 5. On the other hand when the whole population becomes large, everyone has a lower fitness of 3, even though this was selected for by evolution. This does not go against Darwin’s survival of the fittest as the introduction of large beetles is a change in the environment and they outcompete the smaller ones. In fact you can check that in any mixed population, the large beetles have a greater average fitness than the small beetles. Note that generally in 2 strategy games, it is possible to have other outcomes such as a mixed population being stable instead of a homogeneous population as above.

Of course, there is a balance to all of this; beetles that are too large to sustain themselves would be selected against in evolution. Variable environmental factors can also affect the optimal size of these beetles. Insects respire via diffusion, which is more inefficient the larger you are – or more accurately, the smaller your surface area to volume ratio is. This is believed to be the main limiting factor for insect size. Back in the prehistoric days, insects were gigantic compared to the ones we see today – the levels of oxygen in the air were also much higher allowing them to still respire effectively despite their size. With these factors in mind, it seems like I don’t we don’t need to worry about insects getting larger!

A mimic octopus can resemble venomous lionfish, sea snakes and more!

In nature, there are also strategies to avoid these arms races between predators and prey. Poisonous or venomous prey tend to have bright coloured markings to indicate their toxicity to possible predators.  However, there are also examples of non-toxic animals which mimic the colouring of these prey if they share a common predator. This gives them the benefit of deterring the predators, whilst not having the cost of producing the toxins. This is referred to as Batesian mimicry and can also take the form of producing the same sounds, odours or even behaviours. Whilst this sounds like the best strategy, it is only effective when the mimics are much fewer in number than the ‘model’ prey, otherwise the predator wouldn’t associate these markings with danger (or in some cases, just foul taste!).

[ Pictures: Banner: Wikimedia Commons user Servophbabu, CC BY-SA 3.0; Beetle: Wikimedia Commons user Bojars, CC BY-SA 3.0; Octopus: Flickr Steve Childs, CC BY 2.0]