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- Hello.

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I am Dr. Nelson Scottgale,

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a member of the Darwin Festival Committee.

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Welcome to the third
day of the 42nd Annual

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Darwin Festival at Salem State University.

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I would like to now
turn the microphone over

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to Dr. Juditha Burchsted
for her introduction.

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Thank you very much.

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- Good afternoon, and welcome
to the Founders' Lecture

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of the 42nd Annual Darwin Festival

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at Salem State University.

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Our founders' lecture honors

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Philip DePalma and Virginia Keval,

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two professors at Salem State,

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who 41 years ago found a way

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to bring the evolutionary
foundation of modern biology

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to their students in their
human and social biology course.

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The fact that the annual Darwin
Festival not only continues

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at Salem State, but serves as
a model for a growing number

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of commemorations throughout the world

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is a testament to their farsightedness,

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as well as their ability to inspire

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their students and colleagues.

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Sadly, Phil DePalma is no longer with us,

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but we are very honored by the presence

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and continued support
of our emerita professor

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Virginia Keval who organized
the Darwin Festival

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for 20 years and joins
us in our webinar today.

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Also joining us remotely
is emerita professor

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Susan Case, who spoke at
the inaugural festival,

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before joining the Biology Department,

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and organized the Darwin Festival

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after professor Keval's retirement.

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Ginger and Sue,

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your shared commitment
to the Darwin Festival

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inspires us all.

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Thank you so much.

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Today, I am very pleased to welcome

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as our founders' lecture
speaker, evolutionary biologist,

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Maydianne Andrade.

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Dr. Andrade is a Professor

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in the Department of Biological Sciences

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at the University of Toronto Scarborough,

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and in the Graduate Department

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of Ecology and Evolutionary Biology

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at the University of Toronto.

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She earned her PhD in
neurobiology and behavior

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from Cornell University
investigating the evolution

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of cannibalistic mating behavior

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of black widow spiders,

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research she continues in
both lab and field work.

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She has received numerous
teaching and research awards

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including a prestigious
Canadian research chair

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in integrative behavioral ecology.

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Dr. Andrade was elected a Fellow

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of the Animal Behavior Society,

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and the American Academy
of Arts and Sciences.

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The Darwin Festival is very
pleased to give a warm welcome

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to Dr. Maydianne Andrade
for our founder's lecture,

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entitled Through a Web, Darkly:

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Sex, Death, and Adaptation
in Widow Spiders.

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- Thank you so much for
that kind introduction,

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and for inviting me here
today, it's an honor.

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This really is one of the
longest Darwin Festivals.

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And as you'll see, Darwin
inspired my entry into science,

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and I wanna talk to you a little bit about

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where that started and where I'm going.

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So in one of the memes
of 2020, how it started.

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It started with a fascination
as an undergraduate

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with sexual cannibalism.

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And at least initially it
was in terms of cannibalism

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in praying mantis, which
you see here on the left,

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and now on the right,

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a video of mantis by Dr.
William Brown and Phil Hastings

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at SUNY Fredonia.

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Now sexual cannibalism is fascinating.

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It has fascinated scientists
for quite a long time.

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This quote being from 1886,

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describing what happens after
a male and female mantis

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were left together in a lab.

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"She had completely rid
herself of her spouse

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"by the morning after
the time they were paired

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"and nothing but his wings remained."

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Now, why did this deliver
entree to evolutionary biology?

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Well it was puzzling, and it
was puzzling not just because

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males were regularly killed

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by potential partners during mating,

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but also because there's
actually a ganglion,

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a cluster of nerve cells in
the male's thorax, upper body.

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And when the female
begins to eat the male,

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typically from the head down,

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that ganglion is removed,

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and actually results in more
vigorous copulatory behavior

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than if it had been intact.

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That is, people speculated,

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that males were actually
adapted for being killed.

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That idea was one that had some legs

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for a while in the popular literature,

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but it wasn't until the 1980s

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that Ruth Buskirk and her colleagues

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asked whether mathematically,

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whether in terms of our actual
theoretical understanding

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of evolutionary biology,
something like this could evolve.

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And they wrote a paper in the 80s,

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called the Natural Selection
of Sexual Cannibalism

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that proposed and showed
quantitative theoretical arguments

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to support the idea that
you could actually get

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the evolution of males,

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that not only just didn't
manage to avoid being killed,

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but actually had become adapted

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to allowing females to
kill them during mating.

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And that idea is one
that gets a heart for me

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of part of what makes evolutionary
biology so interesting,

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not the sexual cannibalism piece,

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but the idea that things that to us

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as human beings are counter-intuitive,

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could be predicted by
evolutionary biology.

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And we can go out and test and see

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whether they actually occur in nature.

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Now, I read this paper
as an undergraduate,

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and was really impressed by the fact

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that in the end, the mathematical argument

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came down to this simple
one that you see here.

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And that is that you
could have the evolution

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of such a counter-intuitive
mating strategy,

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if the expected number of
matings in nature for males

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who aren't cannibalized was fairly low,

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and if males who were cannibalized

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had an increase in the number of offspring

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that they had as a result.

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So the balance then shift in favor of

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you're better off mating once
and getting more offspring,

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rather than having a very small chance

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of having a chance to mate again, anyway.

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Now, shortly after this paper came out,

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it was addressed in a natural history

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of popular science article
by Stephen Jay Gould.

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And for those of you who don't
know of Stephen Jay Gould,

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he passed away in the early
2000s, but he was and is

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an extremely influential
evolutionary biologist.

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Not just because of his
contributions to theory,

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but because he also engaged
heavily with the public.

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He wrote essays in natural
history magazines, 300 of them,

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over the course of his career,

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and he was also a
prolific writer of books.

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And you see some of his books here.

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The wonderful Life,

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the book on the upper left
is the one that got me hooked

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on evolution to begin with.

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It's an amazing book if
you've never read it.

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And a lot of my entree

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to understanding evolutionary reasoning

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came from Stephen Jay Gould.

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He even tackled issues
around scientific racism

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in the Mismeasure of Man.

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Anyway, to get back to
cannibalism, Stephen Jay Gould,

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this foundational figure for
me in evolutionary biology

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was not exactly what I
would call a Darwinist.

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So the idea that I just outlined,

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that you could sort of
mathematically look at it and say,

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"This is counterintuitive,

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"but it makes sense in terms
of reproductive success.",

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that is at the heart of Darwin's idea

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about how evolution worked.

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But Stephen Jay Gould
often quarried whether

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that sort of an idea was really
going to play out in nature,

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or whether evolutionary forces
that slow down adaptation

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were more likely to be important.

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And in the case of this particular paper,

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proposing natural selection
for sexual cannibalism,

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he said, "Do males for the sake
of their genetic continuity

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"actively elicit or even passively submit

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"to the care and feeding
of their fertilized eggs

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"with their own bodies?

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"I find little persuasive
evidence for such a phenomenon.

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"And I wonder if it exists at all."

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I show here diagrams of two of the species

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in which cannibalism
was fairly well-known,

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praying mantis, but also
black widow spiders,

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which I ended up working on.

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To continue with his quote, he says,

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"The argument would provide
an excellent illustration

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"of a curiosity that makes little sense

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"unless the evolutionary world

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"works for reproductive
success of individuals,

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"as Darwinism argues."

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So, the gauntlet is down,

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and this, I wanna say, is
where I started in my career.

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I'll tell you quickly about
the answer to this question,

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and move on to tell you
where an understanding

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of how this works has led
to other types of insights.

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So when I started working on questions

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to do with sexual cannibalism,

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that was at the beginning of my career,

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I focused on black widow spiders.

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And most people are aware
of one or two black widows,

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types of black widows,
depending on where you live.

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But I just wanna point
out that black widows are

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a group of about 30 different species.

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They are distributed worldwide.

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And if you look at this sort
of very truncated phylogeny

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or family history that's being worked on

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by my student Charmaine Condi,

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you can see that they are distributed,

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that their phylogeny sort
of maps onto biogeography.

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And I just wanna point out
that a couple of the species

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I'll be talking about in depth today,

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Latrodectus hasselti,
or the red back spider,

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and Latrodectus hesperus,
or the Western black widow,

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are found in Australia and North America.

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But there's others that we raise
in the lab for experiments.

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So you need to to know
a bit about their mating

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to understand how we can
go about testing the idea

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about sexual cannibalism.

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And spiders mate in a
way that are different

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from most other creatures.

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And that is that males'
copulatory organs are paired,

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and they're located essentially anteriorly

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near the front of their
head or cephalothorax.

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So this is a closeup of
these two structures,

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which becomes important later.

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These two copulatory organs inseminate

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two separate sperm
storage organs in females.

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So the females' genital
opening is located about here

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on their underside,

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the male, each one of
his palps would insert

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into one of these sperm storage organs,

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and the female can store sperm

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for up to two years after mating,

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and reproduce successfully
throughout that time.

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Now for most species in this
genus, they mate like this,

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these are the black widow mating posture,

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a male will climb on the female's abdomen,

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males are much smaller than females.

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They'll insert one of those palps

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the coil that you saw into
the female's genital opening,

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and then they'll sort of
mate kind of belly to belly.

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And this is what we see in Western widows.

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But in red back spiders,
and in brown widow spiders,

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one other species in the genus,
they do something different.

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They start out in the same posture,

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but shortly after the
commencement of copulation,

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the male moves into a headstand posture,

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which you're seeing in the video now.

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And then he flips over completely,

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and he lands with his
abdomen with his body

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over the female's fangs.

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The female will begin to eat him

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while he's copulating with her,

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and he's able to transfer sperm
as he's being cannibalized.

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Now, these look, for all
intents and purposes,

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like males who are adapted

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to letting themselves be cannibalized,

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the male actively moves into this posture,

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other species in the genus don't do it,

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00:11:59,240 --> 00:12:02,690
and about 65% of matings
end with a male being killed

257
00:12:02,690 --> 00:12:04,020
by the female.

258
00:12:04,020 --> 00:12:06,990
So these males seem to be
single mating specialists.

259
00:12:06,990 --> 00:12:08,630
This is not random or happenstance.

260
00:12:08,630 --> 00:12:10,890
This is not a male who didn't
get away from the female.

261
00:12:10,890 --> 00:12:12,980
This is a male with a
behavior that makes it

262
00:12:12,980 --> 00:12:14,533
more likely this will occur.

263
00:12:15,420 --> 00:12:17,860
And so if we go back
to this elegant model,

264
00:12:17,860 --> 00:12:19,460
and the Buskirk et al paper,

265
00:12:19,460 --> 00:12:21,270
and we think about these
two different things

266
00:12:21,270 --> 00:12:24,590
that might lead to the evolution
of a behavior like this,

267
00:12:24,590 --> 00:12:26,860
it turns out that in terms
of increase the number

268
00:12:26,860 --> 00:12:29,060
of offspring with cannibalism,

269
00:12:29,060 --> 00:12:30,030
it happens in a slightly

270
00:12:30,030 --> 00:12:31,330
different way than they imagined,

271
00:12:31,330 --> 00:12:35,460
and in a great paper by Kate
Barry and colleagues at WASO,

272
00:12:36,960 --> 00:12:39,620
they found that actually,
when a male is cannibalized,

273
00:12:39,620 --> 00:12:42,460
his offspring have increased
survival and growth

274
00:12:42,460 --> 00:12:43,840
relative to the offspring of male

275
00:12:43,840 --> 00:12:45,080
who's not cannibalized.

276
00:12:45,080 --> 00:12:46,020
So in other words,

277
00:12:46,020 --> 00:12:48,320
the number of surviving
offspring would go up.

278
00:12:50,160 --> 00:12:51,060
In my lab group,

279
00:12:51,060 --> 00:12:54,500
we looked at the other side
of this and that is the,

280
00:12:54,500 --> 00:12:56,260
sorry, and that there's another way

281
00:12:56,260 --> 00:12:58,800
in which you can increase your fitness.

282
00:12:58,800 --> 00:13:02,400
And that is that the number
of offspring fathered

283
00:13:02,400 --> 00:13:04,210
by a male when he's in competition

284
00:13:04,210 --> 00:13:07,070
with other males might also
increase with cannibalism.

285
00:13:07,070 --> 00:13:08,750
And that's actually what we found.

286
00:13:08,750 --> 00:13:12,510
Males who are killed by females
actually copulate longer

287
00:13:12,510 --> 00:13:15,670
while the females eating
them, transfer more sperm,

288
00:13:15,670 --> 00:13:16,870
and when they're in competition

289
00:13:16,870 --> 00:13:19,060
with other males that have
mated with that female,

290
00:13:19,060 --> 00:13:21,133
they will father most of the offspring.

291
00:13:23,190 --> 00:13:25,410
So we, thinking about this balance, then,

292
00:13:25,410 --> 00:13:26,700
whether you'd have the evolution

293
00:13:26,700 --> 00:13:29,460
of a self-sacrificial mating behavior,

294
00:13:29,460 --> 00:13:31,480
you then have more surviving offspring

295
00:13:31,480 --> 00:13:33,440
weighing down on the side of

296
00:13:33,440 --> 00:13:36,430
essentially males letting
themselves be killed.

297
00:13:36,430 --> 00:13:38,530
But in a series of studies, we also found,

298
00:13:38,530 --> 00:13:39,820
that the balance of that,

299
00:13:39,820 --> 00:13:41,580
the expected number of matings in nature

300
00:13:41,580 --> 00:13:45,220
is actually quite low
because most males die

301
00:13:45,220 --> 00:13:48,560
without ever finding a
female in red back spiders.

302
00:13:48,560 --> 00:13:51,010
So they just have a very arduous trip

303
00:13:51,010 --> 00:13:52,440
to find females in nature.

304
00:13:52,440 --> 00:13:55,393
They're often killed by
predators or die of starvation.

305
00:13:57,050 --> 00:13:57,883
And in fact,

306
00:13:57,883 --> 00:13:58,716
we found that that's true

307
00:13:58,716 --> 00:14:00,430
for other Latrodectus species as well.

308
00:14:00,430 --> 00:14:02,260
So in other studies in my lab,

309
00:14:02,260 --> 00:14:03,630
and the studies in Israel,

310
00:14:03,630 --> 00:14:05,760
they found that other Latrodectus spiders

311
00:14:05,760 --> 00:14:06,860
have the same pattern.

312
00:14:06,860 --> 00:14:08,390
Males have to search for females

313
00:14:08,390 --> 00:14:10,320
through a challenging environment,

314
00:14:10,320 --> 00:14:12,930
and during that mate searching period,

315
00:14:12,930 --> 00:14:14,680
their likelihood of success is low.

316
00:14:15,930 --> 00:14:19,140
But only red backs and brown
widows show this balance,

317
00:14:19,140 --> 00:14:20,350
because you have to have both

318
00:14:20,350 --> 00:14:22,330
that there's few mating opportunities

319
00:14:22,330 --> 00:14:25,080
and that you have a benefit
from being cannibalized,

320
00:14:25,080 --> 00:14:27,070
in this case, more surviving offspring

321
00:14:27,070 --> 00:14:30,090
to end up with self-sacrificial males.

322
00:14:30,090 --> 00:14:32,830
Nevertheless, this point about
high mortality is important

323
00:14:32,830 --> 00:14:34,810
to the what I'm doing now story,

324
00:14:34,810 --> 00:14:36,310
so I'll come back to it later.

325
00:14:38,030 --> 00:14:40,660
Okay, so back to Stephen Jay Gould then,

326
00:14:40,660 --> 00:14:42,680
despite the fact that he debated

327
00:14:42,680 --> 00:14:45,980
whether this type of Darwinian
thinking was important

328
00:14:45,980 --> 00:14:47,020
to our understanding of nature,

329
00:14:47,020 --> 00:14:49,230
I would say that in this case,

330
00:14:49,230 --> 00:14:51,300
the evolutionary world does seem to work

331
00:14:51,300 --> 00:14:53,160
for the reproductive
success of individuals

332
00:14:53,160 --> 00:14:54,680
as Darwinism argues.

333
00:14:54,680 --> 00:14:56,040
And it's even more compelling

334
00:14:56,040 --> 00:14:57,260
than if it was something

335
00:14:57,260 --> 00:14:59,240
that just made sense to
us intuitively, right?

336
00:14:59,240 --> 00:15:01,440
Here's a case where we might
not have predicted this

337
00:15:01,440 --> 00:15:03,220
unless we actually worked it out

338
00:15:03,220 --> 00:15:05,620
with theory and then took
a look at the numbers.

339
00:15:07,460 --> 00:15:10,020
So since then we found that
there's many other species

340
00:15:10,020 --> 00:15:12,500
that also show these kinds of
terminal investment patterns.

341
00:15:12,500 --> 00:15:14,280
This is not a one-off in nature,

342
00:15:14,280 --> 00:15:18,560
although it does seem to be
concentrated among spiders.

343
00:15:18,560 --> 00:15:21,880
And we find there's common
traits of these types of males.

344
00:15:21,880 --> 00:15:23,380
They have very high investment

345
00:15:23,380 --> 00:15:25,480
in a limited number of mating.

346
00:15:25,480 --> 00:15:28,630
And quite often they
undergo genital damage

347
00:15:28,630 --> 00:15:30,300
or mutilation during those matings,

348
00:15:30,300 --> 00:15:32,250
and I'll tell you about that later,

349
00:15:32,250 --> 00:15:33,480
they're capital breeders,

350
00:15:33,480 --> 00:15:36,140
which means that they
eat only as juveniles,

351
00:15:36,140 --> 00:15:37,710
and they have to accomplish that mating

352
00:15:37,710 --> 00:15:39,560
using only the energy they accumulated

353
00:15:39,560 --> 00:15:42,530
as before they become sexually mature.

354
00:15:42,530 --> 00:15:45,150
Even if they're not cannibalized,
they have short lifespans,

355
00:15:45,150 --> 00:15:48,910
and they're all subject to
intense sperm competition.

356
00:15:48,910 --> 00:15:51,920
And sperm competition again is
when more than one male mates

357
00:15:51,920 --> 00:15:54,760
with a female, especially
in a species like spiders,

358
00:15:54,760 --> 00:15:57,630
where they store the sperm,
the sperm can be in competition

359
00:15:57,630 --> 00:16:00,860
with each other inside the
female's body for access to eggs.

360
00:16:00,860 --> 00:16:03,150
And we showed that cannibalism
gives males an edge

361
00:16:03,150 --> 00:16:04,223
in that competition.

362
00:16:06,970 --> 00:16:09,310
Okay, so where am I now?

363
00:16:09,310 --> 00:16:11,330
Started my career with sexual cannibalism

364
00:16:11,330 --> 00:16:14,050
and with being drawn into this debate,

365
00:16:14,050 --> 00:16:16,680
and then once I began to
learn more about black widows,

366
00:16:16,680 --> 00:16:19,660
I began to see them as
more than just a curiosity,

367
00:16:19,660 --> 00:16:21,530
but instead, a system
that it could allow us

368
00:16:21,530 --> 00:16:23,120
to ask some really interesting questions,

369
00:16:23,120 --> 00:16:25,540
because of unique
features of their biology,

370
00:16:25,540 --> 00:16:28,150
or I should say extreme
features of their biology.

371
00:16:28,150 --> 00:16:30,970
So essentially I was
invoking the Krogh Principle,

372
00:16:30,970 --> 00:16:33,750
and recognizing that for a
large number of problems,

373
00:16:33,750 --> 00:16:35,330
there'll be some animal of choice

374
00:16:35,330 --> 00:16:37,570
on which it can be most
conveniently studied.

375
00:16:37,570 --> 00:16:39,820
In other words, it is not always the case

376
00:16:39,820 --> 00:16:42,280
that the typical laboratory
models are the best

377
00:16:42,280 --> 00:16:43,930
for studying every topic.

378
00:16:43,930 --> 00:16:46,020
Even though sometimes
we're tempted to do that.

379
00:16:46,020 --> 00:16:48,430
Sometimes there's reasons
why a slightly more

380
00:16:48,430 --> 00:16:50,650
unusual model gives you an advantage

381
00:16:50,650 --> 00:16:53,810
and asking a particular
question and answering it.

382
00:16:53,810 --> 00:16:55,450
And the question that I was interested in,

383
00:16:55,450 --> 00:16:59,150
how it's going these days
is adaptive plasticity.

384
00:16:59,150 --> 00:17:01,810
So let me shift and talk a
bit about adaptive plasticity,

385
00:17:01,810 --> 00:17:04,580
and I'm gonna show you how
the information we have so far

386
00:17:04,580 --> 00:17:07,580
has brought us, it allowed
us to answer a question

387
00:17:07,580 --> 00:17:10,540
that was pressing and where
we might go from here.

388
00:17:10,540 --> 00:17:13,450
So this shows you an example
of adaptive plasticity.

389
00:17:13,450 --> 00:17:17,810
These are two Daphnia, they
are genetically identical.

390
00:17:17,810 --> 00:17:19,310
They are clones of each other.

391
00:17:20,260 --> 00:17:23,610
The one on the left developed in a pond

392
00:17:23,610 --> 00:17:26,090
in which there were cues
of predators present.

393
00:17:26,090 --> 00:17:28,500
The one on the right did not.

394
00:17:28,500 --> 00:17:32,250
What happens is that when
these are animals are juveniles

395
00:17:32,250 --> 00:17:33,943
if they detect cues of predators,

396
00:17:33,943 --> 00:17:36,570
in this case, chemical cues of predators,

397
00:17:36,570 --> 00:17:39,980
they pour more investment
into defensive structures.

398
00:17:39,980 --> 00:17:41,520
And those defensive structures make them

399
00:17:41,520 --> 00:17:43,680
more likely to survive in the environment

400
00:17:43,680 --> 00:17:45,450
in which they live.

401
00:17:45,450 --> 00:17:48,810
The animal on the right not
having detected predators

402
00:17:48,810 --> 00:17:50,170
is likely to be maturing

403
00:17:50,170 --> 00:17:51,940
in an environment where
there are no predators

404
00:17:51,940 --> 00:17:54,550
and use the resources
that would've gone into

405
00:17:54,550 --> 00:17:57,090
building those defensive
structures instead for other things

406
00:17:57,090 --> 00:17:58,670
like perhaps gonads, for example,

407
00:17:58,670 --> 00:18:01,050
so they can reproduce at a higher rate.

408
00:18:01,050 --> 00:18:02,970
So there's a mechanistic switch

409
00:18:02,970 --> 00:18:04,790
that allows animals to adapt

410
00:18:04,790 --> 00:18:07,680
to variation in their environment.

411
00:18:07,680 --> 00:18:11,010
And that process is one that
is generally interesting,

412
00:18:11,010 --> 00:18:12,340
and for me, very interesting,

413
00:18:12,340 --> 00:18:14,610
with respect to the models that I'm using.

414
00:18:14,610 --> 00:18:17,450
And the reason why I'm
interested in plasticity

415
00:18:17,450 --> 00:18:19,410
is to try to understand what happens

416
00:18:19,410 --> 00:18:22,190
when you have an organism that is an adult

417
00:18:22,190 --> 00:18:24,500
that has a fixed set of traits,

418
00:18:24,500 --> 00:18:27,800
and has to accomplish something

419
00:18:27,800 --> 00:18:29,770
in the environment in order
to accrue fitness, right?

420
00:18:29,770 --> 00:18:32,470
So the Darwinian currency of evolution.

421
00:18:32,470 --> 00:18:34,550
So let's say for example, in this context,

422
00:18:34,550 --> 00:18:36,360
a male that's maturing in an environment

423
00:18:36,360 --> 00:18:38,360
with a lot of females around,

424
00:18:38,360 --> 00:18:40,750
this relatively large
male has high fitness

425
00:18:42,140 --> 00:18:43,570
has relatively, sorry,

426
00:18:43,570 --> 00:18:44,850
low fitness, it's on the low end

427
00:18:44,850 --> 00:18:46,150
of the fitness distribution here,

428
00:18:46,150 --> 00:18:47,850
for reasons I'll talk about later.

429
00:18:49,180 --> 00:18:52,210
But in a different context,
perhaps when females are rare,

430
00:18:52,210 --> 00:18:54,993
that same big male may have high fitness.

431
00:18:55,990 --> 00:18:57,920
So males have a set of tools,

432
00:18:57,920 --> 00:18:59,590
their life history and their physiology

433
00:18:59,590 --> 00:19:02,560
and morphology equips them
in a certain way as an adult,

434
00:19:02,560 --> 00:19:06,780
and we know from earlier work
and from many decades of work

435
00:19:06,780 --> 00:19:10,000
that behavior allows
males to take those tools

436
00:19:10,000 --> 00:19:12,250
and adjust to short-term changes

437
00:19:12,250 --> 00:19:13,500
in their environment, right?

438
00:19:13,500 --> 00:19:14,580
So the male may be large,

439
00:19:14,580 --> 00:19:16,950
but he may have strategies
or behavioral tactics

440
00:19:16,950 --> 00:19:19,150
to allow him to increase fitness,

441
00:19:19,150 --> 00:19:22,620
even if he's not exactly
matched to that environment.

442
00:19:22,620 --> 00:19:25,410
What I'm really interested
in with more recent work

443
00:19:25,410 --> 00:19:28,250
is whether it works in the
opposite direction as well.

444
00:19:28,250 --> 00:19:30,530
That is when you have
an environmental context

445
00:19:30,530 --> 00:19:34,940
that requires a certain
type of behavioral strategy

446
00:19:34,940 --> 00:19:36,740
over evolutionary time,

447
00:19:36,740 --> 00:19:38,830
can you have developmental plasticity?

448
00:19:38,830 --> 00:19:43,210
In other words, can that behavioral need,

449
00:19:43,210 --> 00:19:45,000
and I'm just saying that metaphorically,

450
00:19:45,000 --> 00:19:46,820
but can the fact that individuals

451
00:19:46,820 --> 00:19:49,450
with a behavior that
matches their environment,

452
00:19:49,450 --> 00:19:51,970
can that feed into the
evolutionary process

453
00:19:51,970 --> 00:19:53,960
and cause a shift in life history,

454
00:19:53,960 --> 00:19:55,240
physiology, and morphology,

455
00:19:55,240 --> 00:19:58,523
to give the male different
tools as he's maturing.

456
00:19:59,690 --> 00:20:02,170
Let me show you this in a different way.

457
00:20:02,170 --> 00:20:04,640
And this is based on theory
on adaptive plasticity

458
00:20:04,640 --> 00:20:05,940
taken from across taxa.

459
00:20:05,940 --> 00:20:07,220
If you don't remember anything else,

460
00:20:07,220 --> 00:20:08,810
this slide will pop up multiple times,

461
00:20:08,810 --> 00:20:11,200
and hopefully you'll
remember how this works.

462
00:20:11,200 --> 00:20:14,860
Now, let me start by saying
the reason this is important.

463
00:20:14,860 --> 00:20:16,840
So what I'm gonna be talking about is

464
00:20:16,840 --> 00:20:19,170
ways in which you might get adjustments

465
00:20:19,170 --> 00:20:22,070
in individual development,
that results in differences

466
00:20:22,070 --> 00:20:26,700
in their adult phenotype that
lets them compete, survive,

467
00:20:26,700 --> 00:20:30,820
and be resilient, even under
variable adult environments.

468
00:20:30,820 --> 00:20:32,490
Now, the context I'm
gonna be talking about

469
00:20:32,490 --> 00:20:35,450
are within the natural range
of variation for the species,

470
00:20:35,450 --> 00:20:37,920
but if we understand adaptive plasticity,

471
00:20:37,920 --> 00:20:40,870
which species have it, and when it arises,

472
00:20:40,870 --> 00:20:43,640
it allows us to ask
questions about, for example,

473
00:20:43,640 --> 00:20:47,340
which species will be resilient
to rapid climate change.

474
00:20:47,340 --> 00:20:48,870
A change in the environment,

475
00:20:48,870 --> 00:20:52,453
can they or can they not
buffer that change and survive?

476
00:20:53,330 --> 00:20:55,610
Evolutionary change can take a long time,

477
00:20:55,610 --> 00:20:58,123
but plasticity can happen
within a generation.

478
00:20:59,070 --> 00:21:01,080
So let's go back to this model.

479
00:21:01,080 --> 00:21:03,000
So the idea is that you have an organism

480
00:21:03,000 --> 00:21:05,500
in which adult environments vary,

481
00:21:05,500 --> 00:21:07,160
and I'm just gonna call
this the adult environment

482
00:21:07,160 --> 00:21:08,990
or the context that is,

483
00:21:08,990 --> 00:21:11,640
under what set of conditions
do they have to compete?

484
00:21:12,820 --> 00:21:15,590
If there's reliable
developmental information

485
00:21:15,590 --> 00:21:17,570
that provides cues about

486
00:21:17,570 --> 00:21:20,620
what that environmental
context is going to be,

487
00:21:20,620 --> 00:21:21,800
then it's possible

488
00:21:21,800 --> 00:21:25,700
that developing juveniles will
integrate that information,

489
00:21:25,700 --> 00:21:27,870
change their development in some way

490
00:21:27,870 --> 00:21:29,780
that changes their adult phenotype,

491
00:21:29,780 --> 00:21:31,210
the set of traits they have,

492
00:21:31,210 --> 00:21:33,260
the tools to compete in that environment.

493
00:21:34,550 --> 00:21:38,090
If this is adaptive
plasticity, that change,

494
00:21:38,090 --> 00:21:42,370
which was initiated by the
detection of some variable aspect

495
00:21:42,370 --> 00:21:43,920
of the environment,

496
00:21:43,920 --> 00:21:46,560
that change will allow
these individuals as adults

497
00:21:46,560 --> 00:21:48,530
to compete more successfully

498
00:21:48,530 --> 00:21:50,750
in the environment that they detected,

499
00:21:50,750 --> 00:21:53,020
than if they hadn't changed.

500
00:21:53,020 --> 00:21:54,860
So they're plastic in the
sense that it would be

501
00:21:54,860 --> 00:21:57,420
one set of genes that could be switched

502
00:21:57,420 --> 00:22:00,200
in different ways to
create different body types

503
00:22:00,200 --> 00:22:02,200
that compete in different ways

504
00:22:02,200 --> 00:22:05,070
depending on what the
environment requires.

505
00:22:05,070 --> 00:22:06,930
So it sounds a bit like magic (laughs)

506
00:22:06,930 --> 00:22:07,910
and I kinda think it is,

507
00:22:07,910 --> 00:22:10,110
because it allows
populations to be buffered

508
00:22:10,110 --> 00:22:13,053
against short-term changes
in the environment.

509
00:22:14,420 --> 00:22:16,880
Now, the context, the
environmental changes

510
00:22:16,880 --> 00:22:19,070
or variations I'm
interested in is demography

511
00:22:19,070 --> 00:22:23,600
is the sex ratio and density
of conspecifics around you.

512
00:22:23,600 --> 00:22:25,560
And I'm really interested
in mating behaviors,

513
00:22:25,560 --> 00:22:29,220
and how male reproductive
traits, given what they,

514
00:22:29,220 --> 00:22:30,660
the challenges are with respect to

515
00:22:30,660 --> 00:22:32,030
competing with other males

516
00:22:32,030 --> 00:22:34,460
and with convincing
females to mate with them,

517
00:22:34,460 --> 00:22:37,590
how those change as
population density changes,

518
00:22:37,590 --> 00:22:40,383
and how they may be adaptively plastic.

519
00:22:41,240 --> 00:22:44,090
It's not easy to make rigorous predictions

520
00:22:44,090 --> 00:22:47,140
about what you expect in
terms of adaptive plasticity,

521
00:22:47,140 --> 00:22:48,810
because the traits that I'm interested in,

522
00:22:48,810 --> 00:22:50,700
these male reproductive traits

523
00:22:50,700 --> 00:22:54,230
are created by organisms
that have to decide

524
00:22:55,260 --> 00:22:58,580
on investment in different
types of traits altogether.

525
00:22:58,580 --> 00:23:00,690
So there's a trait you need to reproduce,

526
00:23:00,690 --> 00:23:02,340
like the males' copulatory organs,

527
00:23:02,340 --> 00:23:04,650
like their ability to court a female.

528
00:23:04,650 --> 00:23:06,660
There is an energy and resources

529
00:23:06,660 --> 00:23:08,770
that have to be devoted
to development and growth

530
00:23:08,770 --> 00:23:10,320
to get to that point,

531
00:23:10,320 --> 00:23:11,930
to somatic maintenance, the maintenance,

532
00:23:11,930 --> 00:23:13,830
for example of an immune system,

533
00:23:13,830 --> 00:23:16,520
and their trade-offs, because
once you use some energy

534
00:23:16,520 --> 00:23:18,210
for developing reproductive traits,

535
00:23:18,210 --> 00:23:20,040
it's no longer available for use

536
00:23:20,040 --> 00:23:22,300
in your immune system, for example.

537
00:23:22,300 --> 00:23:23,430
Now in many organisms,

538
00:23:23,430 --> 00:23:26,170
it's hard to make predictions
about what you expect,

539
00:23:26,170 --> 00:23:29,470
in a given environment, in
terms of the reproductive traits

540
00:23:29,470 --> 00:23:31,690
that will allow a male to succeed.

541
00:23:31,690 --> 00:23:34,460
And it's hard to predict this
because you're constantly

542
00:23:34,460 --> 00:23:36,210
getting replenishment
of the resources here

543
00:23:36,210 --> 00:23:38,660
so that trade-off can shift,

544
00:23:38,660 --> 00:23:41,570
and because the number of
matings a male can achieve

545
00:23:41,570 --> 00:23:44,800
in his lifetime can continue to accumulate

546
00:23:44,800 --> 00:23:47,350
and happen in very different
types of environments.

547
00:23:47,350 --> 00:23:49,240
So a few matings this year or this month,

548
00:23:49,240 --> 00:23:50,450
and a few in the next month,

549
00:23:50,450 --> 00:23:53,050
and the context in which
that's happening may shift.

550
00:23:54,020 --> 00:23:55,640
So finally we're back to black widows,

551
00:23:55,640 --> 00:23:57,230
and the Krogh Principle.

552
00:23:57,230 --> 00:24:00,780
So why are these good models
for adaptive plasticity?

553
00:24:00,780 --> 00:24:03,320
Because of the fact that
their mating system means

554
00:24:03,320 --> 00:24:07,210
that there's a very simplified
adult context for males.

555
00:24:07,210 --> 00:24:08,410
They don't eat as adults,

556
00:24:08,410 --> 00:24:11,700
so there's no replenishing
of that bolus of resources,

557
00:24:11,700 --> 00:24:12,820
if you will.

558
00:24:12,820 --> 00:24:15,810
They have few or one
mating in their lifetime.

559
00:24:15,810 --> 00:24:17,590
Red back spiders, brown widow spiders,

560
00:24:17,590 --> 00:24:19,580
they actually sacrifice
themselves to females,

561
00:24:19,580 --> 00:24:21,250
are usually cannibalized,

562
00:24:21,250 --> 00:24:23,020
but even some of those other species

563
00:24:23,020 --> 00:24:24,530
like Western widows, we now know

564
00:24:24,530 --> 00:24:27,590
that their mate search
mortality is so high,

565
00:24:27,590 --> 00:24:29,560
that they have a limited
number of matings likely

566
00:24:29,560 --> 00:24:30,463
in their lifetime.

567
00:24:31,400 --> 00:24:33,440
So this means you don't
really have to think about

568
00:24:33,440 --> 00:24:35,330
trade-offs for future matings.

569
00:24:35,330 --> 00:24:37,160
What you're trying to optimize,

570
00:24:37,160 --> 00:24:38,420
what you're trying to predict,

571
00:24:38,420 --> 00:24:40,110
then that mathematical way is

572
00:24:40,110 --> 00:24:42,270
what would it be best for males

573
00:24:42,270 --> 00:24:45,133
in a relatively tight
context around the time

574
00:24:45,133 --> 00:24:47,410
that they become sexually mature

575
00:24:47,410 --> 00:24:49,323
to achieve basically one mating?

576
00:24:50,800 --> 00:24:53,960
The other piece of this
puzzle is here, and that is,

577
00:24:53,960 --> 00:24:56,100
you'll only expect to see plasticity

578
00:24:56,100 --> 00:24:58,340
if there's variation in the environment

579
00:24:58,340 --> 00:25:00,950
that affects how male traits will do

580
00:25:00,950 --> 00:25:02,870
in terms of competition.

581
00:25:02,870 --> 00:25:04,530
And if there's something
that will allow you

582
00:25:04,530 --> 00:25:06,163
to predict that variation.

583
00:25:07,780 --> 00:25:10,730
Now is the environment variable,

584
00:25:10,730 --> 00:25:13,570
in a way that can't, that
could be predictable,

585
00:25:13,570 --> 00:25:14,940
but that isn't just the same

586
00:25:14,940 --> 00:25:16,530
as what your parents experienced?

587
00:25:16,530 --> 00:25:18,810
That's really what we mean by variation.

588
00:25:18,810 --> 00:25:20,380
If the environment is exactly the same

589
00:25:20,380 --> 00:25:21,920
prepared an offspring,

590
00:25:21,920 --> 00:25:23,530
then you don't need to detect things

591
00:25:23,530 --> 00:25:25,830
about the environment, you
will inherit from your parents

592
00:25:25,830 --> 00:25:27,950
the traits that will allow you to do best

593
00:25:27,950 --> 00:25:29,790
in that environment, right?

594
00:25:29,790 --> 00:25:31,610
Each one of us sitting here listening

595
00:25:31,610 --> 00:25:34,163
is the product of a successful parent.

596
00:25:35,010 --> 00:25:38,670
So with black widow spiders,
we do have that element

597
00:25:38,670 --> 00:25:41,460
of variability that is not
predictable based on the parents,

598
00:25:41,460 --> 00:25:42,970
and that's because they disperse

599
00:25:42,970 --> 00:25:45,720
by something called
ballooning, as spiderlings.

600
00:25:45,720 --> 00:25:48,190
So on the right, you can see a
video from an excellent paper

601
00:25:48,190 --> 00:25:49,290
from a few years ago,

602
00:25:49,290 --> 00:25:51,257
spiderlings let out a little bit of silk,

603
00:25:51,257 --> 00:25:54,040
and this is in early instars,
early in their development,

604
00:25:54,040 --> 00:25:57,220
and they essentially fly on
the wind to a new habitat.

605
00:25:57,220 --> 00:26:02,220
So where their parents
were competing, reproduced,

606
00:26:02,330 --> 00:26:05,690
and where these eggs
hatched is not the same

607
00:26:05,690 --> 00:26:09,150
as where the juveniles
will have to do well,

608
00:26:09,150 --> 00:26:11,860
if they're going to survive
and reproduce themselves.

609
00:26:11,860 --> 00:26:14,023
So the context is definitely variable,

610
00:26:14,970 --> 00:26:17,070
and it's also potentially predictable

611
00:26:17,070 --> 00:26:18,470
because of the fact that males,

612
00:26:18,470 --> 00:26:21,030
as I'll show you soon
are very short-lived.

613
00:26:21,030 --> 00:26:22,620
They may live only for a few weeks,

614
00:26:22,620 --> 00:26:25,680
even if they aren't killed by females.

615
00:26:25,680 --> 00:26:28,550
And so that means that the
environment that they detect

616
00:26:28,550 --> 00:26:31,780
late in development
would be a good predictor

617
00:26:31,780 --> 00:26:34,960
of the adult environment in
which they'd have to compete,

618
00:26:34,960 --> 00:26:36,950
but they couldn't get that
information from their parents.

619
00:26:36,950 --> 00:26:38,860
In other words, a parent's
traits that they passed on,

620
00:26:38,860 --> 00:26:41,220
the static traits won't
necessarily allow them

621
00:26:41,220 --> 00:26:42,970
to compete in that new environment.

622
00:26:44,660 --> 00:26:46,380
So let's apply some of these ideas,

623
00:26:46,380 --> 00:26:48,030
and I'll talk to you about these,

624
00:26:48,030 --> 00:26:50,600
this is again, I took
a little license here,

625
00:26:50,600 --> 00:26:51,500
and this is a little bit

626
00:26:51,500 --> 00:26:55,950
of a travel log through research
over many years in my lab,

627
00:26:55,950 --> 00:26:58,630
ending with things that have
been happening most recently.

628
00:26:58,630 --> 00:27:00,780
And it was really
applied to this question.

629
00:27:02,500 --> 00:27:05,470
Does adaptive plasticity
explain extreme variation

630
00:27:05,470 --> 00:27:06,303
in male size?

631
00:27:06,303 --> 00:27:09,010
So we picked a trait that
was particularly prominent,

632
00:27:09,010 --> 00:27:11,090
and that we knew was
related to sexual selection,

633
00:27:11,090 --> 00:27:12,650
and we asked whether plasticity

634
00:27:12,650 --> 00:27:15,200
that plasticity framework
could help us understand it.

635
00:27:15,200 --> 00:27:16,700
And the variation is quite huge.

636
00:27:16,700 --> 00:27:19,760
So here we see black widows,
Western black widows,

637
00:27:19,760 --> 00:27:21,980
and this is a large male and a small male,

638
00:27:21,980 --> 00:27:23,660
45 milligrams versus nine,

639
00:27:23,660 --> 00:27:25,960
which is actually quite
a lot of variation,

640
00:27:25,960 --> 00:27:27,160
much more so than you see here

641
00:27:27,160 --> 00:27:28,927
from Manute Bol and Muggsy Bogues.

642
00:27:28,927 --> 00:27:31,270
And if you were in my classroom,
I'd ask you to name them.

643
00:27:31,270 --> 00:27:33,460
And one person in the class
usually can name them,

644
00:27:33,460 --> 00:27:35,170
unless you're a fan of the eighties,

645
00:27:35,170 --> 00:27:36,820
or lived in the eighties like me.

646
00:27:38,330 --> 00:27:41,720
So extreme variation
in male size or weight

647
00:27:41,720 --> 00:27:44,170
is found in a number of different taxa,

648
00:27:44,170 --> 00:27:46,500
and is in itself an interesting question.

649
00:27:46,500 --> 00:27:48,590
So just to show you some
of the variation here,

650
00:27:48,590 --> 00:27:51,020
this is from a paper from 2009,

651
00:27:51,020 --> 00:27:53,330
and what they did was
calculate the coefficient

652
00:27:53,330 --> 00:27:54,660
of variation for male mass.

653
00:27:54,660 --> 00:27:56,770
So this basically looks at variation

654
00:27:56,770 --> 00:27:59,150
in male mass within a species,

655
00:27:59,150 --> 00:28:03,110
and it normalizes it for the
average size of that species.

656
00:28:03,110 --> 00:28:03,943
So in other words,

657
00:28:03,943 --> 00:28:05,950
you can compare a fruit
fly to a human being,

658
00:28:05,950 --> 00:28:07,840
and what you're looking at is variation

659
00:28:07,840 --> 00:28:10,050
from some average value.

660
00:28:10,050 --> 00:28:11,560
The gray lines here at the gray bars

661
00:28:11,560 --> 00:28:14,020
are different human
populations or societies,

662
00:28:14,020 --> 00:28:17,100
and the black lines are
bars are different species,

663
00:28:17,100 --> 00:28:20,540
and this is for 210 different
species across taxa.

664
00:28:20,540 --> 00:28:22,090
So you can see there's
quite a lot of difference

665
00:28:22,090 --> 00:28:24,500
in coefficient of
variation for a male mass,

666
00:28:24,500 --> 00:28:27,300
and as I'll point out
again, the Krogh principle

667
00:28:27,300 --> 00:28:30,280
Latrodectus species are
very high on this range,

668
00:28:30,280 --> 00:28:32,050
even across species.

669
00:28:32,050 --> 00:28:34,830
So we had seven different
species in the lab

670
00:28:34,830 --> 00:28:36,890
help reared under common conditions,

671
00:28:36,890 --> 00:28:39,850
and they range on this upper
end of the distribution.

672
00:28:39,850 --> 00:28:41,150
And I love to put this one in

673
00:28:41,150 --> 00:28:42,460
'cause this is the Northern widow,

674
00:28:42,460 --> 00:28:45,430
the Canadian Northern widow
populations are right near

675
00:28:45,430 --> 00:28:47,510
the top of this distribution.

676
00:28:47,510 --> 00:28:49,350
So it's an interesting question.

677
00:28:49,350 --> 00:28:52,160
How do you have such
wide variation in size

678
00:28:52,160 --> 00:28:55,210
and its extreme, even
relative to other species

679
00:28:55,210 --> 00:28:56,773
that show variation in size.

680
00:28:58,570 --> 00:29:01,050
So together with former
student Michael Kasumovic,

681
00:29:01,050 --> 00:29:02,030
who's now a professor

682
00:29:02,030 --> 00:29:04,950
at University of New
South Wales in Australia,

683
00:29:04,950 --> 00:29:06,960
we started to think about this in terms

684
00:29:06,960 --> 00:29:11,380
of the mathematics of
evolutionary change or plasticity.

685
00:29:11,380 --> 00:29:13,470
And what we were interested in the idea

686
00:29:13,470 --> 00:29:15,510
was that there was trade-offs

687
00:29:15,510 --> 00:29:17,840
between the things I
show on the slide here

688
00:29:17,840 --> 00:29:20,210
and that the type of context

689
00:29:20,210 --> 00:29:21,840
with respect to population density

690
00:29:21,840 --> 00:29:23,230
in which you find yourself

691
00:29:23,230 --> 00:29:25,440
might affect, which side of this trade off

692
00:29:25,440 --> 00:29:27,690
would be to the highest fitness.

693
00:29:27,690 --> 00:29:30,130
So for invertebrates like spiders,

694
00:29:30,130 --> 00:29:34,150
development time, if you have
a given set of resources,

695
00:29:34,150 --> 00:29:37,590
the longer you spend developing,
the larger you'll be,

696
00:29:37,590 --> 00:29:39,350
and the higher your body condition.

697
00:29:39,350 --> 00:29:42,523
So the fatter you'll be for
each unit length, let's say.

698
00:29:43,360 --> 00:29:45,450
And so this sets up a trade-off,

699
00:29:45,450 --> 00:29:48,420
rapid development would
mean that you're smaller,

700
00:29:48,420 --> 00:29:51,500
slower development would
mean that you're bigger.

701
00:29:51,500 --> 00:29:53,480
So we were interested in the question

702
00:29:53,480 --> 00:29:55,520
of whether that might tell us something

703
00:29:55,520 --> 00:29:57,630
about variation in size,

704
00:29:57,630 --> 00:29:59,430
and if in fact what we might be seeing

705
00:29:59,430 --> 00:30:00,990
is adaptive plasticity,

706
00:30:00,990 --> 00:30:03,310
where something about population density

707
00:30:03,310 --> 00:30:04,743
affects this trade off.

708
00:30:06,200 --> 00:30:08,130
So let's start with development time,

709
00:30:08,130 --> 00:30:10,910
and ask about what might
be affecting the trade-off.

710
00:30:10,910 --> 00:30:14,080
So in work with earlier
students, Lindsey Snow

711
00:30:14,080 --> 00:30:16,800
and Emily MacLeod now Dr. Emily MacLeod,

712
00:30:16,800 --> 00:30:20,880
we looked at sperm
competition in spiders, right?

713
00:30:20,880 --> 00:30:23,250
So this idea that males
were competing for access

714
00:30:23,250 --> 00:30:25,000
to the female's eggs.

715
00:30:25,000 --> 00:30:27,900
And what we found was males actually

716
00:30:27,900 --> 00:30:30,750
in both black widows and red back spiders

717
00:30:30,750 --> 00:30:32,920
lose a part of their copulatory organ,

718
00:30:32,920 --> 00:30:35,600
the genital mutilation,
or alteration I mentioned

719
00:30:35,600 --> 00:30:36,710
when they mate.

720
00:30:36,710 --> 00:30:40,830
And that piece, that little
tip of their copulatory organ

721
00:30:40,830 --> 00:30:42,610
actually becomes lodged in

722
00:30:42,610 --> 00:30:44,537
the female sperm storage organs at mating.

723
00:30:44,537 --> 00:30:46,950
And in fact, you can find
these in museum collections

724
00:30:46,950 --> 00:30:49,340
in 100-year-old females
preserved in alcohol.

725
00:30:49,340 --> 00:30:50,830
It's remarkable.

726
00:30:50,830 --> 00:30:53,190
So here we have the red back spiders,

727
00:30:53,190 --> 00:30:55,340
about 73% of the time the plugs,

728
00:30:55,340 --> 00:30:57,690
this tip will be placed in a place

729
00:30:57,690 --> 00:31:00,700
that prevents future males
from fertilizing eggs,

730
00:31:00,700 --> 00:31:03,710
and for Western widows,
it's fairly similar.

731
00:31:03,710 --> 00:31:05,580
This sets up a situation in which

732
00:31:05,580 --> 00:31:07,350
the first male to mate will father

733
00:31:07,350 --> 00:31:09,610
most of the female's offspring.

734
00:31:09,610 --> 00:31:12,250
So however many males are
there in an environment,

735
00:31:12,250 --> 00:31:15,000
when a female becomes
sexually mature and receptive,

736
00:31:15,000 --> 00:31:16,480
the first male to get to her

737
00:31:16,480 --> 00:31:18,290
and to be accepted as a mate is

738
00:31:18,290 --> 00:31:20,830
likely to father most of her offspring.

739
00:31:20,830 --> 00:31:23,083
That's what we call scramble competition.

740
00:31:24,650 --> 00:31:26,620
And scramble competition has implications

741
00:31:26,620 --> 00:31:28,100
for development time.

742
00:31:28,100 --> 00:31:29,940
So for a male who's
becoming sexually mature

743
00:31:29,940 --> 00:31:32,930
in an environment in
which females are nearby,

744
00:31:32,930 --> 00:31:36,970
rapidly developing maybe more
important than being large.

745
00:31:36,970 --> 00:31:39,480
So decreased development
time in the presence

746
00:31:39,480 --> 00:31:42,380
of females would result
potentially in decreased size,

747
00:31:42,380 --> 00:31:45,313
and body condition, because
that's how invertebrates work.

748
00:31:47,240 --> 00:31:49,610
Now, what about the other side?

749
00:31:49,610 --> 00:31:51,200
What about size and body condition?

750
00:31:51,200 --> 00:31:52,707
You can't just sort of
throw those away and say,

751
00:31:52,707 --> 00:31:54,677
"Okay, well always develop rapidly."

752
00:31:55,660 --> 00:31:57,620
Are there conditions
under which increased size

753
00:31:57,620 --> 00:31:59,440
of body condition are important?

754
00:31:59,440 --> 00:32:00,620
And in work with Jeff Stoltz,

755
00:32:00,620 --> 00:32:02,610
we explore this in a
variety of different ways,

756
00:32:02,610 --> 00:32:06,330
looking at the outcomes of
female choice, competition,

757
00:32:06,330 --> 00:32:08,060
and also what males needed to do

758
00:32:08,060 --> 00:32:10,870
to be successful courting females.

759
00:32:10,870 --> 00:32:12,680
Courtship is actually
a really important part

760
00:32:12,680 --> 00:32:14,650
of the mating system of these spiders,

761
00:32:14,650 --> 00:32:17,400
because they spend quite a long time

762
00:32:17,400 --> 00:32:18,840
courting on the females' web,

763
00:32:18,840 --> 00:32:23,260
moving with an energetically
expensive vibrational signal,

764
00:32:23,260 --> 00:32:25,210
even before they touch the female.

765
00:32:25,210 --> 00:32:26,840
And then after they climb on the female,

766
00:32:26,840 --> 00:32:29,950
they still spend a long time
courting that female, as well.

767
00:32:29,950 --> 00:32:32,470
And in fact, the total
time spent in courtship

768
00:32:32,470 --> 00:32:35,040
prior to mating is about three
hours for red back spiders,

769
00:32:35,040 --> 00:32:37,420
about one hour on average
for Western widows.

770
00:32:37,420 --> 00:32:38,790
So a lot of investment,

771
00:32:38,790 --> 00:32:40,710
especially for a male that's doing this

772
00:32:40,710 --> 00:32:43,560
using only the energy
acquired as a juvenile

773
00:32:43,560 --> 00:32:46,020
after a period of mate searching.

774
00:32:46,020 --> 00:32:48,210
Now some great work by Sen Sivalinghem

775
00:32:48,210 --> 00:32:50,180
that I'll let you listen to in a minute,

776
00:32:50,180 --> 00:32:52,110
looks at this courtship pattern.

777
00:32:52,110 --> 00:32:53,870
And --
(loud vibrations)

778
00:32:53,870 --> 00:32:54,950
what you're hearing is actually

779
00:32:54,950 --> 00:32:57,303
the male's courtship
vibrations on the web.

780
00:32:57,303 --> 00:33:00,560
(loud vibrations)

781
00:33:00,560 --> 00:33:03,823
Very energetically costly
and maintained for hours.

782
00:33:05,240 --> 00:33:08,290
Now when the male gets to the female,

783
00:33:08,290 --> 00:33:11,500
there is potentially
competition with other males,

784
00:33:11,500 --> 00:33:13,100
because it's not always just one male.

785
00:33:13,100 --> 00:33:14,990
Sometimes there's two males on the web.

786
00:33:14,990 --> 00:33:16,150
And when that happens,

787
00:33:16,150 --> 00:33:20,020
male size can be important, as
well as having the endurance

788
00:33:20,020 --> 00:33:21,370
or the condition that allows them

789
00:33:21,370 --> 00:33:23,300
to get through the courtship
in the first place.

790
00:33:23,300 --> 00:33:24,920
And here, what you're seeing is a closeup

791
00:33:24,920 --> 00:33:29,040
of one male who was
copulating with a female,

792
00:33:29,040 --> 00:33:30,510
a second male who was actually larger

793
00:33:30,510 --> 00:33:32,110
who bit that first male.

794
00:33:32,110 --> 00:33:33,940
and the first one goes down.

795
00:33:33,940 --> 00:33:38,810
So this sort of competition is a feature

796
00:33:38,810 --> 00:33:40,760
of mating whenever
there's two males present

797
00:33:40,760 --> 00:33:41,610
on the web together,

798
00:33:41,610 --> 00:33:44,080
and can result in the
death of one of the males.

799
00:33:44,080 --> 00:33:46,040
So it can be very costly.

800
00:33:46,040 --> 00:33:49,220
So in a series of studies and
I provide the citations here,

801
00:33:49,220 --> 00:33:51,030
Jeff and I looked at this together

802
00:33:51,030 --> 00:33:54,930
with colleague Daniela Liaz,
Andrew Mason, and Paul DeLuca.

803
00:33:54,930 --> 00:33:57,530
And we asked the question
across these studies,

804
00:33:57,530 --> 00:34:01,380
can size predict who
wins, and in fact it can.

805
00:34:01,380 --> 00:34:04,740
It's about 75% of the
time the large male wins,

806
00:34:04,740 --> 00:34:07,630
about 25% of the time the small male wins.

807
00:34:07,630 --> 00:34:09,920
So there's an advantage to being large.

808
00:34:09,920 --> 00:34:12,970
Larger males can endure
that long courtship,

809
00:34:12,970 --> 00:34:15,000
they can endure a long mate search

810
00:34:15,000 --> 00:34:17,373
and they also do better
in direct competition.

811
00:34:19,150 --> 00:34:23,710
So if you look at the flip
side of this trade-off then,

812
00:34:23,710 --> 00:34:25,610
endurance and direct competition

813
00:34:25,610 --> 00:34:29,050
favor larger size and or body condition,

814
00:34:29,050 --> 00:34:30,810
and so if you're in a context

815
00:34:30,810 --> 00:34:32,950
in which size and body
condition are important,

816
00:34:32,950 --> 00:34:35,110
in which there's likely to
be a lot of competition,

817
00:34:35,110 --> 00:34:36,970
in which you have to
search a long distance

818
00:34:36,970 --> 00:34:38,280
to find a female,

819
00:34:38,280 --> 00:34:42,250
then body size and condition
would allow males to do better.

820
00:34:42,250 --> 00:34:44,980
And the result is that
their development time

821
00:34:44,980 --> 00:34:46,600
would have to be longer

822
00:34:46,600 --> 00:34:49,833
at a given level of resources
to build that type of a body.

823
00:34:52,020 --> 00:34:56,607
So Mike then developed this
predictive framework and said,

824
00:34:56,607 --> 00:34:59,177
"If this is true, if what we
understand so far is true,

825
00:34:59,177 --> 00:35:01,227
"if plasticity works
the way that we expect,

826
00:35:01,227 --> 00:35:04,337
"then what we should see
is that at high densities,

827
00:35:04,337 --> 00:35:05,677
"at high population densities,

828
00:35:05,677 --> 00:35:09,087
"when females are close by
and present in large numbers,

829
00:35:09,087 --> 00:35:11,767
"males should decrease
the development time,

830
00:35:11,767 --> 00:35:15,420
"at the cost of smaller body
size and body condition."

831
00:35:15,420 --> 00:35:17,100
When there's very few females,

832
00:35:17,100 --> 00:35:18,770
when there's a low density population

833
00:35:18,770 --> 00:35:20,540
or females are far away,

834
00:35:20,540 --> 00:35:23,250
males should increase
their development time

835
00:35:23,250 --> 00:35:25,310
in order to increase
body size and condition

836
00:35:25,310 --> 00:35:27,670
and do well under those contexts.

837
00:35:27,670 --> 00:35:30,730
Now I'm using verbal shorthand
in evolutionary terms

838
00:35:30,730 --> 00:35:35,460
what this means is any male
that had a plastic response

839
00:35:35,460 --> 00:35:38,950
to detecting something
about population density

840
00:35:38,950 --> 00:35:42,200
and made this trade off longer
or shorter development times

841
00:35:42,200 --> 00:35:44,770
would be favored, and that
trait's expected to spread

842
00:35:44,770 --> 00:35:45,920
through the population.

843
00:35:47,690 --> 00:35:49,780
So if we go back to this framework then,

844
00:35:49,780 --> 00:35:51,920
what we expected then is that low density

845
00:35:51,920 --> 00:35:54,260
males would be adapted to endure,

846
00:35:54,260 --> 00:35:56,720
to develop for longer and be larger,

847
00:35:56,720 --> 00:35:59,390
at high densities, they'd
be adapted to scramble,

848
00:35:59,390 --> 00:36:02,100
and they would accelerate
their development,

849
00:36:02,100 --> 00:36:04,070
be smaller but be better able to compete

850
00:36:04,070 --> 00:36:07,093
and they rapidly rapidly get to females.

851
00:36:07,970 --> 00:36:09,210
So what's missing now is

852
00:36:09,210 --> 00:36:12,500
what about their environment
would provide cues

853
00:36:12,500 --> 00:36:14,950
about which of these
strategies they should take.

854
00:36:16,140 --> 00:36:18,340
And we knew something about this

855
00:36:18,340 --> 00:36:21,080
for both red backs and
black widow spiders,

856
00:36:21,080 --> 00:36:24,050
and part of that is
dependent on their lifespan.

857
00:36:24,050 --> 00:36:26,120
So red back spider males,

858
00:36:26,120 --> 00:36:27,560
even when they're not killed by females

859
00:36:27,560 --> 00:36:29,410
survive for only about 25 days,

860
00:36:29,410 --> 00:36:31,670
that's under optimal
laboratory environments.

861
00:36:31,670 --> 00:36:33,160
Typically we expect in the field,

862
00:36:33,160 --> 00:36:36,450
they actually survive for
only a couple of weeks.

863
00:36:36,450 --> 00:36:38,320
In contrast, Western black widow males

864
00:36:38,320 --> 00:36:39,830
are much more long-lived,

865
00:36:39,830 --> 00:36:41,370
they will live for about three months,

866
00:36:41,370 --> 00:36:42,870
which is typically the duration

867
00:36:42,870 --> 00:36:46,160
of a complete mating season.

868
00:36:46,160 --> 00:36:48,440
In both cases, males
become sexually mature

869
00:36:48,440 --> 00:36:50,750
at various times through the season.

870
00:36:50,750 --> 00:36:52,580
So for red backs, there's a narrow window

871
00:36:52,580 --> 00:36:54,400
around the time at which
they become mature,

872
00:36:54,400 --> 00:36:56,590
and under which they have to compete,

873
00:36:56,590 --> 00:36:58,520
for Western widows, they
tend to be competing

874
00:36:58,520 --> 00:37:01,290
across an entire chunk of a season,

875
00:37:01,290 --> 00:37:04,463
during which the demographic
context may change.

876
00:37:06,510 --> 00:37:08,810
What might they be using to detect this?

877
00:37:08,810 --> 00:37:12,070
In both species, they have
access to sex pheromones.

878
00:37:12,070 --> 00:37:14,790
Females produce sex
pheromones that are released

879
00:37:14,790 --> 00:37:17,710
into the air and that males
can detect at a distance.

880
00:37:17,710 --> 00:37:19,490
They also have pheromones
that trigger courtship

881
00:37:19,490 --> 00:37:21,270
when they're in contact.

882
00:37:21,270 --> 00:37:22,407
We know from previous work

883
00:37:22,407 --> 00:37:24,430
and some of this with Luciana Baruffaldi,

884
00:37:24,430 --> 00:37:25,670
some with Catherine Scott,

885
00:37:25,670 --> 00:37:28,180
some with Jeff Stoltz, all
three of them now doctors.

886
00:37:28,180 --> 00:37:30,320
Congratulations (chuckles).

887
00:37:30,320 --> 00:37:32,560
We know that sex pheromones indicate

888
00:37:32,560 --> 00:37:34,660
the receptivity level of females,

889
00:37:34,660 --> 00:37:36,400
and it also can give the males information

890
00:37:36,400 --> 00:37:38,830
about the risk of cannibalism,
but that's another story.

891
00:37:38,830 --> 00:37:40,210
So some of the ways that we look at this

892
00:37:40,210 --> 00:37:41,790
is by looking at male attraction

893
00:37:41,790 --> 00:37:46,790
to pheromone-laden filter paper,

894
00:37:47,730 --> 00:37:51,010
either in a setup like this
and an airborne pheromone trial

895
00:37:51,010 --> 00:37:52,293
or in contact.

896
00:37:54,170 --> 00:37:57,350
So information in the air

897
00:37:57,350 --> 00:37:59,690
could give males
information about density.

898
00:37:59,690 --> 00:38:01,120
And we expected that would be important

899
00:38:01,120 --> 00:38:02,700
for red back spiders in particular,

900
00:38:02,700 --> 00:38:05,720
because what they smell
as they're developing

901
00:38:05,720 --> 00:38:08,090
in that last instar will be very relevant

902
00:38:08,090 --> 00:38:09,350
to how they have to compete

903
00:38:09,350 --> 00:38:12,010
in the few weeks during
which they would survive.

904
00:38:12,010 --> 00:38:14,570
So fine-scale variation, local variation,

905
00:38:14,570 --> 00:38:17,233
both temporal and spatial
would be important.

906
00:38:18,140 --> 00:38:20,220
We didn't expect the
same for black widows,

907
00:38:20,220 --> 00:38:22,850
and that's because they live
for an entire mating season,

908
00:38:22,850 --> 00:38:25,390
which may be three months in duration.

909
00:38:25,390 --> 00:38:26,690
But as I'll show you in a minute,

910
00:38:26,690 --> 00:38:30,540
we did know that density
of females is predictable

911
00:38:30,540 --> 00:38:33,220
in a seasonal pattern in black widows,

912
00:38:33,220 --> 00:38:35,500
over the course of those
three month periods.

913
00:38:35,500 --> 00:38:36,590
So what we expected then

914
00:38:36,590 --> 00:38:38,960
for black widows is
that they would use cues

915
00:38:38,960 --> 00:38:43,960
of the time of season to determine
plasticity if it existed.

916
00:38:45,260 --> 00:38:47,600
So we did field work that showed this.

917
00:38:47,600 --> 00:38:50,970
This is work with Catherine
Scott and Sean McCann and

918
00:38:50,970 --> 00:38:54,010
I should say, thanks very much
to the Tsawout First Nation,

919
00:38:54,010 --> 00:38:56,210
for giving us access to their lands.

920
00:38:56,210 --> 00:38:57,620
And over the course of the season,

921
00:38:57,620 --> 00:39:01,270
what we found is that at
the early in the season,

922
00:39:01,270 --> 00:39:03,300
females are present at
fairly low densities.

923
00:39:03,300 --> 00:39:04,930
That shows you the number of females

924
00:39:04,930 --> 00:39:07,780
from July through September
at this field site,

925
00:39:07,780 --> 00:39:09,070
and the number of males,

926
00:39:09,070 --> 00:39:11,140
and you can see that as
the season goes along,

927
00:39:11,140 --> 00:39:14,000
more and more females
become sexually adults,

928
00:39:14,000 --> 00:39:15,260
and become receptive,

929
00:39:15,260 --> 00:39:16,560
so that there's a higher density

930
00:39:16,560 --> 00:39:18,610
of females towards the end of the season.

931
00:39:20,640 --> 00:39:23,480
Similar work in the Hastings
Natural History Reserve

932
00:39:23,480 --> 00:39:25,930
in California showed
the same sort of pattern

933
00:39:25,930 --> 00:39:28,600
in that early in the season in the fall,

934
00:39:28,600 --> 00:39:30,000
over the course of three months,

935
00:39:30,000 --> 00:39:32,510
females tend to be
present in high density,

936
00:39:32,510 --> 00:39:33,980
many of them unmated.

937
00:39:33,980 --> 00:39:35,100
Later in the season,

938
00:39:35,100 --> 00:39:37,673
females are unmated and
present at low density.

939
00:39:38,590 --> 00:39:40,030
So we have this situation

940
00:39:40,030 --> 00:39:41,650
in which we do have this difference,

941
00:39:41,650 --> 00:39:43,160
and we have two different
types of the cues

942
00:39:43,160 --> 00:39:47,170
that could demonstrate what's going on.

943
00:39:47,170 --> 00:39:49,020
So in the lab, we did an experiment,

944
00:39:49,020 --> 00:39:51,900
a series of experiments,
really, where we expose males

945
00:39:51,900 --> 00:39:54,350
to pheromones, red back
males to pheromones

946
00:39:54,350 --> 00:39:58,510
produced by females at
simulated high or low densities,

947
00:39:58,510 --> 00:40:00,630
and we found just what we predicted.

948
00:40:00,630 --> 00:40:02,860
When we simulated low density of females,

949
00:40:02,860 --> 00:40:05,240
males slowed down their development.

950
00:40:05,240 --> 00:40:06,600
Their longevity was higher

951
00:40:06,600 --> 00:40:08,630
and they were larger as a result.

952
00:40:08,630 --> 00:40:12,210
Whereas when lots of females
could be detected by males,

953
00:40:12,210 --> 00:40:14,693
they developed fairly
rapidly, at small size.

954
00:40:16,450 --> 00:40:19,220
We did similar experiments
with black widows,

955
00:40:19,220 --> 00:40:20,053
but in this case,

956
00:40:20,053 --> 00:40:22,750
simulating differences
in this time of season,

957
00:40:22,750 --> 00:40:25,143
and what we did was simulated,

958
00:40:26,250 --> 00:40:27,083
I skipped that one,

959
00:40:27,083 --> 00:40:29,160
we simulated the fall typical

960
00:40:29,160 --> 00:40:30,730
or spring typical temperatures,

961
00:40:30,730 --> 00:40:33,320
when we expected males to evolve,

962
00:40:33,320 --> 00:40:35,930
to develop to endure
long distance, searching

963
00:40:35,930 --> 00:40:38,420
for mates versus having
lots of females around

964
00:40:38,420 --> 00:40:40,033
as we showed in the field data.

965
00:40:40,960 --> 00:40:42,530
And what we found was,

966
00:40:42,530 --> 00:40:45,930
as we predicted when males
were reared under conditions

967
00:40:45,930 --> 00:40:48,090
typical of a high density population,

968
00:40:48,090 --> 00:40:49,890
that is typical of a fall,

969
00:40:49,890 --> 00:40:53,070
they developed much more rapidly,

970
00:40:53,070 --> 00:40:54,970
and as a result, they were smaller

971
00:40:54,970 --> 00:40:56,100
than when we did the same

972
00:40:56,100 --> 00:40:58,403
in the spring typical temperatures.

973
00:40:59,340 --> 00:41:01,000
More than that, we asked about

974
00:41:01,000 --> 00:41:03,490
whether they were adapted
for scramble competition,

975
00:41:03,490 --> 00:41:05,457
by essentially racing them on a track.

976
00:41:05,457 --> 00:41:08,320
(chuckles) This is something
we do with widow spiders.

977
00:41:08,320 --> 00:41:09,930
We simulate a predation stimulus

978
00:41:09,930 --> 00:41:11,760
by lightly touching
them with a paintbrush,

979
00:41:11,760 --> 00:41:15,340
and then we see how fast
and sustainably they run.

980
00:41:15,340 --> 00:41:17,243
The more sustainably they run,

981
00:41:17,243 --> 00:41:20,100
we refer as the better they'll
be at scramble competition

982
00:41:20,100 --> 00:41:22,710
performance racing to find a female.

983
00:41:22,710 --> 00:41:24,150
So I'm just gonna show
you some of the data

984
00:41:24,150 --> 00:41:27,780
from a number of times amount
stops in this condition.

985
00:41:27,780 --> 00:41:30,170
So males who are able to run continuously

986
00:41:30,170 --> 00:41:33,790
with fewer stops are better
at scramble competition.

987
00:41:33,790 --> 00:41:35,020
And what you can see here is

988
00:41:35,020 --> 00:41:37,570
that males reared under
fall typical temperatures,

989
00:41:37,570 --> 00:41:38,810
which simulate the time

990
00:41:38,810 --> 00:41:41,220
during which should be a
lot of females available,

991
00:41:41,220 --> 00:41:42,700
they rarely stop,

992
00:41:42,700 --> 00:41:45,350
regardless of the diet
in which we reared them.

993
00:41:45,350 --> 00:41:48,490
Whereas males reared on
spring typical temperatures

994
00:41:48,490 --> 00:41:50,850
are only able to run sustainably

995
00:41:50,850 --> 00:41:53,110
if they have a very high diet.

996
00:41:53,110 --> 00:41:54,750
So males on a lower diet

997
00:41:54,750 --> 00:41:56,760
show this big difference
in that they're stopping

998
00:41:56,760 --> 00:41:57,593
all the time.

999
00:41:59,690 --> 00:42:00,523
More than that,

1000
00:42:00,523 --> 00:42:02,510
we showed in another set of experiments,

1001
00:42:02,510 --> 00:42:06,370
that if you release males in nature,

1002
00:42:06,370 --> 00:42:09,880
or if you put out traps that draw males in

1003
00:42:09,880 --> 00:42:11,920
by using pheromones of females,

1004
00:42:11,920 --> 00:42:14,320
and then take a look at the size of males

1005
00:42:14,320 --> 00:42:16,290
who show up at these traps,

1006
00:42:16,290 --> 00:42:18,250
the timing is very different,

1007
00:42:18,250 --> 00:42:20,190
as a function of male size.

1008
00:42:20,190 --> 00:42:22,160
We measured a measure of fitness,

1009
00:42:22,160 --> 00:42:24,100
which looks partly at how quickly males

1010
00:42:24,100 --> 00:42:26,400
reach these pheromone sources,

1011
00:42:26,400 --> 00:42:27,860
and what we find that is it's actually

1012
00:42:27,860 --> 00:42:29,943
the smaller males who get there faster.

1013
00:42:31,440 --> 00:42:35,050
So essentially then we've made our way

1014
00:42:35,050 --> 00:42:36,800
through this theoretical framework,

1015
00:42:36,800 --> 00:42:41,010
we've used a species that
according to the Krogh's Principle

1016
00:42:41,010 --> 00:42:43,830
may be a really good one for
looking at adaptive plasticity.

1017
00:42:43,830 --> 00:42:45,690
And we now have evidence

1018
00:42:45,690 --> 00:42:47,920
that not only the adult
environment changes

1019
00:42:47,920 --> 00:42:51,190
with respect to the density
and availability of females,

1020
00:42:51,190 --> 00:42:54,130
not only that for some that
species are able to detect

1021
00:42:54,130 --> 00:42:55,830
that difference either through pheromones

1022
00:42:55,830 --> 00:42:57,460
or the time of season,

1023
00:42:57,460 --> 00:42:58,430
and they changed their,

1024
00:42:58,430 --> 00:43:00,800
that they changed their
development as a result,

1025
00:43:00,800 --> 00:43:02,760
but that changed developmental pattern

1026
00:43:02,760 --> 00:43:04,520
can also lead to a fitness advantage

1027
00:43:04,520 --> 00:43:06,543
in the environment in which they develop.

1028
00:43:08,980 --> 00:43:11,650
I wanna stop there so that
we have time for questions.

1029
00:43:11,650 --> 00:43:13,920
And just wanna say that we are exploring

1030
00:43:13,920 --> 00:43:15,720
many other areas in the lab,

1031
00:43:15,720 --> 00:43:18,750
this is the one that I love talking about,

1032
00:43:18,750 --> 00:43:21,670
because it is to me, such a great example

1033
00:43:21,670 --> 00:43:22,760
of why even though there are weird

1034
00:43:22,760 --> 00:43:24,280
black widows with cannibalism,

1035
00:43:24,280 --> 00:43:26,700
they may tell us something
fundamental about theory.

1036
00:43:26,700 --> 00:43:28,550
And I really wanna thank these undergrads.

1037
00:43:28,550 --> 00:43:30,240
I like to talk while
they're up on the screen

1038
00:43:30,240 --> 00:43:31,960
because this work requires

1039
00:43:31,960 --> 00:43:35,870
that we rear thousands
and thousands of spiders,

1040
00:43:35,870 --> 00:43:37,540
and I have 10 to 15 undergrads

1041
00:43:37,540 --> 00:43:39,730
in a non-COVID year
working in a lab at a time,

1042
00:43:39,730 --> 00:43:40,730
and I wanna thank them.

1043
00:43:40,730 --> 00:43:42,340
Ariela is named in particular,

1044
00:43:42,340 --> 00:43:43,990
and so Sean on the next slide,

1045
00:43:43,990 --> 00:43:46,130
because they are my COVID-rearing crew,

1046
00:43:46,130 --> 00:43:49,200
and they are working even
while my lab was shut down

1047
00:43:49,200 --> 00:43:52,110
as a essential worker,
so thank you for that.

1048
00:43:52,110 --> 00:43:52,943
And then finally,

1049
00:43:52,943 --> 00:43:56,550
I really wanna thank all
the lab members currently

1050
00:43:56,550 --> 00:43:59,550
who are instrumental to everything I do.

1051
00:43:59,550 --> 00:44:01,460
And I neglected to say on the first slide,

1052
00:44:01,460 --> 00:44:04,240
although I did include his Twitter handle,

1053
00:44:04,240 --> 00:44:06,550
Dr. Sean McCann took the outstanding

1054
00:44:06,550 --> 00:44:07,770
many of the outstanding videos

1055
00:44:07,770 --> 00:44:10,740
and pictures you saw in this presentation,

1056
00:44:10,740 --> 00:44:12,590
and they're amazing, thank you, Sean.

1057
00:44:13,590 --> 00:44:15,160
And then finally, I
think I'll stop with that

1058
00:44:15,160 --> 00:44:17,840
and just say thanks also
to the million black widows

1059
00:44:17,840 --> 00:44:19,080
we estimate we've reared

1060
00:44:19,080 --> 00:44:21,200
in the lab over the last 20 years.

1061
00:44:21,200 --> 00:44:22,350
And I thank you for your attention,

1062
00:44:22,350 --> 00:44:23,713
I'm happy to take questions.

1063
00:44:27,610 --> 00:44:28,740
- Thank you so much, Maydianne,

1064
00:44:28,740 --> 00:44:30,433
that was a great talk,

1065
00:44:31,470 --> 00:44:33,780
the research is really interesting.

1066
00:44:33,780 --> 00:44:36,660
We have a bunch of questions here.

1067
00:44:36,660 --> 00:44:39,727
I'll start with this one.

1068
00:44:39,727 --> 00:44:42,427
"If the male black widow
has a lower survival rate,

1069
00:44:42,427 --> 00:44:45,480
"does this affect the ratio
of male to female eggs

1070
00:44:45,480 --> 00:44:46,313
that are produced?

1071
00:44:46,313 --> 00:44:48,007
"Would there be a higher percentage

1072
00:44:48,007 --> 00:44:50,830
"of male spider eggs laid by the female?"

1073
00:44:50,830 --> 00:44:52,390
- Outstanding question.

1074
00:44:52,390 --> 00:44:54,840
And I would that I had an answer already,

1075
00:44:54,840 --> 00:44:56,890
because I'm asked that
question frequently.

1076
00:44:56,890 --> 00:44:58,030
We don't know yet.

1077
00:44:58,030 --> 00:45:03,030
As far as we can tell, the
sex ratios are usually 50-50

1078
00:45:03,100 --> 00:45:06,220
when females produce offspring,

1079
00:45:06,220 --> 00:45:08,790
but we know from other species

1080
00:45:08,790 --> 00:45:12,070
that when there is different payoffs

1081
00:45:12,070 --> 00:45:13,360
for having males or females,

1082
00:45:13,360 --> 00:45:16,240
that they can adjust the sex
ratio of their offspring.

1083
00:45:16,240 --> 00:45:18,440
So there's some classic studies in birds,

1084
00:45:18,440 --> 00:45:20,850
there's one species of spider
where they're able to do this.

1085
00:45:20,850 --> 00:45:22,320
And we know they can do that

1086
00:45:22,320 --> 00:45:25,100
in various kinds of really
fascinating mechanistic ways.

1087
00:45:25,100 --> 00:45:26,990
The question would be,
what's the advantage

1088
00:45:26,990 --> 00:45:29,320
to the female of having
more males or females,

1089
00:45:29,320 --> 00:45:32,290
rather than kinda what's
the sex ratio at the end.

1090
00:45:32,290 --> 00:45:35,610
So if females are able to
manipulate the sex ratio,

1091
00:45:35,610 --> 00:45:37,173
I predict we probably
wouldn't see it in the lab

1092
00:45:37,173 --> 00:45:39,340
because they're not getting the cues

1093
00:45:39,340 --> 00:45:42,180
unless we do an experiment
of a higher low density,

1094
00:45:42,180 --> 00:45:43,610
or a situation in which having a male

1095
00:45:43,610 --> 00:45:46,840
would be particularly valuable
or particularly costly.

1096
00:45:46,840 --> 00:45:49,660
So what we really need to do
is figure out in the field,

1097
00:45:49,660 --> 00:45:52,710
does a female who skews
her offspring towards males

1098
00:45:53,600 --> 00:45:56,650
end up with more grand
offspring than one who doesn't,

1099
00:45:56,650 --> 00:45:58,220
and then we would
simulate those conditions

1100
00:45:58,220 --> 00:46:00,120
and ask if they shift the sex ratio.

1101
00:46:00,120 --> 00:46:02,360
My postdoc, Luciana
Baruffaldi's actually taken

1102
00:46:02,360 --> 00:46:03,750
with those questions right now.

1103
00:46:03,750 --> 00:46:04,850
So invite me back next year,

1104
00:46:04,850 --> 00:46:06,650
and maybe I can tell you the answer.

1105
00:46:09,470 --> 00:46:10,350
- Wonderful talk.

1106
00:46:10,350 --> 00:46:12,630
Thank you so much.
- Thank you.

1107
00:46:15,640 --> 00:46:17,800
I'm gonna go sort of in order,

1108
00:46:17,800 --> 00:46:21,020
why do female black widows eat their mate?

1109
00:46:21,020 --> 00:46:23,720
You sort of alluded to that, but --

1110
00:46:23,720 --> 00:46:25,690
- Yeah.
- There's a direct question.

1111
00:46:25,690 --> 00:46:28,110
- That's a great question.

1112
00:46:28,110 --> 00:46:30,560
So we do know that from that one study

1113
00:46:30,560 --> 00:46:31,860
that came out of Australia, actually,

1114
00:46:31,860 --> 00:46:33,930
Kate Barry's lab, Sean Wilder,

1115
00:46:33,930 --> 00:46:37,050
and I'm sorry, Boiso, I
forgot your first name,

1116
00:46:37,050 --> 00:46:37,943
Luke, I believe,

1117
00:46:39,200 --> 00:46:41,080
that there is an advantage for females

1118
00:46:41,080 --> 00:46:43,490
through cannibalism in the
sense that their offspring

1119
00:46:43,490 --> 00:46:44,870
is not that they have more offspring,

1120
00:46:44,870 --> 00:46:47,400
but the offspring
actually developed faster,

1121
00:46:47,400 --> 00:46:49,620
and have higher fitness for that reason.

1122
00:46:49,620 --> 00:46:51,480
What we don't know is
whether that's because

1123
00:46:51,480 --> 00:46:54,300
of something specific in the male's body,

1124
00:46:54,300 --> 00:46:57,770
or whether the female is actually
allocating more resources

1125
00:46:57,770 --> 00:47:01,560
to males, to offspring
after she kills her mate.

1126
00:47:01,560 --> 00:47:05,130
Now that sounds weird, but
we know that cannibalism

1127
00:47:05,130 --> 00:47:08,640
seems to be a mechanism of
choice for females, in a sense,

1128
00:47:08,640 --> 00:47:12,110
and so it could be that
females are selecting males

1129
00:47:12,110 --> 00:47:14,880
who are cannibalized by females

1130
00:47:14,880 --> 00:47:16,840
are better quality males in some way,

1131
00:47:16,840 --> 00:47:19,250
and so the female invests
more in their offspring.

1132
00:47:19,250 --> 00:47:21,030
But the truth is we don't know.

1133
00:47:21,030 --> 00:47:22,640
And one of the reasons
we don't know is that

1134
00:47:22,640 --> 00:47:27,280
unlike the three weeks to
a month for a male to live,

1135
00:47:27,280 --> 00:47:29,080
females can live up to two years.

1136
00:47:29,080 --> 00:47:31,570
And so we are just now starting
to accumulate enough data

1137
00:47:31,570 --> 00:47:33,930
to ask these questions
about female strategies

1138
00:47:33,930 --> 00:47:36,360
instead of focusing all the
time on the male strategies.

1139
00:47:36,360 --> 00:47:38,980
And I'll be excited
again, sorry to tell you,

1140
00:47:38,980 --> 00:47:40,210
maybe in a couple of years,

1141
00:47:40,210 --> 00:47:43,333
what the answer to that
question is. (laughs)

1142
00:47:44,513 --> 00:47:46,337
- Maydianne, I'm gonna ask two questions,

1143
00:47:46,337 --> 00:47:48,163
'cause they're related.

1144
00:47:50,360 --> 00:47:51,520
Are there, in a population,

1145
00:47:51,520 --> 00:47:54,750
are there more males
than females, generally?

1146
00:47:54,750 --> 00:47:58,163
And if it's so hard for
males to find a female,

1147
00:47:59,776 --> 00:48:02,750
you know, how often do
they need to compete

1148
00:48:02,750 --> 00:48:03,770
for the same female,

1149
00:48:03,770 --> 00:48:05,920
and do the males often
run into each other?

1150
00:48:07,050 --> 00:48:08,280
- Great.

1151
00:48:08,280 --> 00:48:12,690
So this is a species with
overlapping generations

1152
00:48:12,690 --> 00:48:15,700
that overwinters at multiple
life history stages.

1153
00:48:15,700 --> 00:48:17,010
So that's the jargon side.

1154
00:48:17,010 --> 00:48:18,343
The last jargon side is,

1155
00:48:19,330 --> 00:48:22,540
males are maturing continuously
through the season.

1156
00:48:22,540 --> 00:48:26,010
Females are as well, but
really only one cohort of them,

1157
00:48:26,010 --> 00:48:28,650
so one set, it takes them
a long time to mature.

1158
00:48:28,650 --> 00:48:30,280
So what that means effectively is that

1159
00:48:30,280 --> 00:48:33,500
even if the sex ratio in
each egg sac is 50-50,

1160
00:48:33,500 --> 00:48:35,740
we have males for multiple egg sacs

1161
00:48:35,740 --> 00:48:37,610
over the course of the season maturing,

1162
00:48:37,610 --> 00:48:42,100
during the time that only one
set of females are maturing.

1163
00:48:42,100 --> 00:48:43,480
So the questions about sex ratio

1164
00:48:43,480 --> 00:48:45,670
actually become quite complex.

1165
00:48:45,670 --> 00:48:47,810
Even though males are dying at high rates

1166
00:48:47,810 --> 00:48:49,660
before they reach females' webs,

1167
00:48:49,660 --> 00:48:51,870
there's multiple cohorts maturing.

1168
00:48:51,870 --> 00:48:52,753
And what you end up finding is

1169
00:48:52,753 --> 00:48:54,580
that the sex ratios are actually skewed

1170
00:48:54,580 --> 00:48:56,800
towards males for a big
chunk of the season,

1171
00:48:56,800 --> 00:48:58,650
because they mature so quickly.

1172
00:48:58,650 --> 00:49:02,010
And so you do find really frequently

1173
00:49:02,010 --> 00:49:05,440
that there's multiple males
on females' webs, up to eight,

1174
00:49:05,440 --> 00:49:09,200
but on average in the systems
I've studied, about two,

1175
00:49:09,200 --> 00:49:10,810
and in the black widows we found,

1176
00:49:10,810 --> 00:49:13,310
it's common to find at
least two or three as well.

1177
00:49:15,470 --> 00:49:16,970
So there's lot of competition.

1178
00:49:20,120 --> 00:49:22,883
- The next question I'm gonna ask is,

1179
00:49:24,840 --> 00:49:26,150
have you identified the range

1180
00:49:26,150 --> 00:49:28,960
in which males can be
plastic in their development?

1181
00:49:28,960 --> 00:49:31,320
I imagine there's a point
in the embryonic development

1182
00:49:31,320 --> 00:49:33,540
where plasticity is not possible

1183
00:49:33,540 --> 00:49:35,440
independent of the environmental cues.

1184
00:49:36,640 --> 00:49:37,850
- Yes.

1185
00:49:37,850 --> 00:49:38,730
So you're right.

1186
00:49:38,730 --> 00:49:41,340
That window for plasticity is actually

1187
00:49:41,340 --> 00:49:43,960
a critical part of the equation
that I kind of glossed over.

1188
00:49:43,960 --> 00:49:48,230
We do predict that it would
be only in the last instar.

1189
00:49:48,230 --> 00:49:50,210
So males of invertebrates go through

1190
00:49:50,210 --> 00:49:51,730
multiple instars of growth,

1191
00:49:51,730 --> 00:49:53,490
and this species they shed their skin,

1192
00:49:53,490 --> 00:49:56,000
they grow a bit, they shed
their skin, they grow a bit.

1193
00:49:56,000 --> 00:49:59,470
Males of red backs, it's
usually five or six instars

1194
00:49:59,470 --> 00:50:02,130
or growth stages before
they're sexually mature.

1195
00:50:02,130 --> 00:50:04,280
We've done all these
experiments exposing males

1196
00:50:04,280 --> 00:50:06,320
only during their final instar,

1197
00:50:06,320 --> 00:50:08,120
during that final growth period.

1198
00:50:08,120 --> 00:50:10,940
And the reason for that is
A, it's easier, (laughs),

1199
00:50:11,870 --> 00:50:13,170
but the main reason is

1200
00:50:13,170 --> 00:50:16,410
we, because females are
becoming sexually mature

1201
00:50:16,410 --> 00:50:19,760
and getting mated at a regular
rate through the season,

1202
00:50:19,760 --> 00:50:23,020
if you wanna predict
in evolutionary terms,

1203
00:50:23,020 --> 00:50:25,360
males who pay attention
to those pheromones

1204
00:50:25,360 --> 00:50:26,990
only during the last instar

1205
00:50:26,990 --> 00:50:27,823
will probably do better than males

1206
00:50:27,823 --> 00:50:29,550
that try to integrate the information

1207
00:50:29,550 --> 00:50:31,380
across their entire development,

1208
00:50:31,380 --> 00:50:33,280
because the situation's changing.

1209
00:50:33,280 --> 00:50:34,620
And what we're expecting is

1210
00:50:34,620 --> 00:50:36,550
that it's the cues that
predict what's gonna happen

1211
00:50:36,550 --> 00:50:38,950
when the male becomes sexually mature,

1212
00:50:38,950 --> 00:50:40,810
i. e. within a week or two

1213
00:50:40,810 --> 00:50:44,500
of detecting it that make the difference.

1214
00:50:44,500 --> 00:50:46,710
So the shifts we're talking
about are really on the order

1215
00:50:46,710 --> 00:50:49,460
of only a few days,
but that time is enough

1216
00:50:49,460 --> 00:50:51,450
to allow that male to get to the female,

1217
00:50:51,450 --> 00:50:55,490
mate her, plug her, and make
her unavailable for other males

1218
00:50:55,490 --> 00:50:57,913
before another male who
took longer to develop.

1219
00:51:02,590 --> 00:51:05,110
- The next question is
regarding the hypothesis

1220
00:51:05,110 --> 00:51:07,360
that environmental cuing
comes from pheromones,

1221
00:51:07,360 --> 00:51:09,380
do you have any strains

1222
00:51:09,380 --> 00:51:12,210
in which females are incapable
of producing pheromones

1223
00:51:12,210 --> 00:51:14,850
where you could test
that more specifically?

1224
00:51:14,850 --> 00:51:18,790
- So the pheromone question
is fairly well-established.

1225
00:51:18,790 --> 00:51:23,510
We, so I should have said in more detail

1226
00:51:23,510 --> 00:51:26,020
that experiment involved caging males

1227
00:51:26,020 --> 00:51:28,137
in screen cages with females

1228
00:51:28,137 --> 00:51:30,180
and screen cages surrounding them.

1229
00:51:30,180 --> 00:51:35,180
These spiders don't see,
their vision is poor.

1230
00:51:35,330 --> 00:51:36,540
They can see sort of light and dark,

1231
00:51:36,540 --> 00:51:38,320
but they don't really see images.

1232
00:51:38,320 --> 00:51:40,120
So there's no visual cues.

1233
00:51:40,120 --> 00:51:42,390
They were isolated for
vibrational cues, as well.

1234
00:51:42,390 --> 00:51:44,300
So the only cues reaching the males

1235
00:51:44,300 --> 00:51:45,770
in those contexts were actually

1236
00:51:45,770 --> 00:51:48,910
the airborne pheromones
coming out of the web.

1237
00:51:48,910 --> 00:51:49,743
In addition to that,

1238
00:51:49,743 --> 00:51:52,620
in the field what we can do is
get a female to create a web

1239
00:51:52,620 --> 00:51:55,570
inside a screen cage, take the female out,

1240
00:51:55,570 --> 00:51:58,610
and then you put that cage in
the field and males show up.

1241
00:51:58,610 --> 00:52:01,560
In fact, the first time we were
working on Northern widows,

1242
00:52:01,560 --> 00:52:02,730
we weren't sure if we were in a field

1243
00:52:02,730 --> 00:52:05,840
with a lot of spiders, and
we had to carry a bag full

1244
00:52:05,840 --> 00:52:06,680
for our field work

1245
00:52:06,680 --> 00:52:08,830
of these screen cages that
had no females in them,

1246
00:52:08,830 --> 00:52:11,050
just the silk, and the males were coming,

1247
00:52:11,050 --> 00:52:13,710
as we were going out, trying
to put the cages down.

1248
00:52:13,710 --> 00:52:15,610
So there, it's quite remarkable,

1249
00:52:15,610 --> 00:52:18,310
and well-established that
the pheromone key was there.

1250
00:52:18,310 --> 00:52:19,860
And in that experiment for development,

1251
00:52:19,860 --> 00:52:21,510
that was the only cue available to male,

1252
00:52:21,510 --> 00:52:22,570
the different males that differ

1253
00:52:22,570 --> 00:52:24,973
between the two experimental contexts.

1254
00:52:28,980 --> 00:52:32,600
- The next question I'm gonna ask

1255
00:52:32,600 --> 00:52:35,083
is a compilation of several.

1256
00:52:36,460 --> 00:52:40,163
One is, how many offspring
can a spider have?

1257
00:52:41,150 --> 00:52:43,320
And there've been several people,

1258
00:52:43,320 --> 00:52:46,830
why is it that male widows
have a shorter life span

1259
00:52:46,830 --> 00:52:47,870
than female widows,

1260
00:52:47,870 --> 00:52:49,783
even without being eaten by a female?

1261
00:52:51,670 --> 00:52:53,650
- So how many offspring can they have?

1262
00:52:53,650 --> 00:52:55,180
Females can produce,

1263
00:52:55,180 --> 00:52:56,720
depending on the species of black widow,

1264
00:52:56,720 --> 00:53:00,160
100 to 400 eggs per egg sac,

1265
00:53:00,160 --> 00:53:02,110
that all hatch into spiderlings.

1266
00:53:02,110 --> 00:53:04,700
And they can do that once every two weeks,

1267
00:53:04,700 --> 00:53:07,200
if they're well-fed for two years.

1268
00:53:07,200 --> 00:53:09,050
So I won't do the calculus on the fly,

1269
00:53:09,050 --> 00:53:10,690
but it's a lot of offspring.

1270
00:53:10,690 --> 00:53:12,920
That's also why some of
these species are invasive.

1271
00:53:12,920 --> 00:53:16,090
So if you have one mated
female showing up someplace,

1272
00:53:16,090 --> 00:53:18,993
they can churn out thousands
and thousands of babies.

1273
00:53:20,160 --> 00:53:23,010
Now, the other question
was, why this difference?

1274
00:53:23,010 --> 00:53:24,990
So that's a whole
another set of questions.

1275
00:53:24,990 --> 00:53:25,853
I love that.

1276
00:53:25,853 --> 00:53:26,900
Thank you for that question.

1277
00:53:26,900 --> 00:53:30,200
So these species are
extremely sexually dimorphic.

1278
00:53:30,200 --> 00:53:32,660
Sexual dimorphism of this
type, sexual size dimorphism,

1279
00:53:32,660 --> 00:53:34,247
when you say very large females,

1280
00:53:34,247 --> 00:53:36,350
and very small males are the subject

1281
00:53:36,350 --> 00:53:38,840
of a whole another field of study.

1282
00:53:38,840 --> 00:53:40,390
You see that in other species as well,

1283
00:53:40,390 --> 00:53:42,980
but it reaches, I think
its Zenith probably

1284
00:53:42,980 --> 00:53:45,900
in spiders, and
essentially they're adapted

1285
00:53:45,900 --> 00:53:47,510
for different types of life history.

1286
00:53:47,510 --> 00:53:50,990
So some people call males sperm on legs,

1287
00:53:50,990 --> 00:53:54,300
partly because the fact that
you have scramble competition

1288
00:53:54,300 --> 00:53:56,790
and that males who get there
earlier will do better.

1289
00:53:56,790 --> 00:53:59,990
Males have not evolved
to spend the long time

1290
00:53:59,990 --> 00:54:01,990
having huge body size.

1291
00:54:01,990 --> 00:54:03,240
Females on the other hand,

1292
00:54:03,240 --> 00:54:05,250
their body size is tightly correlated

1293
00:54:05,250 --> 00:54:07,040
with how many eggs they can produce.

1294
00:54:07,040 --> 00:54:09,920
So in terms of evolutionary
fitness or Darwinian fitness,

1295
00:54:09,920 --> 00:54:14,550
females who are able to be grow
to be large enough as adults

1296
00:54:14,550 --> 00:54:16,520
sufficiently large to produce more eggs

1297
00:54:16,520 --> 00:54:18,670
will do better than females that let's say

1298
00:54:18,670 --> 00:54:20,070
had shorter life history.

1299
00:54:20,070 --> 00:54:22,870
So in terms of the evolution
of these life history patterns

1300
00:54:22,870 --> 00:54:24,800
how much time you spend developing

1301
00:54:24,800 --> 00:54:26,010
when you become sexually mature,

1302
00:54:26,010 --> 00:54:28,020
how many offspring you can produce,

1303
00:54:28,020 --> 00:54:30,300
males and females are
just radically different.

1304
00:54:30,300 --> 00:54:32,270
And in fact, sorry, I'm
gonna go say one more thing,

1305
00:54:32,270 --> 00:54:33,440
in the early literature,

1306
00:54:33,440 --> 00:54:35,530
they didn't know the males
were the same species.

1307
00:54:35,530 --> 00:54:37,280
There's really early literature on spiders

1308
00:54:37,280 --> 00:54:38,497
where like "There's this big spider,

1309
00:54:38,497 --> 00:54:39,827
"then there's all these little things

1310
00:54:39,827 --> 00:54:41,480
"hanging around on web."

1311
00:54:41,480 --> 00:54:43,427
And they weren't sure at
first it was the same species,

1312
00:54:43,427 --> 00:54:46,360
those are some great early work that says

1313
00:54:46,360 --> 00:54:48,110
yeah, in fact, those are the males.

1314
00:54:50,830 --> 00:54:54,080
- I'm gonna double up on
the next question, as well.

1315
00:54:56,251 --> 00:54:58,200
Does cannibalistic behavior occur

1316
00:54:58,200 --> 00:55:00,920
in invertebrates outside
of the arthropods,

1317
00:55:00,920 --> 00:55:03,930
and then can you describe the process

1318
00:55:03,930 --> 00:55:06,683
of capturing the spiders
and how the traps work?

1319
00:55:08,580 --> 00:55:09,413
- Yeah.

1320
00:55:09,413 --> 00:55:11,960
So does sexual cannibalism occur

1321
00:55:11,960 --> 00:55:15,943
outside of insects and spiders?

1322
00:55:17,019 --> 00:55:19,090
(laughs)

1323
00:55:19,090 --> 00:55:20,120
Not in this form.

1324
00:55:20,120 --> 00:55:23,410
There's the odd anecdotal
report in other groups,

1325
00:55:23,410 --> 00:55:27,990
there is a similar sort of
bifurcation or shift difference

1326
00:55:27,990 --> 00:55:30,880
between male and female
in some other species.

1327
00:55:30,880 --> 00:55:32,890
So one of the classic
examples I often talk about

1328
00:55:32,890 --> 00:55:35,850
is a little marsupial
mouse, a little mammal,

1329
00:55:35,850 --> 00:55:39,090
that Australians
endearingly called dibblers,

1330
00:55:39,090 --> 00:55:40,610
but they're actually
called Apicalis, I guess,

1331
00:55:40,610 --> 00:55:41,800
is the Latin name,

1332
00:55:41,800 --> 00:55:43,790
in this species there's
no sexual cannibalism,

1333
00:55:43,790 --> 00:55:45,320
but in terms of one thing being

1334
00:55:45,320 --> 00:55:47,350
males being evolved for short lifespans

1335
00:55:47,350 --> 00:55:50,150
and females for relatively long lifespans,

1336
00:55:50,150 --> 00:55:52,950
the males of these species mate

1337
00:55:52,950 --> 00:55:54,700
in over the course of one season only,

1338
00:55:54,700 --> 00:55:57,150
whereas females can survive
for more than one season.

1339
00:55:57,150 --> 00:56:00,120
And during that season,
the males actually engage

1340
00:56:00,120 --> 00:56:03,960
in such pitched competition
because of sperm competition,

1341
00:56:03,960 --> 00:56:08,860
that they shift all their
resources into producing sperm

1342
00:56:08,860 --> 00:56:11,953
into competing for females,

1343
00:56:13,250 --> 00:56:14,590
their hair drops out,

1344
00:56:14,590 --> 00:56:15,840
they're riddled with parasites,

1345
00:56:15,840 --> 00:56:18,470
because they shift energy
away from their immune system,

1346
00:56:18,470 --> 00:56:19,810
and they die at the end of the season,

1347
00:56:19,810 --> 00:56:21,070
whereas females are longer lived.

1348
00:56:21,070 --> 00:56:22,310
So it's not exactly the same,

1349
00:56:22,310 --> 00:56:24,690
but this idea of investment,
differential investment

1350
00:56:24,690 --> 00:56:26,770
is seen elsewhere.

1351
00:56:26,770 --> 00:56:28,250
And then I forgot the other question,

1352
00:56:28,250 --> 00:56:30,152
'cause now I'm thinking of
these poor little marsupials.

1353
00:56:30,152 --> 00:56:30,985
(laughs)

1354
00:56:30,985 --> 00:56:34,477
- Yeah, do fields capture --

1355
00:56:34,477 --> 00:56:35,710
- Oh yeah.

1356
00:56:35,710 --> 00:56:37,120
- That's where for the spiders.

1357
00:56:37,120 --> 00:56:38,520
- If I'd had a wee bit more time,

1358
00:56:38,520 --> 00:56:41,420
sometimes I just show like
five slides on the stuff we do

1359
00:56:41,420 --> 00:56:42,447
in my lab 'cause people are always like

1360
00:56:42,447 --> 00:56:44,130
"How do you work on spiders?"

1361
00:56:44,130 --> 00:56:47,070
So females can go into these screen cages,

1362
00:56:47,070 --> 00:56:50,040
the silk that they produce
is laden with pheromones

1363
00:56:50,040 --> 00:56:51,700
and produces airborne pheromones,

1364
00:56:51,700 --> 00:56:54,760
so again, we can make basically
a pheromone trap for males

1365
00:56:54,760 --> 00:56:56,880
by just having females build dense webbing

1366
00:56:56,880 --> 00:56:58,490
inside a screen box,

1367
00:56:58,490 --> 00:57:00,190
putting that box out in the field.

1368
00:57:01,090 --> 00:57:02,840
You can then, in our
first round of doing this,

1369
00:57:02,840 --> 00:57:05,950
we actually used sticky traps
around those dense boxes

1370
00:57:05,950 --> 00:57:07,030
so when males are drawn

1371
00:57:07,030 --> 00:57:08,250
and they were stuck to the traps,

1372
00:57:08,250 --> 00:57:10,340
and we could measure them and things,

1373
00:57:10,340 --> 00:57:11,173
we felt terrible,

1374
00:57:11,173 --> 00:57:12,370
'cause a lot of the males died,

1375
00:57:12,370 --> 00:57:14,340
and it turns out that if
you don't have the traps,

1376
00:57:14,340 --> 00:57:16,200
Sheena Fry, my student discovered this,

1377
00:57:16,200 --> 00:57:18,120
the males would climb on
the cage and just sit there,

1378
00:57:18,120 --> 00:57:19,074
like, "Where is my female?"

1379
00:57:19,074 --> 00:57:19,907
(laughs)

1380
00:57:19,907 --> 00:57:21,670
So you could actually just pluck them off,

1381
00:57:21,670 --> 00:57:23,930
and go regularly and
collect them that way.

1382
00:57:23,930 --> 00:57:26,600
We can also and we do mark female webs

1383
00:57:26,600 --> 00:57:28,440
in the field because
females are sedentary.

1384
00:57:28,440 --> 00:57:30,480
They typically don't move
once they're sexually mature.

1385
00:57:30,480 --> 00:57:31,640
Even before that.

1386
00:57:31,640 --> 00:57:33,343
And then we can mark females and males

1387
00:57:33,343 --> 00:57:35,440
with paint colors on their legs,

1388
00:57:35,440 --> 00:57:39,000
so when we're doing these actives
surveys of male movements,

1389
00:57:39,000 --> 00:57:42,210
we all collect males, take
them to the lab, weigh them,

1390
00:57:42,210 --> 00:57:43,720
take pictures so we can measure them,

1391
00:57:43,720 --> 00:57:46,270
and use tiny drops of
paint on their eight legs

1392
00:57:46,270 --> 00:57:47,810
by four or five different colors,

1393
00:57:47,810 --> 00:57:50,210
gives you a lot of different
you know, right leg blue,

1394
00:57:50,210 --> 00:57:52,740
left leg, white, whatever codes.

1395
00:57:52,740 --> 00:57:55,610
And so we know males, not by
name, but by their paint codes.

1396
00:57:55,610 --> 00:57:57,010
And so if we find them later on

1397
00:57:57,010 --> 00:57:58,320
in another females web, we'll know,

1398
00:57:58,320 --> 00:58:00,100
okay, this male move from web number five,

1399
00:58:00,100 --> 00:58:01,400
which has been there for three months

1400
00:58:01,400 --> 00:58:02,880
and has a female who's immature,

1401
00:58:02,880 --> 00:58:05,210
and moved to this female, who
just became sexually mature,

1402
00:58:05,210 --> 00:58:07,210
and they moved this distance, et cetera.

1403
00:58:09,890 --> 00:58:13,563
- And I would like to end
with this question if we may,

1404
00:58:14,420 --> 00:58:17,970
and this is from a friend of,

1405
00:58:17,970 --> 00:58:19,890
or a person from the English Department

1406
00:58:19,890 --> 00:58:21,842
at University of Waterloo, just to --

1407
00:58:21,842 --> 00:58:22,675
- Oh, hi.

1408
00:58:22,675 --> 00:58:24,179
So --
- Hello, Canadian.

1409
00:58:24,179 --> 00:58:25,567
(laughter)

1410
00:58:25,567 --> 00:58:28,210
Do you have any suggestions
for getting kids of color,

1411
00:58:28,210 --> 00:58:29,920
especially in developing countries

1412
00:58:29,920 --> 00:58:31,853
interested in the area of evolution?

1413
00:58:33,210 --> 00:58:37,260
- Yeah, I think it
depends on understanding

1414
00:58:37,260 --> 00:58:39,350
what's interesting to them
in their context, right?

1415
00:58:39,350 --> 00:58:41,260
And in fact, I'll just
put in a plug right now

1416
00:58:41,260 --> 00:58:43,290
for Black and Entomology, Black and ento,

1417
00:58:43,290 --> 00:58:45,950
we're gonna have a week
at the end of the month,

1418
00:58:45,950 --> 00:58:48,100
we actually talk a lot about the fact that

1419
00:58:48,100 --> 00:58:51,260
there's a lot of cultural knowledge

1420
00:58:51,260 --> 00:58:55,500
in places where people tend to
be a little bit more in touch

1421
00:58:55,500 --> 00:58:56,980
with their environment than we are.

1422
00:58:56,980 --> 00:58:59,480
And that as Western scientists,

1423
00:58:59,480 --> 00:59:03,710
as, or developed global North scientists,

1424
00:59:03,710 --> 00:59:04,950
we tend to go into these places

1425
00:59:04,950 --> 00:59:06,650
and tell them what's interesting to study.

1426
00:59:06,650 --> 00:59:08,770
But if instead we ask
what is already there

1427
00:59:08,770 --> 00:59:09,930
that they're interested in,

1428
00:59:09,930 --> 00:59:11,970
there will be evolution
stories in it, right?

1429
00:59:11,970 --> 00:59:16,180
So it's a challenge, but check
out Black and Entomology,

1430
00:59:16,180 --> 00:59:17,820
'cause entomology's one
way to get them into it.

1431
00:59:17,820 --> 00:59:19,060
They're cheap to work on,

1432
00:59:19,060 --> 00:59:20,060
they're plentiful.

1433
00:59:20,060 --> 00:59:22,760
You can do observational
and experimental work,

1434
00:59:22,760 --> 00:59:25,280
and they are great models for evolution.

1435
00:59:25,280 --> 00:59:26,830
So thanks for ending with that.

1436
00:59:29,240 --> 00:59:32,950
- So I'll turn it over to
my colleague, Dr. Burchsted.

1437
00:59:38,260 --> 00:59:40,470
- That was absolutely fascinating.

1438
00:59:40,470 --> 00:59:43,040
I can't thank you enough for being here,

1439
00:59:43,040 --> 00:59:48,040
and clearly stimulating our
audience with lots of questions,

1440
00:59:49,160 --> 00:59:52,380
and lots of information and
lots of evolutionary thinking.

1441
00:59:52,380 --> 00:59:55,280
So thank you, Dr. Andrade for coming

1442
00:59:55,280 --> 00:59:57,720
to give our founders' lecture

1443
00:59:57,720 --> 01:00:00,583
at this 42nd Annual Darwin Festival.

1444
01:00:01,940 --> 01:00:04,740
- Thank you for inviting me.

1445
01:00:04,740 --> 01:00:09,110
- I second that, and this ends
our presentation for today.

1446
01:00:09,110 --> 01:00:11,030
I wanna encourage folks to come

1447
01:00:11,030 --> 01:00:15,120
to subsequent talks for
the Darwin Festival.

1448
01:00:15,120 --> 01:00:17,580
The next one will be
at 11 o'clock tomorrow.

1449
01:00:17,580 --> 01:00:18,630
Thanks so much.

1450
01:00:18,630 --> 01:00:19,463
Bye-bye.