Whales Are Sprinters: What a Blue Whale's Heart Does When It Lunge Feeds

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What happens to a whale's
heart rate when it lunge feeds?

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That's our topic for today's episode
when we talk to Ashley Blawas from the

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Hopkins Marine Center at Stanford at
the Doerr School of Sustainability.

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She's on the podcast.

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She's doing her postdoc on this type
of subject, measuring the heart rates

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of blue whales and humpback whales
to see how the heart rate is affected

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when they actually lunge feed.

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She goes into about her career and how
she got to where she is right now, how

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she started as a biomedical engineer and
is now studying the physiology of some

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of the biggest animals on the planet.

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Uh, and, uh, she also talks about,
like, how lunge feeding works.

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There's three parts.

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It's really cool to see how it works
and to see how these animals are taking

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advantage of these prey patches that
they want to eat, of krill and maybe

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depending on, the animal, depending
on the marine mammal, these, uh,

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small fish, prey patches as well.

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This is a really interesting topic
that we haven't touched on at all

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yet, on the podcast out of over
2,000 episodes that we've done here.

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So sharing this type of science has
benefits to even human physiology

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and understanding human physiology.

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Apparently, whales can really
change their heart rate from,

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like, 120 beats per minute to 40
beats per minute in two beats.

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That's pretty phenomenal, and it
doesn't really affect them from a health

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perspective that we've found so far.

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Imagine a human doing that.

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That would not be good.

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So it's kind of cool to hear the
differences and, how these animals work,

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uh, especially from a physiological
standpoint and a scientific standpoint.

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So thank you, Ashley, for bringing that.

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So here is the interview.

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We're talking all about blue
whales, humpback whales, and lunge

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feeding and their heart rates.

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Enjoy, and I'll talk to you after.

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Hey, Ashley.

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Welcome to the How to
Protect the Ocean podcast.

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Are you ready to talk about
lunge feeding in whales?

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Thanks, Andrew, for having me.

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I'm super excited.

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Can't wait to get into it.

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All right.

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I, I, you know, I have to admit, I don't
think that's ever on this episode come out

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of my mouth, where I said, "Are we ready
to talk about lunge feeding in whales?"

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But it's always something
I've been interested in.

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Because I've seen, you
know, studies on it.

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I've seen people talking about it.

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Um, and then you watch it happen,
like, you know, with these now

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that with drones and, aerial
photography and stuff like that.

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And you watch these, blue whales and
sperm whales and humpback whales and

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all these, like, massive whales just
kinda be able to just carry themselves,

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just even in the water, and then
be able to do these lunge feeding.

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And I just think it's awesome, and I can't
wait to be able to talk about it with you.

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But before we get into that,
Ashley, why don't you just let us

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know who you are and what you do?

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Yeah, sure.

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So my name is Ashley Blawas, and
I am a postdoctoral researcher

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in Jeremy Goldbogen's group.

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We are based at Hopkins Marine
Station, which is affiliated

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with Stanford University.

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And, um, basically, I'm a physiologist,
and I'm motivated to ask and answer

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questions about how animals work,
and primarily I work in big whales.

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And so those are the rorqual
whales, which include blue whales,

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fin whales, humpback whales.

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And mostly I'm interested in heart
rate, and so that's been sort of the

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latest and greatest of my research.

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I love it.

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I love it.

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So post-docs, that means you've been
through PhD. Did you do a master's

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or did you go straight to a PhD?

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I went straight from undergrad to the PhD.

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Okay.

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Amazing.

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before we get into that part, what
got you interested just in the ocean

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in general and wanting to be a marine
scientist or marine physiologist?

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so I grew up in central North Carolina and
spent every summer vacationing for like

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a week or two with family at the beach.

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And I think without knowing it, that
was probably where my first interests

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you know, the seed got planted.

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But, um, then growing up and sort
of having that as a part of my

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life and really valuing, you know,
not just the time spent like in

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an environment like that, but how
unique it is to be able to access the

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water, see the animals in the water.

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uh, yeah, I think honestly
it started there with beach

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vacations, and then sort of- Yeah
… tumbleweeded into where I am now.

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I love it.

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Now, you went to Duke University,
uh, obviously, and that makes

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sense because you're so close in
North Carolina, like in that area.

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now you, got your undergrad
in biomedical engineering.

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I guess not too far off in terms
of, looking at physiology as

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you get older, but what made you
go into biomedical engineering?

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When I was in high school, I really
liked science and I really liked math.

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and so I kind of, going into college,
I thought engineering could be a

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nice way to fuse those two interests.

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but I also kind of had this part
of me that was really interested

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in, you know, specifically like how
things work under the hood, which is

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a lot of what engineers do, right?

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But I think I was most interested in
applying that to biological systems, which

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is really right where physiology falls.

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So, when I was going to college, I
knew I wanted to focus on engineering,

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but biomedical engineering was this
sort of, you know, perfectly, designed

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major for what I wanted to do, which
is like how do things work, but how do

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things work biologically rather than
maybe understanding like the electrical

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or mechanical systems behind them.

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Yeah.

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and I, I'm glad you brought that up
because when I hear of engineer, when

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I think of engineering, you think
of the mechanical, the electronical,

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even the software engineers.

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Right.

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You don't hear a lot of people
go into, you know, biomedical

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engineering, but it makes sense, right?

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Obviously, there's a lot of people who do.

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It doesn't get talked about a lot,
sort of like in the mainstream

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conversations, I should say.

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maybe it's just my
conversations or I don't know.

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But it seems like engineers are more
focused on like you have mechanical,

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civil, you know, you have those engineers.

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what type of career could you have,
you know, other than one that you took?

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Like what were you thinking as you
went through this of like, as you

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go through those careers, what kind
of careers could someone with a

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biomedical engineering degree have?

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Yeah.

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So one of the main ones I would say
that sort of I was, in the track

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for was biomedical device design.

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That's a lot of what- folks who are
getting this kind of training do,

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is how do we build the systems that
measure the physiology, that we're

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interested in in a clinical setting.

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But also a lot of folks who are
pursuing med school, a lot of folks

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who are pursuing like graduate level
education in biology or staying in

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that biomedical engineering track.

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But I think, to be honest, you
know, it's a lot of having this

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like problem-solving mindset-

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Mm-hmm

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…
and applying that to how we
learn more about biology.

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So, you know, there are definitely
the folks who are gonna build systems

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that are electrical and mechanical
and- Of course … and use those.

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but there are also folks who are
just, you know, thinking about how

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do we do engineering, but how do we
do it maybe on the human body, Yeah

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rather than, a lab setting.

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I,

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I picture Rocky III.

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You know, where they have the Russian
boxer who's got… Like he's on the

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treadmill, and he's got everything
like, you know, hooked up to him,

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all the pads hooked up to him so
they were looking at heart rate.

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I'm thinking of when you mentioned
like coming up with the devices to

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measure all like the physiological
system and how it responds to

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probably like stimuli and stuff.

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And then it goes to like Rocky
trying to catch a chicken in a farm

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somewhere- … in the, in the winter.

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this is where my mind is going of like,
'cause like this is what I think about

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when I think of biomedical engineering.

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And I'm sure a lot of it was catered
toward human biology, I assume- Right

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like in that program.

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Was there ever a time where you
started to deviate from that and

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start to think of, oh, well, there's
mammals here as humans, but then there

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are other mammals as other animals?

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Did you ever think… Like was
that when that started to deviate?

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Or when did that hit?

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Yeah.

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So I had, as an sort of early college
student, always been interested in,

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um, visiting the Duke Marine Lab
while I was at Duke for undergrad.

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So Duke has this campus down by
the coast that you can go and do,

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quote-unquote, "a study abroad semester,"
though you're not going abroad, there.

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And that actually happened to be
in the town that I had, vacationed

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in growing up with family.

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Oh.

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And so when I got in to Duke,
part of me was really excited.

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Wow, I could actually go and
take classes there, this place

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that I've known for so long.

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So I had always sort of planned that
I'd like to incorporate time at the

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Duke Marine Lab into my engineering
curriculum, and I was able to do that,

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and met a faculty member there who
ended up being my PhD advisor, who was

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actually doing this exact thing, which
is that he was trained as a biologist,

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in his undergraduate, and then trained
as an engineer in his graduate training.

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Mm-hmm.

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And then building tools to
study animals in the ocean.

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And that was, I think,
when it clicked for me.

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oh, wait, actually maybe my sort of,
otherness in the sense that I'm an

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engineer playing marine scientist,
could actually come in handy here.

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And some of the things that I think about
building or for understanding humans might

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be relevant in a way that I didn't- Yeah

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think of before.

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That's really cool.

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So I love it.

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It's out of curiosity of just being in
the town that you used to vacation in.

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You're gonna go and do, like, a
semester just to see what's there.

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Then you meet somebody there that is
doing something very similar to what you

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wanted to do, but you never really thought
about from an animal or ocean animal

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perspective at that point until you met…

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Who, who was it, sorry?

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This was, my PhD advisor,
Dr. Doug Nowacek.

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Okay, cool.

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Mm-hmm.

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So you d- you hadn't really thought
about animals until, like, or

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doing what you were, gonna do and
applying it to animals at that point?

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Not really.

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I think I almost saw it as, like,
a two disparate paths of my career.

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It was like- Right … "Okay, I'm an
engineer." And then, every once in

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a while go to the beach and, like,
play this marine scientist role.

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and I think it was when, yeah, really
seeing him and his expertise that

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I realized those two things could
synergize in an interesting way.

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I love it.

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I think that's great.

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I mean, so what a way to explore, too.

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Um, even just natural curiosity
coupled with sort of, like,

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childhood feelings, you know,
about being- Yeah … in that town.

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But then just being like, "Oh, this would
be kinda cool." And then it ends up kinda

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changing your life in a way of, like,
or the path of your life, in that way.

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Right.

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That's amazing.

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Totally.

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Yeah.

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That's, that's awesome.

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And so your PhD was on that type of work.

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Can you kinda go into a little
bit about what you were doing?

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Yeah, sure.

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So then sort of for the last two years
of undergrad, I really started to orient

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towards, you know, how could I apply
these engineering skills towards building,

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for lack of a better term, biomedical
devices for, um, for marine mammals.

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Mm-hmm.

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And one of the courses that I got to take
while I was at the Duke Marine Lab was,

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uh, the biology of marine mammals, and
that was where I first learned about these

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bio logging tags that are so standard
in the field, that I'm working in now.

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Um- But so I was exposed to this in the
last couple years of undergrad, pursued

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a couple opportunities in the summers
to, get exposure to the field more

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like out of the classroom and- Mm-hmm

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in a research setting.

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And so I got the chance to work
with Dr. Andres Folman, who's a

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comparative physiologist that's
worked with like any marine system

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you can name, he's worked with it.

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He's worked with penguins, he's
worked with Steller sea lions, he's

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00:10:38,262 --> 00:10:43,092
worked with whales, and, he let me
sort of be a, you know, sort of an

231
00:10:43,102 --> 00:10:47,012
undergraduate volunteer on a project,
to look at dolphin physiology.

232
00:10:47,072 --> 00:10:52,292
And yeah, that really led
me to pursuing, the graduate

233
00:10:52,292 --> 00:10:54,202
research with Doug as my advisor.

234
00:10:54,592 --> 00:11:00,262
And for that work, I studied both
dolphin and pilot whale physiology.

235
00:11:00,262 --> 00:11:03,442
Those are two fairly related species.

236
00:11:03,542 --> 00:11:05,522
the pilot whales are much
better divers than the dolphins.

237
00:11:05,542 --> 00:11:10,052
But, um, my graduate work was basically
on diving physiology of these animals.

238
00:11:10,152 --> 00:11:10,272
Mmm.

239
00:11:10,282 --> 00:11:14,352
What are the specifically cardiac
and respiratory adaptations that they

240
00:11:14,352 --> 00:11:17,492
have to hold their breath underwater
for extended periods of time?

241
00:11:17,622 --> 00:11:20,242
So I did that in a couple different
ways, but that was the broad theme of it.

242
00:11:20,542 --> 00:11:21,252
yeah, so I spent-

243
00:11:21,252 --> 00:11:21,782
That's crazy

244
00:11:22,214 --> 00:11:25,151
Four and a half-ish really fun
years working on that- Yeah

245
00:11:25,151 --> 00:11:25,768
before the postdoc.

246
00:11:26,828 --> 00:11:27,768
That's amazing.

247
00:11:27,808 --> 00:11:30,688
and to think that hasn't really been
studied that much before, I assume,

248
00:11:30,688 --> 00:11:33,188
like in terms of the adaptations
for these different whales or these

249
00:11:33,188 --> 00:11:34,388
different, like dolphins and stuff?

250
00:11:35,178 --> 00:11:35,248
Yeah.

251
00:11:35,448 --> 00:11:39,398
I mean, I would say there are definitely
experts who've dedicated full careers

252
00:11:39,398 --> 00:11:44,258
to this field, but I think that, it
wasn't really till the 1940s that

253
00:11:44,258 --> 00:11:48,788
you see the first publications on
these animals, in terms of like how

254
00:11:48,788 --> 00:11:51,388
do they hold their breath and what
might be going on under the hood- Mm

255
00:11:51,388 --> 00:11:52,688
that's different than humans.

256
00:11:53,208 --> 00:11:54,318
Um, so yeah.

257
00:11:54,318 --> 00:11:57,258
I mean, there's only been, you
know, a handful of generations of

258
00:11:57,278 --> 00:11:58,478
people doing this kind of work.

259
00:11:58,548 --> 00:12:02,548
Mm. So there's still quite a bit that's
new and that we haven't learned yet.

260
00:12:03,011 --> 00:12:07,008
It's amazing, though, how much
we can still find out about, you

261
00:12:07,008 --> 00:12:08,218
know, just whales in general.

262
00:12:08,218 --> 00:12:10,418
Like you'd think everybody loves whales.

263
00:12:10,478 --> 00:12:13,948
We pretty much know everything we do, but
no, there's still so much we can find out.

264
00:12:13,948 --> 00:12:16,724
I find the same thing when we
do the Beyond GE Shark podcast.

265
00:12:16,724 --> 00:12:20,564
Like we know very little about sharks-
Yeah … in the world compared to what we

266
00:12:20,564 --> 00:12:23,224
can find out, and we're always finding new
ones, and we're always finding new things.

267
00:12:23,564 --> 00:12:26,094
It's the same, I feel the same
thing with marine mammals is

268
00:12:26,364 --> 00:12:26,824
just- Totally

269
00:12:26,824 --> 00:12:27,814
…
you just, you never know, right?

270
00:12:28,064 --> 00:12:28,414
Yeah.

271
00:12:28,744 --> 00:12:28,784
Re- Um,

272
00:12:28,844 --> 00:12:29,314
which is awesome … is

273
00:12:29,314 --> 00:12:30,404
it… Yeah.

274
00:12:30,404 --> 00:12:34,584
It's awesome, and then it's an exciting
part of the job is that often- Yeah we

275
00:12:34,584 --> 00:12:38,934
might put one of these bio logging tags
out that we use, and chances are you're

276
00:12:38,934 --> 00:12:42,284
probably gonna see something you've
never seen before just because- Mm

277
00:12:42,654 --> 00:12:45,394
there's not that much data of this type.

278
00:12:45,824 --> 00:12:46,078
Yeah … yeah.

279
00:12:46,078 --> 00:12:49,204
And I, I mean, you know, something
we'll get to later, but like

280
00:12:49,214 --> 00:12:50,724
something as basic as heart rate.

281
00:12:50,814 --> 00:12:52,574
You know, we don't even know-
Yeah … the heart rates of these

282
00:12:52,574 --> 00:12:57,764
animals, which is like such a basic
measurement, uh- Yeah … for people.

283
00:12:57,804 --> 00:12:59,248
So yeah, it's an exciting- But-

284
00:12:59,248 --> 00:13:00,004
field to work in, for sure.

285
00:13:00,004 --> 00:13:03,364
And if you think about it, it's like how
do you get a heart rate from… Like it's

286
00:13:03,364 --> 00:13:06,624
not as if you can have a stethoscope and
just kinda swim as they're diving down.

287
00:13:06,654 --> 00:13:07,664
Like that's not happening.

288
00:13:07,994 --> 00:13:08,774
Um, so yeah.

289
00:13:08,774 --> 00:13:10,874
So there's obviously
challenges, uh, with that.

290
00:13:10,874 --> 00:13:15,554
Can I ask from your PhD, what's
like something that you figured

291
00:13:15,554 --> 00:13:17,674
out that has stuck with you today?

292
00:13:18,357 --> 00:13:19,327
That's a good question.

293
00:13:19,387 --> 00:13:27,677
I would say, I think probably the main
takeaway from my graduate research

294
00:13:27,677 --> 00:13:32,827
that I still really look back to is
that, the dive response, which is sort

295
00:13:32,827 --> 00:13:39,230
of the, this is the main adaptation
that we think of when we think of, you

296
00:13:39,230 --> 00:13:42,690
know, how is a blue whale going down
and performing exercise underwater.

297
00:13:42,920 --> 00:13:45,140
It all comes back to this
idea of a dive response.

298
00:13:45,690 --> 00:13:50,080
And I think one of the things that
I learned is it, it is so complex-

299
00:13:50,090 --> 00:13:51,430
Mm-hmm … as a graduate student.

300
00:13:51,720 --> 00:13:56,040
and so one of the papers that
I wrote was looking at how

301
00:13:56,050 --> 00:13:58,000
breathing alone affects heart rate.

302
00:13:58,360 --> 00:13:58,780
Mm-hmm.

303
00:13:59,170 --> 00:14:03,440
in dolphins, if they're just sitting
at the surface and breathing, just the

304
00:14:03,440 --> 00:14:08,490
act of breathing itself can vary their
heart rate dramatically from, say, 120

305
00:14:08,490 --> 00:14:13,300
beats per minute, during the breath
down to, like, 40 beats per minute- Wow

306
00:14:13,350 --> 00:14:16,070
in the period between breaths, which
is really dramatic, and that's not

307
00:14:16,070 --> 00:14:19,200
something- Yeah … as humans… You know,
actually, that phenomenon does happen

308
00:14:19,200 --> 00:14:21,160
in humans, but we don't perceive it.

309
00:14:21,190 --> 00:14:23,510
It's happening to a
much, uh, smaller degree.

310
00:14:23,930 --> 00:14:28,880
And I think just that, how complex and
how variable this sort of physiology

311
00:14:28,880 --> 00:14:32,641
can be was, quite fascinating
and has definitely stayed with me

312
00:14:33,071 --> 00:14:35,821
through- I feel like if it was a human
that was doing that, you would not be

313
00:14:35,821 --> 00:14:37,101
in good health if you were- It's true

314
00:14:37,101 --> 00:14:41,301
going from, like, 120 down to 40 and then
back up to 120, you know, so forth, right?

315
00:14:41,301 --> 00:14:41,791
With every breath.

316
00:14:42,270 --> 00:14:42,700
Yeah.

317
00:14:42,700 --> 00:14:44,100
It's- So that's-- Oh,
that's really interesting.

318
00:14:44,171 --> 00:14:44,671
Good for you.

319
00:14:45,001 --> 00:14:46,491
Uh, and now you're at a postdoc.

320
00:14:46,550 --> 00:14:50,100
did you go right from your PhD
to Stanford, uh, as a postdoc?

321
00:14:50,100 --> 00:14:52,065
Is this your first postdoc
after that, or did you do one

322
00:14:52,065 --> 00:14:52,148
before?

323
00:14:52,148 --> 00:14:52,550
I did.

324
00:14:53,130 --> 00:14:53,460
Yeah.

325
00:14:53,460 --> 00:14:55,150
So this is my first postdoc after that.

326
00:14:55,150 --> 00:15:00,170
So I, I finished my PhD, early
2023, and then made the move

327
00:15:00,170 --> 00:15:03,400
from North Carolina out here to
California and started the postdoc.

328
00:15:03,720 --> 00:15:04,080
Okay.

329
00:15:04,090 --> 00:15:04,610
Interesting.

330
00:15:04,610 --> 00:15:07,447
Before we go on the postdoc,
you did about four and a half

331
00:15:07,467 --> 00:15:10,107
years, finished early 2023.

332
00:15:10,637 --> 00:15:17,337
How did COVID affect your PhD because,
like, I don't know, I know the States

333
00:15:17,337 --> 00:15:20,977
didn't close down as much, but, you
know, that must have impacted it.

334
00:15:20,977 --> 00:15:23,917
Did it impact it any, kinda change the
way- Oh, definitely … what you were,

335
00:15:23,997 --> 00:15:26,837
you were… 'Cause I know with a lot of
people who started graduate programs,

336
00:15:26,837 --> 00:15:28,387
you probably started, what, 2019?

337
00:15:28,897 --> 00:15:29,427
Is that when you started?

338
00:15:29,427 --> 00:15:29,567
Yeah,

339
00:15:29,567 --> 00:15:30,337
2018. Yep.

340
00:15:30,397 --> 00:15:34,587
2018. So starting in 2018, hitting
that, like, for maybe a year and

341
00:15:34,587 --> 00:15:38,537
a, and a- maybe two years, and then
hitting that, that pandemic March,

342
00:15:38,577 --> 00:15:40,167
whatever, 17th or whatever it was.

343
00:15:40,907 --> 00:15:43,157
Did that affect sort of,
like, your field work?

344
00:15:43,167 --> 00:15:46,647
Did that affect anything
that you had for your PhD?

345
00:15:47,004 --> 00:15:47,904
It totally did.

346
00:15:48,094 --> 00:15:52,244
I mean, at Duke, the boats were
considered an extension of the classroom.

347
00:15:52,254 --> 00:15:52,284
Okay.

348
00:15:52,584 --> 00:15:58,904
And so it meant boats were a no-go, so
long as the campus itself was shut down.

349
00:15:59,044 --> 00:15:59,314
Yeah.

350
00:15:59,364 --> 00:16:05,674
Um, and so for my work specifically, I
ended up doing a couple different pivots.

351
00:16:05,724 --> 00:16:11,134
In one sense, instead of collecting my
own data in the field, I used h- basically

352
00:16:11,134 --> 00:16:12,524
what we would call historical data.

353
00:16:12,584 --> 00:16:14,964
Not that it's super old data,
but just things that have been

354
00:16:14,964 --> 00:16:16,734
collected by lab mates in the past.

355
00:16:17,054 --> 00:16:21,014
So that was one change I had to make,
was instead of collecting new data,

356
00:16:21,074 --> 00:16:22,484
turning to things that we already had.

357
00:16:23,094 --> 00:16:27,444
And then another pivot that I made that
actually ended up being something that

358
00:16:27,514 --> 00:16:31,714
has continued to stay with me through
the postdoc and probably will through

359
00:16:31,714 --> 00:16:36,864
the rest of my career, is that I got
involved with a lab at the Duke Cancer

360
00:16:36,864 --> 00:16:38,994
Institute that was doing, lab work.

361
00:16:39,354 --> 00:16:44,904
And at the time because they were
caring for live cells for humans, they

362
00:16:44,914 --> 00:16:48,474
had the ability to go into lab and
continue that work to some degree.

363
00:16:48,904 --> 00:16:53,434
And I had got involved with them through
a, serendipitous, like, meet and greet

364
00:16:53,534 --> 00:16:58,104
sort of thing, and, uh, they ended
up getting some whale biopsies and

365
00:16:58,124 --> 00:17:02,724
culturing cells from those whale biopsies
for different sort of experiments.

366
00:17:03,014 --> 00:17:07,244
Um, but because we were in COVID and
because they could still do lab work,

367
00:17:07,534 --> 00:17:12,084
that enabled this whole other branch
of my research, which was diving into

368
00:17:12,374 --> 00:17:14,614
whale adaptations at the molecular scale.

369
00:17:15,004 --> 00:17:18,704
and like I said, it's something I still
work on and something that was a chapter

370
00:17:18,704 --> 00:17:22,394
of the PhD, but it was, it… You
know, one of those moments where you're

371
00:17:22,394 --> 00:17:25,944
like, "Oh, okay, this actually maybe,"
you know, turned out for the better.

372
00:17:25,944 --> 00:17:28,994
I ha- Yeah … I mean, I had lab mates
who were… Or not lab mates I should say,

373
00:17:28,994 --> 00:17:33,064
but cohort mates who were doing, like,
marsh grass experiments in their backyard.

374
00:17:33,504 --> 00:17:33,734
Um-

375
00:17:33,744 --> 00:17:34,014
Mm-hmm.

376
00:17:34,354 --> 00:17:34,644
Yeah

377
00:17:34,704 --> 00:17:34,994
…
yeah.

378
00:17:34,994 --> 00:17:38,254
So everyone, was doing something, but
for me it, worked out well in that sense.

379
00:17:38,564 --> 00:17:38,824
Good.

380
00:17:38,874 --> 00:17:41,087
I mean, you had to be creative
at, some point too, right?

381
00:17:41,087 --> 00:17:43,107
to be able to continue
to work and towards that.

382
00:17:43,107 --> 00:17:45,837
And it's great that it became a chapter
in your PhD, which is great- Yeah

383
00:17:45,887 --> 00:17:46,687
you continue to work on it.

384
00:17:46,687 --> 00:17:47,187
That's awesome.

385
00:17:47,507 --> 00:17:50,724
okay, so Going back to Stanford,
you got this postdoc at Stanford.

386
00:17:50,724 --> 00:17:54,964
And what was this one, this specific, It's
a, it's a postdoctoral scholar, I guess.

387
00:17:55,164 --> 00:17:55,640
And, and- Sure.

388
00:17:55,640 --> 00:17:57,034
Yep … so can you tell
us a little bit about it?

389
00:17:57,034 --> 00:17:58,934
'Cause I think this is gonna be the
focus for the rest of the interview.

390
00:17:59,714 --> 00:18:00,034
Yeah.

391
00:18:00,034 --> 00:18:05,294
So I was hired as a postdoctoral
researcher in Jeremy Goldbogen's group

392
00:18:05,614 --> 00:18:12,914
with my main, project being to enhance
the design of a biologger device.

393
00:18:12,914 --> 00:18:16,834
And when I say biologger, all I mean
is a sort of like a mini computer,

394
00:18:16,834 --> 00:18:20,524
you can think of it much like an
iPhone, that is, gonna sort of

395
00:18:20,524 --> 00:18:22,314
ride the back of the animal- Mm-hmm

396
00:18:22,324 --> 00:18:25,484
uh, for lack of a better explanation,
and record a bunch of different

397
00:18:25,484 --> 00:18:26,504
parameters about its life.

398
00:18:26,514 --> 00:18:26,544
Okay.

399
00:18:26,894 --> 00:18:30,984
So I got brought on to enhance
the design of one of these

400
00:18:31,004 --> 00:18:32,884
biologgers to record heart rate.

401
00:18:33,444 --> 00:18:38,384
And I say enhance because the lab had
had some success in the past measuring

402
00:18:38,384 --> 00:18:43,924
heart rate using, a technique called
an electrocardiogram, short for ECG.

403
00:18:44,074 --> 00:18:45,864
It's very common in human medicine too.

404
00:18:46,424 --> 00:18:50,614
Um, and they had recorded the f- first
ever blue whale heart rates that way.

405
00:18:51,004 --> 00:18:56,924
Um- Wow … but then, wanted to expand
the use of this device and, use it

406
00:18:56,974 --> 00:19:01,714
to study different species and make
it more reliable in different ways.

407
00:19:01,744 --> 00:19:04,954
And so that's why I got brought
on specifically under an

408
00:19:05,024 --> 00:19:08,334
Office of Naval Research grant,
which supported me to do that.

409
00:19:08,594 --> 00:19:12,894
And then, you know, in the off times of
that, working on other related projects.

410
00:19:13,384 --> 00:19:16,714
So I mean, the fact that you did the
biomedical engineering, giving you

411
00:19:16,714 --> 00:19:23,056
that basis in You know, these using
devices and testing out devices and,

412
00:19:23,056 --> 00:19:26,336
kind of figuring out how they're built
so that you can test them out later

413
00:19:26,336 --> 00:19:28,666
on for this postdoc and your PhD.

414
00:19:28,666 --> 00:19:31,716
Like, I mean, that's come in, quite
a bit of handy for your career.

415
00:19:32,016 --> 00:19:32,176
Extremely.

416
00:19:32,186 --> 00:19:33,616
Very unique aspect.

417
00:19:33,616 --> 00:19:35,276
And I mean, obviously I was just
reading up as you were reading,

418
00:19:35,276 --> 00:19:38,946
like Jeremy Goldberg's lab, that's
essentially what he does is, is

419
00:19:38,946 --> 00:19:40,966
looks at the technology in that.

420
00:19:41,376 --> 00:19:46,536
So when you're testing this out, are
you looking at both the testing itself

421
00:19:46,536 --> 00:19:51,186
as they're fastened to the animals,
and also how they're actually built?

422
00:19:51,256 --> 00:19:55,886
Like, are you using that engineering,
uh, degree and, knowledge to look at how

423
00:19:55,886 --> 00:19:59,696
they're actually built and how they can
be either improved or kind of fidget with

424
00:19:59,696 --> 00:20:01,296
it a little bit to do something different?

425
00:20:01,990 --> 00:20:02,630
Definitely.

426
00:20:02,690 --> 00:20:07,080
And I would say the engineering
expertise has come in extremely handy.

427
00:20:07,090 --> 00:20:12,170
It's one of those things where people,
you know, might look at my CV and imagine

428
00:20:12,180 --> 00:20:14,410
like, "Wow, you perfectly planned this."

429
00:20:14,760 --> 00:20:19,890
But I certainly did not, and I think,
it's just one of these, you keep putting

430
00:20:19,890 --> 00:20:23,070
yourself in places to end up being at
the right place at the right time, and

431
00:20:23,360 --> 00:20:26,976
I think it worked out really well for
me that this post-doc brought together

432
00:20:27,006 --> 00:20:30,756
the engineering knowledge and the
marine mammal physiology knowledge from

433
00:20:30,756 --> 00:20:32,536
grad school in a, yeah, an ideal way.

434
00:20:32,936 --> 00:20:36,196
Um, but I would say as
for the tags, definitely.

435
00:20:36,276 --> 00:20:42,116
I mean- Mm-hmm … we're, the fundamental
idea of an electrocardiogram recording,

436
00:20:42,116 --> 00:20:44,066
we're not engineering in any way.

437
00:20:44,086 --> 00:20:45,446
That works the way it works.

438
00:20:45,446 --> 00:20:48,406
And, you know- Of course … there's
some different aspects that you can,

439
00:20:48,826 --> 00:20:52,590
experiment with, number of, leads that
you might use, the material- Mm-hmm

440
00:20:52,590 --> 00:20:55,290
of the electrode that's
making contact with the skin.

441
00:20:55,300 --> 00:20:56,720
These are variables you can change.

442
00:20:57,170 --> 00:21:01,780
But I mean, the biggest part of
that most certainly is actually

443
00:21:01,780 --> 00:21:03,050
the engineering of the device.

444
00:21:03,320 --> 00:21:05,380
What are the- Mm-hmm … drag
forces on the device?

445
00:21:05,750 --> 00:21:07,400
Is it making contact?

446
00:21:07,410 --> 00:21:10,480
Do we have high-frequency
oscillations of the tag that

447
00:21:10,480 --> 00:21:12,220
are adding noise to our signal?

448
00:21:12,620 --> 00:21:15,600
All of those sorts of things
become really relevant.

449
00:21:15,970 --> 00:21:20,640
Um, and so definitely it was an
engineering project through and through.

450
00:21:20,640 --> 00:21:21,050
Yeah.

451
00:21:21,550 --> 00:21:22,220
That's awesome.

452
00:21:22,270 --> 00:21:23,410
That's so cool too.

453
00:21:23,680 --> 00:21:25,876
'Cause it's like, people will do
marine biology degrees, they'll go

454
00:21:25,876 --> 00:21:28,556
into marine biology, and they'll
kinda come up with all these…

455
00:21:28,826 --> 00:21:31,390
their path leads to people who
you meet, like you said, being in

456
00:21:31,390 --> 00:21:32,780
the right place at the right time.

457
00:21:32,780 --> 00:21:35,680
And, but I think it's kinda cool
when you go into something where you

458
00:21:35,680 --> 00:21:39,620
start off with the biomedical aspect,
then you go into a little bit more,

459
00:21:39,620 --> 00:21:41,460
but now you're in the, marine side.

460
00:21:41,460 --> 00:21:44,790
And then now this is like a perfect
kinda thing, and you're discovering

461
00:21:44,790 --> 00:21:46,770
things that is just amazing.

462
00:21:47,080 --> 00:21:49,840
and you know, we're obviously, we're
here to talk about lunge feeding.

463
00:21:49,840 --> 00:21:53,990
So how did you come across
this project in particular?

464
00:21:53,990 --> 00:21:55,570
'Cause you're measuring a bunch of things.

465
00:21:55,700 --> 00:21:58,090
Is it as they do the lunge feeding?

466
00:21:58,090 --> 00:21:58,540
Is that it?

467
00:21:58,540 --> 00:22:01,490
Or how did this entire project-
Mm … just kinda come up in

468
00:22:01,490 --> 00:22:04,340
terms of we wanna know what's
happening to them physiologically?

469
00:22:04,530 --> 00:22:08,010
Like, how are these massive bodies are,
how are they able to go through the

470
00:22:08,010 --> 00:22:12,276
water At this, like, lunging speed, you
know- Sure … this quick burst, right?

471
00:22:12,916 --> 00:22:13,266
Yeah.

472
00:22:13,266 --> 00:22:19,116
So I think really the impetus for,
like, how can we use this tool that

473
00:22:19,116 --> 00:22:24,396
records heart rate to understand lunge
feeding comes from what our group's

474
00:22:24,486 --> 00:22:29,526
larger motivations are, which I would
say the theme under which we all

475
00:22:29,526 --> 00:22:33,976
work is, um, how did we end up with
the biggest animals on the planet?

476
00:22:34,496 --> 00:22:35,916
Why are they in the ocean?

477
00:22:36,126 --> 00:22:39,916
And what are the keys that
unlock life at the largest scale?

478
00:22:40,066 --> 00:22:40,406
Yeah.

479
00:22:40,516 --> 00:22:47,556
So that's what we all work on as a group,
and we think one of the big keys to how

480
00:22:47,566 --> 00:22:51,496
we've ended up with these giant animals
in the ocean, my boss Jeremy likes to

481
00:22:51,496 --> 00:22:53,106
say we're living in the age of giants.

482
00:22:53,106 --> 00:22:55,056
These are the largest
animals that have ever lived.

483
00:22:55,446 --> 00:23:00,096
and so we have the unique opportunity
to study, uh, these types of questions.

484
00:23:00,667 --> 00:23:04,547
But lunge feeding is definitely a key
to how they've been able to do that.

485
00:23:04,777 --> 00:23:04,847
Right.

486
00:23:05,017 --> 00:23:09,967
So unlike terrestrial mammals who are,
you know, single prey predators, like

487
00:23:09,967 --> 00:23:13,677
they're going after something, grabbing
it with teeth, and then processing

488
00:23:13,677 --> 00:23:15,827
it, these are filter feeding animals.

489
00:23:15,837 --> 00:23:22,637
So they are underwater identifying in
some way a big prey patch, and they're

490
00:23:22,637 --> 00:23:27,027
accelerating up to speed towards that
prey patch, at which point they open

491
00:23:27,027 --> 00:23:32,897
their mouth, engulf the prey, and then
instead of teeth, they have baleen plates.

492
00:23:33,237 --> 00:23:38,187
These baleen plates are a lot like coffee
filters, and so they allow them to push

493
00:23:38,187 --> 00:23:43,177
the water out, retain the prey, and
then after this lunge, they can swallow.

494
00:23:43,627 --> 00:23:47,477
And that filtering time takes
quite a bit, and so that limits how

495
00:23:47,477 --> 00:23:48,937
many lunges they can do per dive.

496
00:23:49,367 --> 00:23:53,667
But really the motivating factor for
like what does heart rate do during

497
00:23:53,667 --> 00:23:59,017
these lunges is, is that we think these
are a key, sort of a, a key life history

498
00:23:59,017 --> 00:24:02,977
strategy for the biggest whales, and
we wanna know how it is they do that.

499
00:24:03,687 --> 00:24:04,137
Okay.

500
00:24:04,177 --> 00:24:07,407
So let's talk about, uh,
first how you measure this.

501
00:24:07,447 --> 00:24:10,407
You have this… Say, let's take
a blue whale, for instance, right?

502
00:24:10,477 --> 00:24:12,107
Largest animal on the planet.

503
00:24:12,547 --> 00:24:14,227
Uh, they're absolutely gorgeous.

504
00:24:14,497 --> 00:24:19,250
They run up and down, the Pacific East
Coast or like the West Coast of the US.

505
00:24:19,610 --> 00:24:24,440
can you just talk a little bit about how
do you measure the heart rate of a massive

506
00:24:24,440 --> 00:24:28,140
animal that's moving through the water at
I would imagine would be, at a good speed?

507
00:24:28,610 --> 00:24:29,000
Yeah.

508
00:24:29,320 --> 00:24:34,150
So it is definitely non-trivial,
and, we don't get it all the time.

509
00:24:34,160 --> 00:24:38,290
So, when you're just like sitting on your
couch measuring your heart rate with,

510
00:24:38,410 --> 00:24:42,030
you know, some sort of smartwatch, you
might get a clean signal all the time.

511
00:24:42,030 --> 00:24:44,700
But if you start jumping up and
down or going for a run- Mm-hmm

512
00:24:44,700 --> 00:24:46,720
or anything like that, you
can of course add noise, and

513
00:24:46,730 --> 00:24:48,230
we face the same challenges.

514
00:24:48,720 --> 00:24:53,910
but the way that we are recording
heart rate, like I said, is using, uh,

515
00:24:54,100 --> 00:24:56,420
a method called an electrocardiogram.

516
00:24:56,844 --> 00:25:03,074
And in simple terms, what this is
looking for is measuring an electrical

517
00:25:03,234 --> 00:25:06,534
signal across the heart as it beats.

518
00:25:06,824 --> 00:25:11,414
Um, because if we sort of scale down to
the smallest scale of cardiac function, we

519
00:25:11,414 --> 00:25:16,884
have little muscle cells that are beating,
and the way they do that is by moving

520
00:25:16,924 --> 00:25:19,684
charged particles across their membranes.

521
00:25:20,214 --> 00:25:24,094
And so we can measure that charge,
basically the movement of that charge.

522
00:25:24,104 --> 00:25:24,184
Gotcha.

523
00:25:24,234 --> 00:25:27,164
And from that movement,
we can pick up heartbeats.

524
00:25:27,684 --> 00:25:30,074
So functionally, that's what we're doing.

525
00:25:30,124 --> 00:25:36,004
What it looks like in practice is we
have a bio logging device that, like

526
00:25:36,004 --> 00:25:37,664
I said, kinda looks like an iPhone.

527
00:25:37,674 --> 00:25:40,224
In this case, it's a little bit bigger
'cause we're doing a little more with it.

528
00:25:40,574 --> 00:25:47,282
but it has two metal electrodes that
are almost, like, quarter-sized, and

529
00:25:47,282 --> 00:25:53,028
they make contact with the whale's skin,
and the electrical potential across

530
00:25:53,028 --> 00:25:56,748
those two electrodes is what we're
picking up, to capture these heartbeats.

531
00:25:57,118 --> 00:26:00,925
And so, yeah, we use these bio
logging tags that have really been

532
00:26:00,925 --> 00:26:04,745
the bread and butter of our group's
work for the last decade or so.

533
00:26:05,155 --> 00:26:05,495
Yeah.

534
00:26:05,505 --> 00:26:08,075
And we've outfitted them with
a new sensor in this case.

535
00:26:08,425 --> 00:26:09,088
That's amazing.

536
00:26:09,088 --> 00:26:12,368
So this gets attached on, so
is it just like a harpoon-type

537
00:26:12,368 --> 00:26:16,105
thing where it gets shot and it
attaches on so it's on the skin?

538
00:26:16,525 --> 00:26:18,845
It's on the skin … 'cause it's gotta
be able to take going over the surface

539
00:26:18,845 --> 00:26:22,398
and, like, you know, some rough areas,
you know, currents and obviously the

540
00:26:22,408 --> 00:26:26,638
whale movement and the friction of the
ocean currents on that device, right?

541
00:26:26,688 --> 00:26:28,558
So it's gotta be- Right … able
to be fastened quite sturdily.

542
00:26:29,348 --> 00:26:30,148
Yeah, exactly.

543
00:26:30,158 --> 00:26:33,188
So they are- That's two
words … suction cup attached tags.

544
00:26:33,218 --> 00:26:33,638
So they- Oh,

545
00:26:33,648 --> 00:26:33,908
okay.

546
00:26:33,908 --> 00:26:34,148
Cool

547
00:26:34,218 --> 00:26:40,508
yeah, so they're non-invasive and
they, are attached by attaching

548
00:26:40,508 --> 00:26:45,148
the tag itself to the end of a
six meter or so carbon fiber pole.

549
00:26:45,728 --> 00:26:46,018
Okay.

550
00:26:46,078 --> 00:26:51,848
And then one of our team members in the
field gets up on the pulpit of a small

551
00:26:51,848 --> 00:26:56,068
boat, which is sort of like, you know, an
overhanging part of the boat where they

552
00:26:56,068 --> 00:27:00,288
can get a good view of- Mm-hmm what's
below or what's right next to the boat.

553
00:27:00,738 --> 00:27:04,528
And then as, say in this case, a
blue whale comes up to take a breath,

554
00:27:04,898 --> 00:27:09,248
um, they can just go ahead and
reach out this pole and basically,

555
00:27:09,308 --> 00:27:13,938
give it a little slap and get these
suction cups to attach to the back.

556
00:27:14,328 --> 00:27:18,478
And then the perk of the hydrostatic
pressure in the ocean is that once the

557
00:27:18,478 --> 00:27:23,738
animal takes a deep dive, you get a
little extra press- Oh onto the tag.

558
00:27:23,838 --> 00:27:24,128
Yeah.

559
00:27:24,128 --> 00:27:28,218
And so then because they're only
suction cup attached, they won't stay

560
00:27:28,218 --> 00:27:30,548
on for really long periods of time.

561
00:27:30,598 --> 00:27:34,708
Most typically we're getting,
you know, maybe 12 to 36 hours

562
00:27:35,288 --> 00:27:35,958
of- That's pretty good.

563
00:27:35,968 --> 00:27:35,988
Yeah.

564
00:27:36,138 --> 00:27:37,358
That's more than I expected.

565
00:27:37,668 --> 00:27:41,318
when you first attach them on, what's
the success rate of it sticking before

566
00:27:41,318 --> 00:27:43,418
it goes down a little deeper to get that?

567
00:27:43,588 --> 00:27:44,548
It can't be easy.

568
00:27:45,028 --> 00:27:45,518
Yeah.

569
00:27:45,648 --> 00:27:49,548
I don't personally do it- Right … so I
have to give big kudos to my lab mates.

570
00:27:49,658 --> 00:27:54,088
they're the ones who've, been putting our
tags on, for this project and many others.

571
00:27:54,148 --> 00:27:57,363
But- I mean, I would say it's fairly high.

572
00:27:57,423 --> 00:28:01,653
It depends, you know, if the
animal maybe does some turn away

573
00:28:01,843 --> 00:28:05,233
at the last second, you know, maybe
your attachment isn't so good.

574
00:28:05,283 --> 00:28:08,393
But I would say for the most
part it's pretty good that these-

575
00:28:08,393 --> 00:28:09,503
Yeah … tags will stick on.

576
00:28:09,723 --> 00:28:14,223
we benefit from working with huge
animals that we model, you know,

577
00:28:14,243 --> 00:28:17,533
almost like they're as flat as a
table because they're so large.

578
00:28:17,543 --> 00:28:20,513
So - Yeah … if you were tagging
something like a pilot whale, you

579
00:28:20,513 --> 00:28:23,663
might have a little bit more to think
of in terms of finessing- Gotcha

580
00:28:24,583 --> 00:28:25,893
the creature, but we get away-
Yeah … with a lot more.

581
00:28:26,333 --> 00:28:26,943
Yeah, that makes sense.

582
00:28:26,943 --> 00:28:29,473
You don't think about that until,
like, you're right up there with

583
00:28:29,473 --> 00:28:32,183
them, and you're just like, "Oh, okay,
there's more surface area to attach

584
00:28:32,183 --> 00:28:32,993
than in flat surface area." Right.

585
00:28:33,043 --> 00:28:33,663
That's cool.

586
00:28:33,943 --> 00:28:38,013
So this, device basically like a
ECG, it just kinda just takes that

587
00:28:38,013 --> 00:28:40,763
measurement for, like, 12 to 36 hours.

588
00:28:40,793 --> 00:28:41,793
It pops off.

589
00:28:41,933 --> 00:28:44,493
You go retrieve it, and
then you get the data.

590
00:28:44,493 --> 00:28:46,383
It's not like satellite-driven data.

591
00:28:46,383 --> 00:28:50,243
It's like the data is, recorded
within the tag, and then you download

592
00:28:50,253 --> 00:28:51,333
the data once you get the tag.

593
00:28:51,333 --> 00:28:51,753
Is that, is that right?

594
00:28:52,203 --> 00:28:52,683
Correct.

595
00:28:52,733 --> 00:28:53,533
Yeah, that's correct.

596
00:28:53,533 --> 00:28:56,243
So we call these sort
of type of tag archival.

597
00:28:56,833 --> 00:28:56,853
Yeah.

598
00:28:57,153 --> 00:29:01,033
So they write all of the
data onto the tag, and then,

599
00:29:01,213 --> 00:29:02,923
require us to go pick them up.

600
00:29:02,973 --> 00:29:07,563
And part of the reason for that is that
not the heart rate tags specifically, but

601
00:29:07,563 --> 00:29:11,333
a lot of the tags we work with have video
and audio data, which would be- What?

602
00:29:11,333 --> 00:29:12,813
… really hard to satellite up.

603
00:29:12,843 --> 00:29:13,503
oh, yeah.

604
00:29:13,823 --> 00:29:15,943
Oh, yeah, we've got- What?

605
00:29:15,943 --> 00:29:17,263
… you can ride the back of a blue whale.

606
00:29:17,263 --> 00:29:19,543
You can ride the back of a
humpback whale in our lab.

607
00:29:19,553 --> 00:29:20,353
I mean- That is- … you

608
00:29:20,353 --> 00:29:21,653
know … so cool.

609
00:29:21,943 --> 00:29:23,683
Theoretically speaking,
not, not literally.

610
00:29:23,683 --> 00:29:23,883
Yeah,

611
00:29:23,883 --> 00:29:24,363
yeah, of course.

612
00:29:24,363 --> 00:29:24,843
But, um-

613
00:29:25,163 --> 00:29:26,903
But you're watching it like you
could… Like you're on the back-

614
00:29:26,903 --> 00:29:27,553
Right, right

615
00:29:27,553 --> 00:29:27,813
…
basically.

616
00:29:27,813 --> 00:29:27,843
Right.

617
00:29:28,023 --> 00:29:28,303
Yeah.

618
00:29:28,363 --> 00:29:28,713
That is so cool.

619
00:29:28,713 --> 00:29:28,723
Yeah.

620
00:29:28,723 --> 00:29:28,833
So

621
00:29:29,203 --> 00:29:33,863
this is… I mean, and this is one
of the really important features of

622
00:29:33,873 --> 00:29:37,783
the tags that we use that allowed us
to learn so much about lunge feeding.

623
00:29:38,063 --> 00:29:38,383
Mm-hmm.

624
00:29:38,383 --> 00:29:45,143
So a lot of the early work in, uh,
the group was to basically use all

625
00:29:45,143 --> 00:29:48,943
of the sort of movement sensors that
the- Yeah … tags have and compare

626
00:29:48,943 --> 00:29:52,263
them to what they were actually seeing
in the video to be able to say, "Oh

627
00:29:52,493 --> 00:29:55,533
yeah, we can see the animal start to
fluke. We can see the animal open-

628
00:29:55,543 --> 00:29:57,043
Yeah … its mouth at this time."

629
00:29:57,353 --> 00:30:01,723
So video has been a huge asset
to the lab to be able to validate

630
00:30:02,023 --> 00:30:03,863
a lot of these kinematic events.

631
00:30:04,043 --> 00:30:04,593
Yeah.

632
00:30:04,593 --> 00:30:08,113
Um, yeah, that you just otherwise
you wouldn't be able to… Unless

633
00:30:08,113 --> 00:30:10,693
you had a scuba diver down there, you
couldn't actually confirm- That's for

634
00:30:10,693 --> 00:30:11,623
sure … that's what the animal's doing.

635
00:30:11,843 --> 00:30:12,053
Yeah.

636
00:30:12,053 --> 00:30:14,356
And then for 12 to 36 hours,
you're not gonna be able to s-

637
00:30:14,356 --> 00:30:16,893
keep up with an animal
that long, even on a boat.

638
00:30:16,983 --> 00:30:18,196
Like, that's, that's phenomenal.

639
00:30:18,506 --> 00:30:19,726
yous- mentioned audio.

640
00:30:19,726 --> 00:30:23,536
Is that to hear… do you guys
measure- you know, the whistles,

641
00:30:23,536 --> 00:30:26,356
clicks, and all that kind of stuff
that they do, and are you measuring

642
00:30:26,356 --> 00:30:31,876
whether it coincides with heart rate
or, lungeing or anything like that?

643
00:30:31,996 --> 00:30:32,926
Or is that, a different project?

644
00:30:33,099 --> 00:30:33,889
That's a good question.

645
00:30:33,979 --> 00:30:37,359
I would say the audio gets used
for lots of different things.

646
00:30:37,489 --> 00:30:37,659
Right.

647
00:30:37,689 --> 00:30:43,759
We haven't used it specifically related to
the heart rate work yet, but we use it to

648
00:30:43,759 --> 00:30:46,659
study noises the animal itself is making.

649
00:30:46,669 --> 00:30:50,279
We use it to study noises in
the animal's environment- Mm-hmm

650
00:30:50,389 --> 00:30:51,299
like if a ship passes by- Oh, yeah.

651
00:30:51,989 --> 00:30:52,159
Yep

652
00:30:52,199 --> 00:30:56,729
…
or if, you know, there might
be some other acute, uh, noise

653
00:30:56,729 --> 00:30:57,949
that the animal's exposed to.

654
00:30:58,219 --> 00:31:01,119
So yeah, the audio comes in handy for
all sorts of different things, not

655
00:31:01,119 --> 00:31:02,239
specifically for this project though.

656
00:31:02,489 --> 00:31:02,839
Yeah.

657
00:31:03,139 --> 00:31:03,789
That's amazing.

658
00:31:03,919 --> 00:31:04,559
That's so cool.

659
00:31:04,799 --> 00:31:07,379
Before we talk about the results that
you saw, let's talk a little bit about

660
00:31:07,379 --> 00:31:09,349
lunge feeding and what it actually is.

661
00:31:09,559 --> 00:31:09,789
Sure.

662
00:31:09,809 --> 00:31:14,329
Uh, and what can people kind of picture
if they haven't seen it themselves in a,

663
00:31:14,329 --> 00:31:14,729
in any format?

664
00:31:14,729 --> 00:31:14,919
Yeah.

665
00:31:14,969 --> 00:31:20,399
I mean, if you can, imagine a blue whale
in your mind, um, lunge feeding is sort

666
00:31:20,399 --> 00:31:24,499
of a three-part of feeding strategy.

667
00:31:24,769 --> 00:31:28,849
The first part is acceleration,
and during this part, you can

668
00:31:28,849 --> 00:31:31,099
imagine the animal fluking hard.

669
00:31:31,109 --> 00:31:33,449
So it has these large flukes.

670
00:31:34,059 --> 00:31:36,529
It's moving them up and
down to pick up speed.

671
00:31:36,939 --> 00:31:40,832
Uh, so during the acceleration
phase, the animal increases in speed

672
00:31:40,832 --> 00:31:43,992
to about four meters per second,
which is about 10 miles an hour.

673
00:31:44,332 --> 00:31:44,362
Mm-hmm.

674
00:31:44,362 --> 00:31:48,302
It doesn't seem super fast, but they're
huge animals, so it's pretty fast.

675
00:31:48,302 --> 00:31:48,542
Yeah.

676
00:31:49,052 --> 00:31:53,346
And then the second part of lunge
feeding is the engulfment phase,

677
00:31:53,346 --> 00:31:55,052
and this is a really quick phase.

678
00:31:55,062 --> 00:32:01,402
So this is when the animal has reached
peak speed, it'll open its mouth and

679
00:32:02,092 --> 00:32:08,352
engulf the prey patch that it finds, and
then close its mouth completely, and that

680
00:32:08,362 --> 00:32:10,962
phase is usually several seconds long.

681
00:32:11,272 --> 00:32:15,392
but during that phase, because the,
ventral groove blubber, which is

682
00:32:15,672 --> 00:32:19,662
sort of what allows the animal to
scoop up a large amount of that

683
00:32:19,662 --> 00:32:24,542
pre-saturated water, it expands a
ton and acts almost like a parachute.

684
00:32:24,842 --> 00:32:26,432
So it slows the animal down.

685
00:32:26,432 --> 00:32:30,022
So we see their speed drop
almost instantaneously from

686
00:32:30,022 --> 00:32:31,732
10 miles an hour down to zero.

687
00:32:32,276 --> 00:32:36,063
So that's part two, and then part
three is the filtering phase.

688
00:32:36,373 --> 00:32:41,323
And so during this phase, what you can
envision is a blue whale sort of riding

689
00:32:41,323 --> 00:32:44,423
out its momentum of the lunge it just did.

690
00:32:44,793 --> 00:32:48,103
This is a largely unpowered phase,
so they're not fluking at all.

691
00:32:48,103 --> 00:32:51,613
They're just sort of gliding through
the water, and this is the part where

692
00:32:51,613 --> 00:32:55,623
they're pushing that water out through
the baleen, and then at the end of

693
00:32:55,623 --> 00:32:56,983
the filter phase, they'll swallow.

694
00:32:57,343 --> 00:33:01,913
and so that acceleration phase,
say for like a blue whale, could

695
00:33:01,923 --> 00:33:05,200
be, 10 to 20 seconds or so,

696
00:33:05,450 --> 00:33:07,040
the engulf- Oh, they can
get that fast, that, that…

697
00:33:07,080 --> 00:33:08,550
They can go that fast that quickly?

698
00:33:09,030 --> 00:33:12,400
Yeah, it depends on exactly the
circumstances surrounding the prey

699
00:33:12,400 --> 00:33:14,560
patch- Of course … and what,
at what depths they're foraging.

700
00:33:14,990 --> 00:33:16,630
but that's relatively short.

701
00:33:16,700 --> 00:33:20,140
The engulfment is very short, and
then the filtering is pretty long.

702
00:33:20,520 --> 00:33:20,550
Okay.

703
00:33:20,560 --> 00:33:24,500
Um, so the filtering phase for the
largest of blue whales takes about

704
00:33:24,550 --> 00:33:26,200
a minute to a minute and a half.

705
00:33:26,210 --> 00:33:26,240
Okay.

706
00:33:26,660 --> 00:33:30,210
So for a minute and a minute and a
half, they are just gliding through

707
00:33:30,210 --> 00:33:34,580
the water, expending no energy,
pushing this water out, Right … to

708
00:33:34,580 --> 00:33:35,980
then be able to swallow their prey.

709
00:33:36,200 --> 00:33:37,910
So that's kind of the
mental image is like-

710
00:33:38,020 --> 00:33:38,280
Yeah

711
00:33:38,780 --> 00:33:40,630
…
accelerate, engulf, filter.

712
00:33:41,190 --> 00:33:41,900
That's amazing.

713
00:33:41,970 --> 00:33:46,860
to go back to the part one, the
acceleration phase, do they time it?

714
00:33:47,260 --> 00:33:50,423
'Cause I imagine, you know, you're going,
was it 10, 10 miles per hour, equivalent

715
00:33:50,423 --> 00:33:51,893
of 10 miles, so four meters per second, is

716
00:33:52,303 --> 00:33:52,403
that what you said?

717
00:33:52,403 --> 00:33:52,413
Right.

718
00:33:53,132 --> 00:33:54,812
so you're seeing something ahead.

719
00:33:54,972 --> 00:33:58,242
You know there's something ahead, And
obviously the distance they travel within

720
00:33:58,242 --> 00:34:01,452
that time is gonna be different depending
on currents and everything like that.

721
00:34:01,982 --> 00:34:04,972
How do they see the patch ahead?

722
00:34:04,972 --> 00:34:06,732
'Cause they're probably
going a good distance.

723
00:34:07,342 --> 00:34:09,392
how do they see the patch
ahead and realize, "I'm gonna

724
00:34:09,392 --> 00:34:10,542
start accelerating now"?

725
00:34:10,542 --> 00:34:11,702
Like, how do they detect that?

726
00:34:12,332 --> 00:34:12,732
Yeah.

727
00:34:12,732 --> 00:34:18,182
So I think part of it is that something
we see most often when they're foraging

728
00:34:18,192 --> 00:34:23,102
deep is rather than come across to a
patch, they're often coming up on it.

729
00:34:23,312 --> 00:34:23,932
Oh,

730
00:34:24,082 --> 00:34:24,372
okay.

731
00:34:24,402 --> 00:34:29,302
And so that helps to have, depending
on the depth, if there is any light

732
00:34:29,342 --> 00:34:32,962
penetrating, to have sort of the
patch- Yeah … backlit in that way.

733
00:34:33,412 --> 00:34:33,652
Yeah.

734
00:34:33,652 --> 00:34:37,179
Um, but to be honest, that
is a big question mark.

735
00:34:37,209 --> 00:34:41,019
We don't really know what sensory
capacities they have, … what

736
00:34:41,019 --> 00:34:42,369
they might be cueing off of.

737
00:34:42,379 --> 00:34:43,949
Are they cueing visually?

738
00:34:44,219 --> 00:34:48,519
are they cueing with some tactile
means that we don't fully understand?

739
00:34:48,949 --> 00:34:53,129
Are they cueing based off vocalizations
that, you know, another animal's

740
00:34:53,129 --> 00:34:55,009
making in the same vicinity as them?

741
00:34:55,586 --> 00:34:58,876
It's a really big question mark, and
I have a lab mate who's trying to

742
00:34:58,876 --> 00:35:00,926
tease apart some of that, uh- Yeah

743
00:35:00,966 --> 00:35:05,346
and thinking about how they might sense
water temperature or some other- Right

744
00:35:05,356 --> 00:35:09,026
… features of the oceanography- Yeah … to
sort of pick out these patches.

745
00:35:09,286 --> 00:35:10,206
but this is really important.

746
00:35:10,226 --> 00:35:13,596
We know that, you know, sort of a big
thing in our field is these marine

747
00:35:13,606 --> 00:35:17,546
predators cannot survive on the average
distribution of prey in the ocean.

748
00:35:17,916 --> 00:35:21,356
They have to find patches that
concentrate their prey to survive.

749
00:35:22,066 --> 00:35:22,156
Right.

750
00:35:22,156 --> 00:35:23,896
And so it's a really important question.

751
00:35:23,906 --> 00:35:26,426
How do they find them, um-
Yeah … and how do they know

752
00:35:26,436 --> 00:35:28,586
what, patches are worth feeding at?

753
00:35:29,066 --> 00:35:29,436
Yeah.

754
00:35:29,522 --> 00:35:32,922
and of course, and if it changes
with climate change coming in,

755
00:35:32,922 --> 00:35:36,262
currents changing, and that, prey
patch gone or somewhere else.

756
00:35:36,332 --> 00:35:36,632
Right.

757
00:35:36,822 --> 00:35:37,772
They have to find it.

758
00:35:37,822 --> 00:35:40,682
that's a lot of energy they have to
expend to go to a new place to find it.

759
00:35:40,692 --> 00:35:41,939
So yeah, that's a cool question.

760
00:35:41,949 --> 00:35:43,639
I love all these questions.

761
00:35:43,639 --> 00:35:49,219
And going back to the heart rate, so what
did you find with heart rate, measuring

762
00:35:49,219 --> 00:35:51,079
heart rate with these lunge feedings?

763
00:35:51,720 --> 00:35:52,060
Yeah.

764
00:35:52,060 --> 00:35:54,610
So we had two big findings
that came out of the study.

765
00:35:54,640 --> 00:35:58,620
The first being that the heart rate
patterns of these lunge-feeding blue

766
00:35:58,620 --> 00:36:01,790
whales and humpbacks, which were the
two species that we studied here,

767
00:36:02,400 --> 00:36:07,350
mimic the heart rate patterns we see
in human sprinters in the sense that-

768
00:36:07,350 --> 00:36:10,830
Hmm … the animal goes through the
acceleration and the engulfment phase,

769
00:36:10,890 --> 00:36:14,460
and then they go into this really low
energy, unpowered filtering phase.

770
00:36:14,920 --> 00:36:18,720
But we see that the heart rate stays
pretty high at the start of the filtering

771
00:36:18,720 --> 00:36:20,140
phase and only gradually recovers.

772
00:36:21,440 --> 00:36:25,170
And actually, that's exactly what
happens in human sprinters, and this

773
00:36:25,170 --> 00:36:31,010
is really important because it allows
oxygenated blood to perfuse the body in

774
00:36:31,010 --> 00:36:37,440
this sort of, rest or recovery time and
renew the fuel sources that the animal

775
00:36:37,440 --> 00:36:41,480
or that the sprinter's gonna use on
the next sprint or on the next lunge.

776
00:36:41,840 --> 00:36:45,600
So that was one really cool
result, is that these animals

777
00:36:45,710 --> 00:36:47,010
act a lot like sprinters.

778
00:36:47,040 --> 00:36:51,130
It gives us the idea that probably
they're using similar metabolic fuel

779
00:36:51,130 --> 00:36:55,270
sources to human sprinters, might
have similar muscular adaptations

780
00:36:55,280 --> 00:36:56,420
to human sprinters, and so on.

781
00:36:56,953 --> 00:37:00,563
And then the second result that
was really neat is we said, "Hey,

782
00:37:00,563 --> 00:37:05,153
what if we look at these heart rate
patterns in the biggest animals and

783
00:37:05,153 --> 00:37:06,653
we compare them to smaller animals?"

784
00:37:06,663 --> 00:37:10,913
So that's like smaller marine mammals-
Yeah … sea birds, and also terrestrial.

785
00:37:11,553 --> 00:37:17,433
And what we found is that it seems like
these really big whales, because of their

786
00:37:17,433 --> 00:37:23,423
large body size, access a larger, what
we called cardiac or heart rate scope.

787
00:37:23,433 --> 00:37:23,443
Hmm.

788
00:37:23,753 --> 00:37:27,556
Which is to say, you know, maybe
your average humpback or blue whale

789
00:37:27,556 --> 00:37:32,946
can access heart rates from 5 to
35 beats per minute, which is about

790
00:37:33,156 --> 00:37:34,686
sevenfold range in heart rates.

791
00:37:35,066 --> 00:37:40,196
Whereas a human on the day-to-day
is accessing 60 to 180 beats

792
00:37:40,206 --> 00:37:43,096
per minute, which is only a
threefold range in heart rates.

793
00:37:43,606 --> 00:37:49,066
And so we think this fact of large
body size, unlocking this large

794
00:37:49,066 --> 00:37:54,196
heart rate scope might actually be
a key to allow these animals to sort

795
00:37:54,196 --> 00:37:59,776
of, support the intense exercise of
lunging at depth, but also conserve

796
00:37:59,776 --> 00:38:01,597
oxygen while on a breath-hold dive.

797
00:38:02,297 --> 00:38:04,410
That's amazing That's so cool.

798
00:38:05,250 --> 00:38:08,880
Just all the, stuff that you can find
out from, like, this one device, Mm-hmm.

799
00:38:08,890 --> 00:38:12,240
And looking at a behavior
is absolutely phenomenal.

800
00:38:12,250 --> 00:38:12,270
Um-

801
00:38:12,540 --> 00:38:17,800
Yeah, it's really fun to… I think
one of the things I like is to take

802
00:38:18,010 --> 00:38:21,470
these things we learn about in whales
and compare them to other systems.

803
00:38:21,760 --> 00:38:25,894
There's so much to learn, you
sort of take whales as, like, this

804
00:38:25,894 --> 00:38:29,744
extreme- Yeah … and then compare
them back, um- That's awesome to the

805
00:38:29,764 --> 00:38:31,194
average, what the average is doing.

806
00:38:31,564 --> 00:38:34,964
So the results that you have now, how
many… Was it more than one field

807
00:38:34,964 --> 00:38:37,194
season, or is this, one field season?

808
00:38:37,604 --> 00:38:40,454
We did collect all of these records,
so there were nine records in

809
00:38:40,464 --> 00:38:44,734
total, over multiple field seasons
in multiple different study areas.

810
00:38:44,804 --> 00:38:45,074
Okay.

811
00:38:45,304 --> 00:38:46,150
Um, yeah.

812
00:38:46,150 --> 00:38:50,730
So in the future I think we'll probably
target, you know, maybe broader scope

813
00:38:50,730 --> 00:38:54,150
of species in different areas, but at
least- Yeah … for this study, it was

814
00:38:54,150 --> 00:38:57,450
a little bit more opportunistic where
we had the chance to put these tags out.

815
00:38:57,680 --> 00:38:59,990
And this was on blue whales and humpbacks?

816
00:39:00,300 --> 00:39:00,880
Correct.

817
00:39:01,210 --> 00:39:01,580
Okay.

818
00:39:01,740 --> 00:39:02,170
So cool.

819
00:39:02,180 --> 00:39:06,367
So what, species are you looking forward
to finding out, that you haven't done yet?

820
00:39:07,167 --> 00:39:09,537
So the minke whales are
the smallest rorquals.

821
00:39:09,537 --> 00:39:09,567
Okay.

822
00:39:09,967 --> 00:39:12,217
Which is funny to say because
they're not that small.

823
00:39:12,217 --> 00:39:12,237
No,

824
00:39:12,597 --> 00:39:13,037
exactly, yeah.

825
00:39:13,177 --> 00:39:13,977
They're about six meters-

826
00:39:14,557 --> 00:39:20,457
six meters in length, which, the biggest
elephants stand at about four meters tall.

827
00:39:20,497 --> 00:39:23,057
So these are big animals even
though- Yeah … in our world we talk

828
00:39:23,057 --> 00:39:23,947
about them- For sure … as small.

829
00:39:24,407 --> 00:39:28,017
they would be really interesting because
they're sort of… We think of blue whales

830
00:39:28,067 --> 00:39:31,757
as pushing the upper limits of size,
and minke whales as pushing the lower

831
00:39:31,757 --> 00:39:33,447
limits of size in this animal group.

832
00:39:33,447 --> 00:39:33,687
Yeah.

833
00:39:33,687 --> 00:39:34,747
So that would be one.

834
00:39:35,177 --> 00:39:39,760
And then the second group that I'd
be really interested in is big whales

835
00:39:39,820 --> 00:39:41,700
that don't rely on lunge feeding.

836
00:39:41,700 --> 00:39:44,820
So something like a North
Atlantic right whale, for example.

837
00:39:44,830 --> 00:39:49,280
Mm. They have a totally different
strategy, which is ram filter feeding.

838
00:39:49,280 --> 00:39:53,360
So almost like a lawnmower, like they
just open their mouth and they just mow

839
00:39:53,360 --> 00:39:58,164
the lawn, and whatever they trap, um,
while they mow the lawn is what they eat.

840
00:39:58,584 --> 00:39:59,114
And so- So

841
00:39:59,114 --> 00:40:03,424
they're going at the same speed as they
would be going regularly, like swimming.

842
00:40:03,434 --> 00:40:03,774
Right.

843
00:40:04,064 --> 00:40:04,654
Exactly.

844
00:40:04,664 --> 00:40:04,954
Ah,

845
00:40:04,964 --> 00:40:05,344
interesting.

846
00:40:05,344 --> 00:40:11,514
Slow… Yeah, super slow speed, very
sort of stable, kind of energy profile.

847
00:40:11,524 --> 00:40:12,974
Much different than the lunge feeders.

848
00:40:12,984 --> 00:40:16,004
So that's the other group that I'd
be really interested in putting

849
00:40:16,004 --> 00:40:18,114
these tags on to understand- Yeah

850
00:40:18,114 --> 00:40:19,344
you know, what… how does that look in?

851
00:40:19,344 --> 00:40:21,134
Is that much different from lunge feeding?

852
00:40:21,214 --> 00:40:22,464
Expecting it probably is.

853
00:40:23,014 --> 00:40:24,594
yeah, I would imagine it, it would be.

854
00:40:24,594 --> 00:40:27,994
But it, yeah, you wonder how much-
energy it can take to do that

855
00:40:27,994 --> 00:40:29,074
is probably a little bit more.

856
00:40:29,074 --> 00:40:32,167
Cause you, wonder with these big
animals, the lunge feeding, is it

857
00:40:32,167 --> 00:40:33,657
the most efficient way of feeding?

858
00:40:34,287 --> 00:40:36,627
Like I assume what happens if they
just went through and did this ram

859
00:40:36,627 --> 00:40:40,627
feeding, they would disperse and they
may not catch all of them at one time.

860
00:40:40,637 --> 00:40:40,667
Right.

861
00:40:41,077 --> 00:40:44,107
'cause like these patches, they're
essentially plankton, right?

862
00:40:44,237 --> 00:40:45,627
Like it's zooplankton that they're eating.

863
00:40:45,637 --> 00:40:46,167
Yeah, right.

864
00:40:46,187 --> 00:40:46,937
Exactly, yeah.

865
00:40:47,017 --> 00:40:49,937
So they're targeting krill and-
Yeah … small forage fish for

866
00:40:49,937 --> 00:40:51,337
some species, but yeah, largely

867
00:40:51,337 --> 00:40:51,347
krill.

868
00:40:51,347 --> 00:40:51,697
Okay.

869
00:40:51,847 --> 00:40:52,727
Right, right.

870
00:40:52,727 --> 00:40:52,817
Yeah.

871
00:40:52,817 --> 00:40:56,887
and so like the lunge allows them
to kinda catch them off guard,

872
00:40:57,327 --> 00:40:58,277
I guess- Right … in a way.

873
00:40:58,567 --> 00:41:02,077
But for a plankton kind of, just
plankton, like they're not really

874
00:41:02,287 --> 00:41:05,107
going anywhere unless they're in
a current of some sort, right?

875
00:41:05,587 --> 00:41:05,967
Right.

876
00:41:06,307 --> 00:41:10,827
And I think the real key of lunge
feeding is that, it's really only

877
00:41:10,837 --> 00:41:15,197
worth it as a feeding strategy when
the prey are at high concentrations.

878
00:41:15,757 --> 00:41:16,107
Yeah.

879
00:41:16,117 --> 00:41:18,457
So we say something like, you
know, you're not gonna go to a

880
00:41:18,457 --> 00:41:21,127
poorly stocked grocery store when
there's a well-stocked- Of course

881
00:41:21,127 --> 00:41:22,157
one down the street.

882
00:41:22,527 --> 00:41:22,767
Yeah.

883
00:41:22,767 --> 00:41:26,697
Um, and so I think that's a big
difference is that these lunge

884
00:41:26,697 --> 00:41:32,499
feeders rely on dense patches, whereas
something like a right whale, which

885
00:41:32,499 --> 00:41:35,999
is more of- Yeah … a grazer, they
don't necessarily require density

886
00:41:36,079 --> 00:41:37,739
They're just opening up
the mouth when it's around.

887
00:41:37,789 --> 00:41:38,244
That's- Exactly

888
00:41:38,244 --> 00:41:39,209
that's what they do, right?

889
00:41:39,209 --> 00:41:39,549
Yeah.

890
00:41:39,849 --> 00:41:40,139
Yeah.

891
00:41:40,179 --> 00:41:40,599
Okay.

892
00:41:40,629 --> 00:41:41,439
that's really cool.

893
00:41:41,439 --> 00:41:43,769
So obviously you already
have some questions.

894
00:41:43,769 --> 00:41:46,379
You kinda know what
you wanna do, uh, next.

895
00:41:46,379 --> 00:41:47,969
This paper, where was it published?

896
00:41:48,409 --> 00:41:50,609
Uh, it came out in PNAS about a month ago.

897
00:41:50,939 --> 00:41:51,289
Okay.

898
00:41:51,439 --> 00:41:51,979
Awesome.

899
00:41:52,219 --> 00:41:55,249
and so, You guys just probably,
like, you're doing a field season

900
00:41:55,259 --> 00:41:56,499
or finishing up a field season.

901
00:41:56,509 --> 00:41:59,729
Are you doing follow-ups on this?

902
00:41:59,779 --> 00:42:03,379
Or like, what's the next step for
this understanding of lunge feeding-

903
00:42:04,109 --> 00:42:04,709
Oh, yeah … with this tool?

904
00:42:04,709 --> 00:42:05,089
Yeah.

905
00:42:05,116 --> 00:42:09,246
I would say anything that probably
comes to mind, we're trying.

906
00:42:09,576 --> 00:42:13,806
more deployments on the same species
to try to capture different behaviors.

907
00:42:13,816 --> 00:42:18,776
So for example- Mm-hmm … can we capture
heart rates, after an animal has lunge

908
00:42:18,776 --> 00:42:20,456
fed for an extended period of time?

909
00:42:20,486 --> 00:42:23,526
Can we- Mm-hmm … understand maybe how
they're recovering on longer timescales?

910
00:42:24,006 --> 00:42:28,266
like I said, smaller species to
see how these patterns translate.

911
00:42:28,776 --> 00:42:33,986
and then I think, one of the hopes we
have for this tool is that in the same

912
00:42:33,986 --> 00:42:38,656
way that you might rely on a smartwatch
or a smart ring to give you heart

913
00:42:38,656 --> 00:42:42,966
rate information to track your health
over time, or to track when you've

914
00:42:42,966 --> 00:42:44,766
had, like, a stressful event in life-

915
00:42:44,816 --> 00:42:45,106
Mm-hmm

916
00:42:45,336 --> 00:42:50,006
we hope that these tools can be
used in a similar way for whales.

917
00:42:50,356 --> 00:42:55,646
Um- Yeah … and to do that, we need to
collect larger data sets and get a better

918
00:42:55,646 --> 00:43:01,306
sense of, what's the resting behavior
like, what's, the high-activity feeding

919
00:43:01,306 --> 00:43:04,846
behavior like, and how do heart rates,
you know, track those different behaviors.

920
00:43:04,866 --> 00:43:08,346
So- Yeah … yeah, I think there's
a million directions to go, and

921
00:43:08,346 --> 00:43:11,486
we're trying to pursue the ones that
interest us most- Yeah … but also

922
00:43:11,766 --> 00:43:13,866
have important conservation impact.

923
00:43:14,336 --> 00:43:14,666
Yeah.

924
00:43:14,666 --> 00:43:18,046
And, and speaking of that, like, you know,
on, the West Coast, and I know this is not

925
00:43:18,046 --> 00:43:21,536
one of your study animals, so I apologize
if this is kinda going out of scope.

926
00:43:21,846 --> 00:43:24,846
Gray whales have been hit pretty
hard over the last few years, right?

927
00:43:24,876 --> 00:43:26,262
They're a big large animal.

928
00:43:26,852 --> 00:43:27,502
They're baleen.

929
00:43:27,512 --> 00:43:29,962
They're ba- baleen, uh-
Yes … cetaceans, right?

930
00:43:29,962 --> 00:43:33,042
And so, but I know that a lot of
their feeding style is different.

931
00:43:33,152 --> 00:43:34,612
They go down into the sediment.

932
00:43:34,662 --> 00:43:38,152
They kinda grab a bunch of stuff,
and then bring it to the surface, and

933
00:43:38,152 --> 00:43:39,682
then kinda eat whatever's in there.

934
00:43:39,692 --> 00:43:41,202
It could be critters and things like that.

935
00:43:41,542 --> 00:43:43,936
Mostly, they travel all the
way to the Arctic to do that,

936
00:43:43,956 --> 00:43:44,876
and then they come back.

937
00:43:45,386 --> 00:43:48,696
Uh, a lot of people are saying just
because of the way things are happening

938
00:43:48,696 --> 00:43:52,152
up north, and the melting of the ice
is happening a little earlier than they

939
00:43:52,152 --> 00:43:55,392
expect before they get there, some of the
plankton's not there when they grab stuff.

940
00:43:55,642 --> 00:43:58,762
so it might just be that their energy
levels are just a little lower when

941
00:43:58,762 --> 00:44:00,592
they do these large migrations.

942
00:44:01,102 --> 00:44:06,052
Do you think in the future, 'cause I
know that might be way off right now,

943
00:44:06,132 --> 00:44:09,982
this measuring of heart rate through
these loggers and maybe other things

944
00:44:09,982 --> 00:44:15,052
as you go along and as you develop this
more could give researchers insight

945
00:44:15,082 --> 00:44:20,692
into how their energetic resources
are impacted by not enough food.

946
00:44:20,702 --> 00:44:24,202
Or even with the ones that, you know,
you, mentioned earlier with the blue

947
00:44:24,222 --> 00:44:28,409
whales and like these concentrated
patches, if they're not there, how does

948
00:44:28,409 --> 00:44:33,929
that affect their physiology in, uh, i.e.
heart rate at a most basic level when

949
00:44:33,929 --> 00:44:36,139
they don't get the food that they need?

950
00:44:36,139 --> 00:44:36,479
Yeah.

951
00:44:36,589 --> 00:44:38,299
And like what's the detection there?

952
00:44:38,299 --> 00:44:42,419
And like what's that point
where survival, non-survival?

953
00:44:42,449 --> 00:44:45,429
Like is that- Right … something
that has ever been discussed or

954
00:44:45,439 --> 00:44:48,679
could be a possibility way down
in the future, do you think?

955
00:44:48,981 --> 00:44:49,391
Yeah.

956
00:44:49,391 --> 00:44:52,351
I think way down in the
future it could be possible.

957
00:44:52,381 --> 00:44:55,651
We have tools that can answer
that question much better

958
00:44:55,651 --> 00:44:56,711
than heart rate can- Oh

959
00:44:56,711 --> 00:44:56,871
at this point.

960
00:44:56,901 --> 00:44:57,241
Gotcha.

961
00:44:57,351 --> 00:45:01,751
So, like, there are a lot of folks,
including in our lab, who use drones to

962
00:45:01,751 --> 00:45:03,671
track- Right … animal body condition.

963
00:45:03,991 --> 00:45:04,021
Right.

964
00:45:04,101 --> 00:45:08,941
Or even, uh, one of the students in our
lab has looked at, because some of the

965
00:45:08,981 --> 00:45:12,831
gray whales that she studies feed in
tidal cycles- Yeah … when the water

966
00:45:12,831 --> 00:45:17,651
sort of pulls back, she can measure
the pits they make and how many- Oh

967
00:45:17,651 --> 00:45:21,191
count how many instances they feed
and estimate how many calories

968
00:45:21,471 --> 00:45:22,391
they're getting from each of those.

969
00:45:22,431 --> 00:45:25,901
So there's a lot of tools that could
definitely answer that question better

970
00:45:25,901 --> 00:45:27,251
and more quickly than heart rate.

971
00:45:27,601 --> 00:45:32,861
But- Gotcha … just, like, to say
something about it, I think the thing we

972
00:45:32,861 --> 00:45:36,621
would look for in heart rate data that
could be helpful to that question of,

973
00:45:36,971 --> 00:45:41,571
you know, potentially long-term stress,
are these metrics that we think about

974
00:45:41,571 --> 00:45:42,861
in humans called heart rate variability.

975
00:45:42,861 --> 00:45:43,601
Mm-hmm.

976
00:45:43,971 --> 00:45:46,541
So this might be something you've
heard of before, but essentially-

977
00:45:46,551 --> 00:45:50,071
Mm-hmm heart rate variability, you
want heart rate variability to be high.

978
00:45:50,091 --> 00:45:55,031
A high heart rate variability shows that
your sort of rest and digest and fight

979
00:45:55,031 --> 00:46:00,511
or flight systems are- Yeah … both at
play and one is not dominant over the

980
00:46:00,511 --> 00:46:02,291
other, which generally reflects health.

981
00:46:02,701 --> 00:46:06,411
and I think there's some interesting
work that can be done there with whales.

982
00:46:06,451 --> 00:46:10,271
And once we have bigger data sets
over longer time scales, those

983
00:46:10,341 --> 00:46:14,061
types of measurements might be
informative about long-term stress.

984
00:46:14,331 --> 00:46:15,121
we're not there yet.

985
00:46:15,141 --> 00:46:17,391
It's definitely a far
off in the future thing.

986
00:46:17,441 --> 00:46:17,701
Yeah.

987
00:46:17,741 --> 00:46:20,531
But I think, there's work that
could be done, and I think- Yeah

988
00:46:20,531 --> 00:46:23,181
there's definitely heart rate
data that could be informative

989
00:46:23,251 --> 00:46:24,571
about long-term stress.

990
00:46:25,171 --> 00:46:28,931
It's amazing what we can find out from
just, like, drones or a heart rate monitor

991
00:46:28,931 --> 00:46:33,311
or something like that about these massive
animals, and, uh, still quite mysterious

992
00:46:33,311 --> 00:46:37,651
it, it appears to be, but we're,
getting to know them bit by bit and, um-

993
00:46:37,791 --> 00:46:38,141
Exactly

994
00:46:38,211 --> 00:46:40,341
ECG by ECG- which is kinda cool.

995
00:46:40,381 --> 00:46:42,144
so yeah, no, this is, fantastic.

996
00:46:42,414 --> 00:46:46,298
you know, like, I guess one thing that
I'd love to ask is, like, when you find

997
00:46:46,298 --> 00:46:50,618
something like this, like the heart rates
and how they change, you know, as they're

998
00:46:50,628 --> 00:46:53,868
lunge feeding, like, from a scientific
point of view for you as a scientist

999
00:46:53,878 --> 00:46:57,618
personally, like, what goes through
your mind when you kinda come out with

1000
00:46:57,618 --> 00:46:59,888
these two cool outcomes from this paper?

1001
00:47:00,582 --> 00:47:01,012
Yeah.

1002
00:47:01,402 --> 00:47:02,862
It's a fun question to think about.

1003
00:47:02,862 --> 00:47:07,428
I think on the day-to-day,
you don't realize, you know,

1004
00:47:07,438 --> 00:47:09,338
maybe how novel something is.

1005
00:47:09,958 --> 00:47:13,638
Um, because of course we expect these
tags to go out and collect this, and

1006
00:47:13,638 --> 00:47:16,658
collect heart rate data, and then it's
there, and then you're like, "Woo,

1007
00:47:16,858 --> 00:47:18,248
it did the thing we wanted it to do."

1008
00:47:19,158 --> 00:47:21,418
And then you analyze it, and
you write a paper just like

1009
00:47:21,418 --> 00:47:22,618
you've always written papers.

1010
00:47:23,018 --> 00:47:26,368
But then I think, you know, it's at
these times when something like this

1011
00:47:26,368 --> 00:47:29,698
comes out, and you take a step back,
and you're explaining it to folks who

1012
00:47:29,698 --> 00:47:32,988
are generally curious and maybe not
sort of in the weeds of the field.

1013
00:47:33,428 --> 00:47:37,058
And, yeah, you get that sense of
awe and curiosity about learning

1014
00:47:37,178 --> 00:47:38,818
new things about awesome animals.

1015
00:47:39,118 --> 00:47:39,318
Yeah.

1016
00:47:39,318 --> 00:47:44,458
That I think sometimes it hits and you
realize, "Wow, this is amazing." Yeah.

1017
00:47:44,468 --> 00:47:48,088
And I feel very proud and
inspired to be able to contribute

1018
00:47:48,088 --> 00:47:49,208
to the field in this way.

1019
00:47:49,588 --> 00:47:50,218
So- That's

1020
00:47:50,218 --> 00:47:50,478
awesome

1021
00:47:50,478 --> 00:47:52,098
…
yeah, it, it's very motivating.

1022
00:47:52,108 --> 00:47:54,768
It's motivating- I can imagine … to get
something like this to the finish line.

1023
00:47:55,258 --> 00:47:55,328
Um.

1024
00:47:56,028 --> 00:47:56,428
Yeah.

1025
00:47:56,468 --> 00:47:56,758
Yeah.

1026
00:47:56,918 --> 00:47:57,938
Not easy stuff to do.

1027
00:47:57,948 --> 00:48:00,748
Like, you know, there's a lotta, stuff,
a lot of variables that go into that,

1028
00:48:00,748 --> 00:48:05,518
and, uh, you know, from the field, from
like working up the data and everything.

1029
00:48:05,518 --> 00:48:09,418
and, uh, we appreciate you coming on,
Ashley, to kinda tell us all about it

1030
00:48:09,418 --> 00:48:11,128
and what goes into a study like this.

1031
00:48:11,128 --> 00:48:14,128
And, um, it's been a while where
I've had somebody come on and we talk

1032
00:48:14,138 --> 00:48:16,878
about like getting into the science
of it and the physiology of it.

1033
00:48:17,138 --> 00:48:19,948
Uh, and I think that's a lot of fun
for I know my audience, I know for me.

1034
00:48:20,218 --> 00:48:22,708
Uh, and so I'm sure the audience
is gonna enjoy it as well.

1035
00:48:22,708 --> 00:48:25,888
We'd love to have you back on or even
some of your lab mates back on to

1036
00:48:26,138 --> 00:48:30,238
talk about more about these massive
giants and, uh, talk more about, what

1037
00:48:30,238 --> 00:48:32,668
you guys are discovering about them
that we could learn from them as well.

1038
00:48:32,678 --> 00:48:36,408
So, um, you're always welcome on, just
let me know, uh, when you- Oh, thank you

1039
00:48:36,408 --> 00:48:37,018
… when you wanna come on and when you

1040
00:48:37,018 --> 00:48:37,234
have something new.

1041
00:48:37,234 --> 00:48:37,378
That's awesome.

1042
00:48:37,378 --> 00:48:38,918
Yeah, you may take that offer back.

1043
00:48:38,918 --> 00:48:42,478
I, I've got- … a lot of friends who
can say a lot about blue whales, so,

1044
00:48:42,868 --> 00:48:44,408
That's… Well, that's not a bad thing.

1045
00:48:44,408 --> 00:48:45,318
I think I like that.

1046
00:48:45,318 --> 00:48:45,648
Yeah.

1047
00:48:45,648 --> 00:48:46,228
that's great.

1048
00:48:46,448 --> 00:48:47,778
Uh, so thank you very much, Ashley.

1049
00:48:47,778 --> 00:48:49,148
We really appreciate your time.

1050
00:48:49,208 --> 00:48:52,028
and thank you for bringing this
information and the work that you do.

1051
00:48:52,278 --> 00:48:53,648
can't wait to have you back on.

1052
00:48:53,678 --> 00:48:55,278
And, uh, yeah, it wa- it was great.

1053
00:48:55,738 --> 00:48:56,088
Awesome.

1054
00:48:56,088 --> 00:48:57,128
Thanks so much for having me.

1055
00:48:57,128 --> 00:48:57,798
I appreciate it.

1056
00:48:58,148 --> 00:48:58,668
You bet.

1057
00:48:59,135 --> 00:49:02,095
Thank you so much, Ashley, for
joining us on today's episode of the

1058
00:49:02,095 --> 00:49:03,345
How to Protect the Ocean podcast.

1059
00:49:03,345 --> 00:49:08,535
It was great to be able to talk to someone
talking about science and physiology and

1060
00:49:08,535 --> 00:49:13,845
sort of all the questions that can come
out of a study like this, where there

1061
00:49:13,845 --> 00:49:15,915
are so many answers that we could get.

1062
00:49:16,135 --> 00:49:19,715
These little devices that these
engineers are creating, to measure

1063
00:49:19,725 --> 00:49:24,885
heart rates, to measure, you know,
body size from, uh, drones and pictures

1064
00:49:24,885 --> 00:49:27,415
and all these algorithms and stuff
that are done are just phenomenal.

1065
00:49:27,415 --> 00:49:31,125
It's being able to answers we didn't
have yet about these animals, and to

1066
00:49:31,125 --> 00:49:35,658
understand them better, to maybe even
help better understand us and how we work

1067
00:49:35,668 --> 00:49:37,528
and how we could work in the futures.

1068
00:49:37,538 --> 00:49:39,098
Lots of stuff that's coming.

1069
00:49:39,098 --> 00:49:42,768
Uh, Ashley, uh, and I were talking
after, and talking about hooking me

1070
00:49:42,768 --> 00:49:46,098
up to a couple people I could talk
to that really look at whales and

1071
00:49:46,098 --> 00:49:49,598
people and the differences and how
that could be beneficial to people.

1072
00:49:49,788 --> 00:49:51,308
Uh, it's gonna be a lot
of fun in the future.

1073
00:49:51,308 --> 00:49:54,528
So thank you, Ashley, for not only coming
on, but also giving us those contacts.

1074
00:49:54,528 --> 00:49:57,338
And all the people at Stanford, at
the Doerr School of Sustainability

1075
00:49:57,338 --> 00:49:59,828
and the Hopkins Marine Center,
for all the work that you do.

1076
00:49:59,978 --> 00:50:03,358
It's just great to be able to find out
more and more about these animals, more

1077
00:50:03,358 --> 00:50:06,378
and more about our environment, and
how these animals interact with them.

1078
00:50:06,408 --> 00:50:07,668
It's so cool to see.

1079
00:50:07,858 --> 00:50:11,268
Uh, we love whales, especially big whales,
the giants, the giants of the sea, folks.

1080
00:50:11,278 --> 00:50:12,458
That's what it's really all about.

1081
00:50:12,728 --> 00:50:13,918
Uh, but again, thank you so much.

1082
00:50:13,918 --> 00:50:16,558
I'm gonna put links to the
website and, uh, to the papers.

1083
00:50:16,888 --> 00:50:19,758
Uh, and then also links to, of course,
my socials if you have any questions

1084
00:50:19,758 --> 00:50:23,448
or, comments about the episode, you
can go ahead and hit me up, uh, DM

1085
00:50:23,458 --> 00:50:27,538
me on TikTok, Instagram, Facebook,
LinkedIn, wherever you can find me.

1086
00:50:27,758 --> 00:50:30,418
Uh, and of course, if you're
on Spotify and you wanna make a

1087
00:50:30,418 --> 00:50:32,538
comment, uh, you know, you can do so.

1088
00:50:32,538 --> 00:50:34,218
I'll be more than happy
to respond when I can.

1089
00:50:34,598 --> 00:50:36,945
Uh, and, uh, yeah, I can't wait
to ta- chat with you about, all

1090
00:50:36,945 --> 00:50:38,715
this stuff and how cool it is.

1091
00:50:38,725 --> 00:50:41,605
So thank you so much for joining
me on today's episode of the How

1092
00:50:41,605 --> 00:50:42,685
to Protect the Ocean podcast.

1093
00:50:42,695 --> 00:50:43,605
I'm your host, Andrew Lewin.

1094
00:50:43,615 --> 00:50:44,245
Have a great day.

1095
00:50:44,245 --> 00:50:46,385
We'll talk to you next time,
and happy conservation.