0:03

Hey

0:09

Marty, what's the weather like in Florida these days?

0:11

Oh, of course, as you know,

0:14

pretty much perfect. Partly cloudy, nice

0:16

and toasty outside. How about you?

0:18

Up in Norway? Ugh, it

0:20

is brutally cold. But

0:22

you know what? The house sparrows in my yard don't

0:25

seem to mind. Every time I

0:27

look out, they seem to be happy and I

0:29

think about you a lot when I stare at

0:31

those house sparrows. I think

0:33

about all the different habitats and climates house

0:36

sparrows have colonized and how lucky you are

0:38

that you get to study them all around

0:40

the world. Yes, we are incredibly

0:42

lucky to be able to study this species

0:44

all over the world. Kenya, Vietnam, Senegal, Israel.

0:47

It's really been a fantastic project. We're really

0:49

lucky to be doing it. It's

0:51

such an interesting species for trying

0:53

to understand the processes that have allowed

0:56

for colonization of new environments. Super

0:58

cool. But I have to confess,

1:00

there is another sparrow I think is a

1:03

lot more interesting. What? How

1:05

can another sparrow be more interesting than

1:07

the house sparrow? Alright, well, I'm fascinated

1:09

by the Eurasian tree sparrow. It was

1:11

also introduced to North America. But

1:13

unlike its close relative, the house sparrow,

1:16

it's been unable to spread beyond the area

1:18

surrounding the city of St. Louis. The

1:21

two species look so alike. Why was one

1:23

species able to expand its distribution around the

1:25

world while the other species has remained local?

1:28

I have lots of thoughts on this topic, and

1:30

we could do a whole episode on it.

1:32

But the tale of these two sparrows highlights

1:35

really big research questions about the ecological and

1:37

evolutionary processes that constrain or

1:39

facilitate how populations expand their

1:41

geographic distributions. This question

1:44

is not only an academic one. Understanding

1:46

what controls the spread of invasive species

1:48

has vastly important consequences for human health.

1:51

Think mosquitoes, food security,

1:54

think agricultural pests, and

1:57

biodiversity. Not only that, these ideas

1:59

are also... important when humans decide to

2:01

purposefully introduce species into new environments.

2:04

A classic, but kind of a cob example,

2:07

is the introduction of the myxoma virus to

2:09

Australia. Myxoma was a very

2:11

effective pathogen of rabbits in South America,

2:14

so when rabbits got out of control in Australia,

2:16

this virus massively reduced populations.

2:19

Now the pathogen has evolved to be

2:21

more benign, so rabbits are far

2:23

more numerous than most Australians would

2:25

prefer. On this episode of Big

2:28

Biology, we talk to Ruth Huffbauer, a

2:30

professor of applied evolutionary ecology at Colorado

2:32

State University. We talk

2:34

to Ruth about the evolution of new populations

2:36

as they colonize new areas. Ruth's current research

2:39

addresses the complex interactions that determine

2:41

how some populations come to establish

2:44

and sometimes thrive in new areas.

2:46

Ruth is interested in this invader

2:48

evolution for two reasons. First,

2:51

colonization is generally interesting to

2:53

understand. It's how all new

2:56

populations get going and never got going

2:58

in the past. Take the genetic paradox

3:00

of invasions. How do new populations ever

3:02

get established and take off from a

3:04

small number of founders? Clearly

3:07

some populations establish, but

3:09

how they overcome genetic bottlenecks, founder

3:11

effects, and all sorts of other

3:14

challenges of being the first to

3:16

arrive has perplexed biologists for decades.

3:18

And don't forget our favorite concept,

3:20

plasticity. Yes, how

3:22

do invading populations ever adapt to

3:24

new conditions if the plastic responses

3:26

that help them colonize also shield

3:28

those individuals from the selective forces

3:30

in the new areas? Another

3:33

paradox. Ruth's other major interest in invaders

3:35

has a practical bent. The applied dimension

3:37

of her job title, right? Right.

3:39

So many successful invaders are successful

3:41

at the expense of resident species,

3:44

including humans. A major

3:46

focus of Ruth's research has been to

3:48

use her experimental evolutionary work on beetles

3:51

to reveal the conditions that facilitate spread

3:53

of these pests. On today's show, we

3:55

talk with Ruth about beetle adaptation, plant

3:57

and animal invasions, and climate-related plasticity. We

4:01

also discuss how this work is giving

4:03

resource managers valuable insights into how to

4:05

mitigate the effects of all sorts of

4:07

different pest species. But before we get

4:09

started, please remember that we're a nonprofit

4:11

and to be brutally honest the Big

4:13

Biology coffers are getting dangerously low. We

4:16

really want to keep making a podcast but we

4:18

can't do it much longer without your financial help.

4:20

You can help us out by becoming a patron

4:22

at www.patreon.com/big

4:25

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4:27

There you can set up a monthly donation

4:29

of one, two, five, ten, twenty

4:32

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4:34

working on revisiting tier benefits and we'll

4:36

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4:39

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4:41

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4:44

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4:46

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4:48

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4:51

support us by telling a friend about the

4:53

show or talking about us on social media.

4:56

Remember, Art Can and me are fine as

4:58

our universities cover our bills, but the rest

5:00

of our team members, Smally our producer, Dana

5:02

our social media expert, and Katie our artist

5:04

are supported by you. We're really grateful for

5:06

your help. I'm Marty Martin

5:08

and I'm Cameron Gallenbor and you are listening

5:11

to Big Biology. Ruth

5:26

Huffbauer, welcome to Big Biology. We're

5:28

so excited you could join us

5:30

today to talk about your work

5:32

on invasive species, biocontrol, and range

5:35

expansions. Let's

5:37

jump right in. Why should we

5:39

care about climate-driven range shifts

5:42

of species, insecticide, or

5:44

antibiotic resistance as examples

5:46

of rapid

5:48

contemporary evolution? What

5:51

are your thoughts on how this kind

5:53

of rapid evolution, how it's differentiated from

5:56

sort of long-term examples of

5:58

evolution? Does it change the way evidence

8:00

for adaptation in these rapid contexts. What's

8:02

the sort of gold standard or what's

8:04

a really good example that

8:06

we might focus on? Yeah,

8:09

sticking with adaptive

8:11

evolution. So now

8:13

we're just thinking of a gold

8:15

standard for rapid adaptation as

8:17

opposed to range

8:20

expansion, for example. There

8:22

can be comparisons through

8:24

time in natural populations,

8:27

sticking with insecticide resistance or

8:30

herbicide resistance. A population

8:32

is controlled and then the next

8:34

year the population isn't controlled, for

8:36

example. But

8:38

what I often do is

8:40

work with the model system

8:43

and then you can create

8:45

environments where populations can evolve

8:48

or not and not let some of them

8:50

evolve. And so that's a

8:52

really, really direct way to look

8:54

at the action of evolution and

8:57

have an actual control because in

8:59

nature we typically

9:02

don't have kind of a control

9:04

scenario where something isn't evolving. Everything

9:07

is evolving all of the time. Right. I

9:09

mean, that's even hard for me to get my head

9:11

around. So tell us more about these systems where you

9:14

stop evolution so you can get

9:16

a better handle on, well, yeah, that's

9:18

adaptation. What kind of systems are

9:20

you talking about? What do your experiments look like? So

9:23

I work with an

9:25

insect as a model system, a tribolium

9:27

beetle. So this is a flower beetle.

9:30

So we're talking about flower that you

9:32

make your cookies with, not flowers that

9:34

you give your sweetheart. And

9:37

so it feeds on flower, all

9:39

sorts of grains, actually. And

9:41

so we can use

9:44

that in the lab to create

9:47

experimental environments. In nature these

9:49

days it's a pest in

9:51

grain silos. And

9:53

then in the lab we can have very

9:55

controlled environments where we can give it different

9:58

kinds of media or mixed- of

10:00

different kinds of flour with different

10:02

amounts of brewer's yeast. And

10:06

I like this as an

10:08

experimental system because they're deployed

10:10

and obligately sexually reproducing.

10:14

There have been tons of really

10:16

elegant, really cool experiments looking

10:19

into the role of rapid evolution,

10:21

rapid adaptation to novel

10:24

environments in lots of microbes. And those have

10:26

revealed a ton of important things. And the

10:28

way that they can have controls is they

10:30

can freeze them, stick them in the freezer,

10:33

and then let a subset evolve,

10:35

then resurrect the ones that are frozen

10:37

and compare them. And

10:40

so that is great. It's super rigorous.

10:42

But a problem is, if we're as

10:45

humans, often the things that

10:47

we're concerned about have obligate

10:50

sexual reproduction and are

10:52

deployed or have more complex genomes.

10:55

And so inbreeding happens, and inbreeding depression

10:57

happens, and all of these, there are

10:59

things that are going on within those

11:01

genomes that are different from a lot of

11:04

the microbial systems. And so

11:06

with tribolium, I feel like I can kind

11:08

of model better things that

11:10

we're concerned about managing in

11:12

nature and also things we're concerned about

11:15

conserving in nature. And

11:17

I completely neglected to say, how

11:19

do you control evolution? Well, that's

11:21

fine. I mean, we needed to know the background of the system

11:23

to get our heads around the rest of it. So

11:26

that was good. So how do you control evolution

11:28

with something that's obligate sexual

11:30

is really laborious. You

11:32

have amazing students in

11:35

the lab. So what

11:37

we can do is have

11:40

populations that are

11:42

kind of running side by side

11:44

with experimental ones that are evolving,

11:46

where we replace individuals one for

11:48

one each generation. So

11:51

for example, if we

11:53

release some individuals into

11:55

a novel environment in our experimental

11:58

system, so we might put it on a different. different

12:00

kind of flower, for example, a

12:02

different carbohydrate source. And we're

12:04

interested in adaptation to that carbohydrate

12:06

source. The control populations

12:09

would be released onto that carbohydrate

12:11

source as well. And

12:13

each generation, when we census

12:15

them, so we, that's something else

12:17

that we could talk about if we want

12:19

to, I can get a complete, complete census

12:21

data. So we know how the populations are

12:23

growing or declining. We replace

12:26

them with individuals that aren't

12:28

in that environment. So each

12:30

generation, they're replaced one for

12:32

one. So the population demography

12:36

is essentially unchanged, but there's

12:38

no longer a continuous line

12:40

of descent and evolution. And

12:43

so Ruth, in these kinds of experiments,

12:45

like so in your experimental populations, do

12:48

you generate like unique family

12:50

lines where you're controlling

12:54

the kind of genetic variation that

12:56

goes into the experiment, or are

12:59

you trying to sort of maximize

13:01

the genetic variation by just like

13:04

putting in a whole bunch of outbred individuals?

13:06

It really depends on the experiment. We've

13:09

done both of those things. So with

13:11

some experiments saying kind of if

13:14

you start with a genetically diverse

13:16

population, how

13:18

does, for example, the number

13:20

of founders influence the ability

13:23

of a population to adapt? And

13:26

then we can simultaneously either

13:28

factorial or in a different experiment say, what

13:30

if you start with a population that's

13:32

passed through a bottleneck, either

13:35

a mild bottleneck or a really extreme

13:37

bottleneck? We know that that should

13:39

influence their ability to adapt, right? And so

13:42

it's nice to be able to control, have

13:45

the evolutionary control to know

13:47

how much of that adaptation

13:49

is or inability to adapt

13:52

is due to the initial founder effect

13:54

and how much of it is the

13:56

genetic background of the individuals that are

13:58

founding that population. So I

14:00

think that's a good transition to talking

14:03

a little bit more specifically about range

14:05

expansions. And that's something that

14:08

Marty and I talk about a lot either

14:10

in house sparrows or guppies. One

14:13

concept in this range expansion

14:16

literature is the idea of

14:18

a pushed versus a

14:20

pulled expansion. Can

14:23

you talk a little bit about those and

14:25

which one you think is more common

14:27

in nature? Yeah.

14:31

So as a range expansion

14:33

happens, individuals

14:35

that disperse out to

14:37

the front of the

14:40

range expansion are individuals

14:42

that were good at that dispersal.

14:44

And so then they get out

14:46

to the edge of that range

14:49

expansion and they mate with

14:51

each other and their offspring then

14:53

continue that range expansion. So you

14:55

can have a pulled

14:57

range expansion by

14:59

individuals colonizing,

15:01

good dispersers colonizing out at

15:04

the front of those patches,

15:06

bringing the population,

15:08

pulling the population along

15:10

behind them. And then

15:12

pushed range expansions are the population

15:14

growth is really behind the front

15:17

is kind of deriving what's happening.

15:20

And some similar processes are happening,

15:22

but there can be, for example,

15:25

positive density dependent dispersal that

15:27

then as the population grows, individuals

15:29

are kind of pushed out of

15:32

that high density patch and out

15:34

into further space. In

15:36

those cases, I mean, I think the expectation

15:38

would be that that's sort of a more

15:41

random. Some fraction of the population gets

15:44

moved. They're not necessarily obvious pioneers in

15:46

the sense that longer legs or wings

15:48

are the things that we've seen in

15:50

many different systems. Is that

15:53

fair? Yeah, I think probably

15:55

less so. We like to think about

15:58

the extremes to understand. understand

16:01

we as in we humans or we scientists

16:03

like there's this and there's that so we

16:05

like to think about the Contrast and what's

16:07

at the ends of the continuum, but really

16:09

those individuals that are pushed out Might

16:12

be the ones that have

16:14

some particularly tendency or ability

16:16

to disperse Do you

16:18

sort of have a sense of what's more

16:21

common in in range expansions? Is

16:23

it really maybe not a dichotomy or is

16:25

it this kind of? Continuum

16:27

or even in the same system

16:29

it could be both processes happening

16:31

at different times Yeah,

16:34

I think there can be both processes

16:36

happening at once, but it depends I

16:38

think which dominates can

16:41

depend upon the environment

16:43

so pain codes they

16:46

have expanded

16:49

largely though not entirely across a

16:51

fairly continuous environment and lots of

16:53

the Experimental work I've done

16:56

with triboleum and other folks have done

16:58

have had constant environments

17:01

and in that case There's nothing

17:03

that is going to slow the

17:05

the range expansion down necessarily if

17:07

as the populations if they're adapted

17:09

to that environment Then

17:11

this pulling by the dispersers out

17:13

in front can be particularly powerful.

17:16

All right, so let's Put

17:18

these things together and get into the

17:21

genetic details of triboleum You know these

17:23

various different systems to the extent we

17:25

know them I want to do that

17:27

from the perspective of what we all

17:29

know is the invasion paradox. So bear

17:31

with me I mean, I know you

17:33

know this story but to get all

17:35

of the listeners on board I mean,

17:37

how do we think about the roles

17:39

of mutation and various sources of genetic

17:41

variation and invasions? Because introduced populations are

17:43

small and so because they're small Stronger

17:46

effects of genetic drift and general

17:48

mutation rates are not super high

17:51

So how do these new populations

17:53

really ever get going especially on

17:55

the path to adaptation? How do

17:57

we resolve this paradox maybe from

17:59

the perspective of Stan? ending variation

18:01

versus mutation? Yeah,

18:03

I think there are a lot of things going on. So

18:07

one thing is that new populations come

18:09

in that might become

18:11

the next big invader, say,

18:14

or might dwindle to extinction.

18:17

First of all, the ones

18:19

that dwindle to extinction we generally don't

18:21

see. So there

18:23

might be lots of introductions that are

18:26

happening of tiny little, you know, little

18:28

propagules here, little groups of propagules there.

18:31

And they just, they dwindle to

18:33

extinction. They don't have what the

18:35

genetic variation to adapt to their

18:37

new environment. The ones that we

18:39

see that are able to establish multiple

18:42

things can happen. One, there can

18:45

be introductions from one part of a

18:47

range, introductions from another part of a

18:49

range. And so the standing genetic variation

18:51

in each of those small groups of

18:53

propagules might be low. And

18:56

by propagules, like we could be seeds, it

18:58

could be eggs, it could be the actual

19:01

individuals. But then

19:03

when they meet up in the introduced range

19:05

and out cross with

19:07

each other, then there could be actually much

19:09

higher genetic variation than found

19:12

in most native populations. So that's one thing

19:14

that can happen. Another thing

19:16

that's important to remember is

19:18

that if bottlenecks are short,

19:21

most heterozygosity remains.

19:25

So rare alleles,

19:27

rare mutations are lost, but most of

19:29

the heterozygosity is still there in a

19:31

short bottleneck. And that's just kind of

19:34

a fundamental of population genetics that

19:37

I think people forget how much variation

19:39

is actually retained. Another

19:41

thing is that if the population

19:43

is able to, so maybe it's

19:45

not poised to

19:47

adapt and

19:49

become the next big invader, but

19:52

maybe the match of

19:54

the environment is good enough that that

19:56

doesn't matter so much. If the population

19:58

is able to grow reasonably. mutation,

20:01

even though mutation rates are

20:03

low, mutation does, it adds

20:05

variation. It does add genetic

20:07

variation there. There's now good

20:10

data and I think a

20:12

beginning of a shift to

20:14

kind of discounting mutation as

20:16

a source of genetic variation

20:18

in contemporary evolution to realizing that

20:21

no, in fact, mutation is contributing.

20:23

At least some as when populations

20:26

get large and if generation times

20:28

are rapid, then from the human

20:30

perspective, you know, there's many

20:33

individual mutations can happen over the

20:35

course of a relatively few years

20:38

because so many generations,

20:40

so many individuals in some

20:43

population of insects, for example. Can

20:45

you say more about the kinds of

20:47

mutations that happen? Because I mean, I,

20:49

this is not something that I'm really so

20:52

much into, but my lab has started to focus a

20:54

lot on particular forms of

20:56

gene regulation. Are there any kind

20:58

of traits of mutations, special forms

21:00

of mutations that play into

21:02

the success of colonizing populations? I

21:05

wouldn't say we're there yet or if we are,

21:07

then I don't have that knowledge. It's

21:10

true that a lot of what we

21:12

do see are deleterious mutations, so that's

21:14

not necessarily playing into the success, but

21:16

they, we know the mutations are

21:18

happening and so, you know, if

21:20

you have a kind of a

21:23

typical distribution of fitness effects of

21:25

those mutations, some of them

21:27

will be beneficial. This is

21:29

something, yeah, Marty and I have been talking about

21:31

lately and we've started to think about this a

21:33

lot more in our own research and I've been

21:35

talking to other people about this in the context

21:38

of the ability of

21:40

guppies, for example, to adapt to

21:42

new environments, seemingly from very low,

21:45

small populations and low genetic variation

21:47

and I think, I just finished

21:49

reading, rereading, I guess now for

21:52

the second time, Barbara McClintock's biography,

21:54

A Feeling for the Organism, and

21:57

more with an eye towards her thoughts on...

22:00

transposable elements role in adaptive

22:02

evolution. And I think in the

22:04

context of sort of what we know now, with

22:07

modern molecular tools, that the

22:09

idea of the genome as far

22:11

more dynamic, I think, than you

22:13

would think of in our traditional

22:15

population genetic models. And there's

22:18

all kinds of crazy stuff happening in there. And

22:21

sometimes it's predictable and a lot

22:23

of times it's not, but yeah,

22:26

it just, in the context of

22:28

like this invasion paradox and these

22:30

cases of repeated adaptive

22:32

evolution from small

22:35

effective population sizes, it

22:37

does make me wonder what role

22:40

these kinds of structural, big

22:42

genetic changes might be playing. Yeah,

22:46

absolutely. I think huge

22:48

roles, like you said, it's so

22:50

much more complicated than a traditional

22:53

population genetics paradigm would have. And

22:55

there are also things like, have you

22:58

seen any of the stuff

23:00

from my department on the

23:02

evolution of herbicide

23:04

resistance, where

23:06

there's extra chromosomal

23:09

DNA that

23:12

has increased in copy number. There

23:15

are these little bits of DNA floating

23:17

around the cells. And

23:19

if plants have a lot of copies

23:21

of them, then they're able to basically just, they're

23:24

able to resist the herbicide in the, I

23:27

would say they tolerate it from kind

23:29

of my academic

23:31

background, but the terminology used within weed

23:33

science is resistant. So the plants are

23:35

able to grow just fine. And it's

23:37

just this copy number of these little

23:40

things that are outside of the chromosomes

23:42

and repeat and repeat and repeat and somehow

23:44

get duplicated. And they're having

23:46

huge effects on rapid, huge and

23:49

rapid effects on phenotypes. And

23:51

there's nothing Mendelian about it. Well,

24:05

this is a good point, I think, to

24:08

bring up another of our favorite

24:10

topics, plasticity, because

24:12

this copy number variation is often driving, you

24:15

know, quantitative variation in expression. I mean, not

24:17

the only method that can be involved here,

24:19

but how are you thinking about plasticity

24:22

these days? Because it's another one of these, it

24:24

brings in another one of these paradoxes, right, where

24:27

the plastic organisms are presumably

24:29

really good colonizers, and yet

24:32

plasticity itself is

24:34

a well-known mechanism to buffer selection, so

24:36

the whole adaptation thing becomes kind of

24:39

complicated if plasticity is initially favorable. So

24:41

how are you thinking about that now?

24:44

Yeah, plasticity I find to

24:46

be kind of a mind bender. Is

24:52

it a trait? Is it an outcome? One

24:55

of the ways I'm thinking about it now is

24:58

with a specific study

25:00

system is that it's kind

25:02

of in this study system, I'm

25:04

thinking about it, I don't

25:06

know, is it a trait or is it an outcome, and

25:09

is it buffering selection or is it

25:11

a response to selection? It's a response

25:13

to selection in this system I'm thinking

25:15

about. So one of the natural

25:18

systems, natural as in a not

25:20

an experimental in the lab system,

25:23

that I'm working on is

25:25

a biological control agent that

25:27

was released against the invasive

25:29

shrub called tamarisk that

25:31

is in river systems across

25:34

western North America. So

25:37

the cool thing about that in terms

25:40

of doing research on it is

25:42

that with range expansion, sometimes it's

25:44

hard to find true replicates, but

25:47

with this, because it's in different

25:49

river systems, we can have different

25:51

range expansions that are going down. We

25:53

can't with a tribolium system, I'll replicate

25:55

things 20 to 40 times. It's

25:58

not like that, but at least we can. and say, okay,

26:01

these three river systems are

26:03

distinct replicates of this range expansion.

26:07

And there, the beetle is expanding

26:09

its range from the north to the

26:12

south, which is also a little bit

26:14

unusual in this era of climate change,

26:16

at least for the northern hemisphere. We're

26:18

often thinking of things moving further

26:20

north with climate

26:22

change. And here, they

26:25

were released into areas

26:27

in Colorado, for example,

26:30

and Wyoming, and then are

26:32

spreading southward where there's more habitat,

26:34

more of this tamarisk lead for

26:36

them to spread onto. So these

26:38

beetles, they have to go into diapause

26:41

to survive the winter. So diapause is

26:43

like hibernation, but for an arthropod.

26:47

And the cue that

26:50

winter is coming is

26:52

that the light changes. So

26:55

in Colorado, we have nice long days

26:57

in the summer, and then as fall

26:59

comes, the days get shorter and

27:01

shorter and shorter, and the short days

27:04

precede the hard frosts.

27:07

And so those short days cue beetles to

27:09

go into diapause, hide in the literature.

27:11

In the literature, that is so funny.

27:14

Hide in the litter. I

27:20

clearly need to have another sip of coffee

27:22

or two. And their

27:25

physiology changes, they resorb their

27:27

eggs and bulk up their fat bodies. So

27:31

in the south, however, the

27:33

day lengths are more constant.

27:35

So down in southern

27:38

Arizona, for example, days in summer are

27:40

a little bit longer than days in

27:42

winter, but not that much longer. And

27:45

days in winter are all much closer

27:48

to 12 hours. They're

27:51

all relatively short, whereas in

27:53

summer in Colorado, like you start with a 16-hour day

27:56

and you go down, now I feel like I am

27:59

getting us off on time. much of a tangent, but

28:01

they need to cube into a different day length

28:03

to be able to survive the winter because even

28:05

though it's further south and it's warmer, there's still

28:07

a winter so they risk

28:10

freezing to death and also the leaves fall off

28:12

the tree and so there's not enough for them

28:14

to eat. But the light

28:16

cue sends them

28:18

into diapause basically too

28:21

early because the

28:23

winter temperatures,

28:26

they could in fact have many

28:28

more generations. There's both evolution of

28:30

what light day length regime they

28:33

cue into and there's

28:35

evolution of plasticity in that. So in

28:37

the north, no matter what the temperature,

28:39

it can be a warm sunny day,

28:41

but if the light

28:43

at a certain length, they will

28:45

physiologically start to enter diapause. And

28:48

what my graduate student, a

28:50

graduate student in my lab, Eliza Clark

28:52

has found is that in the south,

28:55

it's temperature dependent. Whether

28:58

a day length sends them into

29:00

diapause depends on the temperature. If

29:02

it's warm, they'll keep going and

29:05

have another generation and if it's cold,

29:07

they'll start the path into diapause. So

29:09

there's this amazing evolution

29:12

of a plastic

29:14

response to day lengths that

29:16

doesn't exist in the north.

29:18

So there the plasticity is

29:20

absolutely part of

29:22

their adaptation to the environment and their

29:24

ability to continue further spread, which is not

29:27

published yet. So you haven't read that one. Okay.

29:31

And also I think really

29:33

resonates with kind of my

29:35

thinking about sometimes plasticity. So

29:37

you have this ancestral plasticity

29:39

that's based on the

29:42

length of day as a cue.

29:44

Right. And so there's already plasticity there

29:47

just in what phenotype

29:49

do you get based on the length of day? Yes. Right.

29:52

But then when you move into this new

29:54

environment, that is

29:56

not adaptive. That type of plasticity

29:58

is not beneficial. in this new

30:01

environment. And so it's initially maladaptive.

30:03

So there must be really strong

30:05

selection to evolve either in

30:07

this case switching to a different

30:09

cue or relying more on a

30:11

different cue. And that initial

30:14

mismatch I think between the phenotype

30:17

that's produced based on the ancestral

30:20

plasticity versus what ends up actually

30:22

evolving is probably what

30:25

generates very strong selection. So

30:28

where does the adaptive evolution come

30:30

from? Where does the strong selection

30:32

come from? It's because it's not

30:34

always adaptive to be plastic or

30:36

at least the plasticity doesn't always

30:38

necessarily help you in these new

30:40

environments because there hasn't been an

30:42

opportunity for selection to act on the variation.

30:45

Absolutely. So another really cool

30:47

part of this story is

30:49

that Eliza actually

30:52

did an old school

30:54

half sib mating

30:56

design and measured heritability in

30:59

a trait related to going

31:01

into diapause in terms of how

31:03

quickly beetles enter diapause

31:06

given a day length. And given

31:09

a day length close to their

31:12

natal environment so that they're

31:14

probably not maladaptive. If they're

31:16

not perfectly adapted to it,

31:19

they're not going to be far off. There

31:21

was a ton of heritability among

31:23

her half sib families. So

31:26

lots of genetic variation and how long

31:28

it took them to go into diapause. You give

31:30

them a day length. So these are relatively

31:33

northern beetles. You give them a day length

31:35

representing a southern fall and

31:38

there was zero heritability.

31:41

Wow. So it's like if

31:43

they move too far, yes

31:45

there's underlying genetic variation. There's stuff going

31:48

on in their genomes. It's

31:50

there. But if you move too

31:52

far, that is not even expressed. But

31:55

if you move a little ways, there's a

31:57

ton of variation selection can act on. Just

31:59

to kind of to clarify like how heritability

32:01

is calculated. So we think

32:04

of heritability as sort of the additive

32:06

genetic component that gets passed on

32:08

from one generation to the next, divided

32:11

by the total phenotypic sort of

32:13

variation in the population. So I'm

32:16

guessing then what's happening is when

32:18

you simulate this very southern environment,

32:21

the amount of total variation

32:23

must get really huge and

32:26

it just swamps out everything else. That

32:29

is exactly what I

32:31

thought would happen and

32:35

when Eliza told me the

32:38

basic pattern, what I thought

32:40

was happening, but it's not,

32:42

it's so strange. The total

32:45

phenotypic variation is very small

32:48

and the additive genetic variation

32:50

is even smaller. There's no

32:53

variation expressed. So

32:55

additive genetic variation goes down and

32:57

total phenotypic variation goes down because

32:59

like you said, like the genomes are

33:02

complicated. Like additive genetic variation, even though

33:04

we think of it as a thing,

33:07

it depends upon the environment, just

33:09

the additive genetic variation. And then

33:12

the phenotypic variation depends on the

33:14

environment as well. And both, I

33:16

expected the additive genetic variations to

33:18

stay the same and total phenotypic

33:20

variation to vary by

33:22

environment, but both of them

33:24

changed radically. But you know, I wonder

33:27

in this case, if traits

33:29

related to diapause fall under

33:31

this kind of like threshold

33:33

model where you

33:36

have to cross some environmental

33:39

cue, some threshold

33:41

before the phenotype can be expressed.

33:43

And so maybe it's one

33:45

of these cases where you don't

33:47

reach the threshold and so then the

33:50

trait doesn't get even expressed. Yeah,

33:52

I think it is very much

33:54

like that. And in this case,

33:56

it's almost, it's a threshold, but

33:58

it's everybody has. completely

34:00

cross that threshold because

34:02

day lengths in the south are shorter. So

34:06

a northern beetle thinks

34:08

to speak very

34:10

anthropomorphically that, oh my

34:12

gosh, I'm so late. I haven't

34:15

gotten into diapause and yet winter

34:17

is already here. Go fast. Everything

34:20

goes into diapause absolutely

34:23

rapidly and there's no variation in

34:25

that. Whereas in

34:28

an environment close to their home

34:30

environment, they're going into diapause at

34:32

all different sorts of timings, different

34:34

rates. Interesting. So, okay, I

34:37

was going to come up with a

34:39

brand new word, but I like you

34:41

guys' explanation. Regulatory overload, but in fact,

34:43

it's more that they're getting a signal.

34:47

Everybody's getting the signal and responding to the

34:49

signal really quickly and diapause just happens for

34:51

everybody. So that's why the heritability declines and

34:54

everything. Can we track back to

34:56

the first part of the story though, because you

34:58

said that the new thing that happens in these

35:00

southern populations is that they're now switching to temperature

35:03

as the more adaptive cue for,

35:05

I mean, presumably adaptive cue for entering

35:07

diapause. Do you think that that kind

35:09

of transition is a common thing in

35:12

general? I mean, it seems to be

35:14

a, it would be a difficult thing

35:16

in a lot of systems for just

35:18

paying attention to some wholly

35:20

new cue, but temperature and day

35:23

length, they correspond reasonably well across

35:25

the landscape in a lot of

35:27

places. So how are

35:29

you guys thinking about that now? Was that transition easy? I

35:32

mean, how do you think it happened? So

35:34

first a point of clarification. They're

35:37

not transitioning wholly to temperature. It's

35:39

a combination of temperature and day length. So

35:41

if temperatures

35:44

are cold and the day lengths

35:46

are long, they will not go

35:48

into diapause. It's that temperature

35:50

is modulating their response to

35:53

day length such

35:55

that they can have a little

35:57

bit more time to feed and reproduce. if

36:00

it's warm and if it's cold and

36:02

the days are short, they'll go

36:04

into that pause. Okay. So do

36:06

you think it's an issue of sort of

36:09

waiting these cues then that in the North,

36:11

they wait day length most and

36:13

maybe temperature little to none. And then as they move

36:15

South, they come to balance these things out a bit

36:18

more. Yeah. I think in the

36:20

North, if again, speaking

36:22

anthropomorphically, we'd love

36:24

to do that. That's fine. Yeah.

36:27

If the Beatles, if they get it wrong

36:29

and they aren't in diapause when that first

36:31

hard freeze comes, like in two days, we

36:33

are going to have temperatures of 10 Celsius.

36:37

It's going to be really cold overnight

36:39

and it's been balmy and lovely. And

36:42

so, but if, if a beetle isn't

36:44

in diapause already, it's dead. The

36:47

consequences of not going into diapause

36:49

in accordance to day length are dire. Whereas

36:52

in the South, the consequence of

36:55

not going into diapause, according

36:57

to just a day length are

36:59

much milder. The freezes

37:02

are much milder. They might be, some

37:04

might be able to make it through

37:06

and they come much later. And the

37:08

benefits of not going

37:10

into diapause are much greater because they

37:12

can keep reproducing and they can keep

37:14

feeding and that family line that keeps

37:16

reproducing and feeding is growing relative

37:19

to the other family lines that have

37:21

gone into diapause and wake up next

37:23

spring with fewer offspring. Yeah.

37:25

Interesting. So Ruth, I

37:27

wanted to circle back though. You

37:30

said something that really kind of caught

37:32

my ear, which was that plasticities role

37:35

as a, as a sort of a

37:37

consequence versus, um,

37:40

what was the, yeah, is it an, is

37:42

it a trait itself or is it an

37:44

outcome? Well, so

37:46

I'm, I'm curious and maybe, I don't know

37:48

if you have data on this, but like

37:50

within the Colorado populations, how

37:53

much genetic variation you see for the

37:55

plasticity. So do you see evidence for

37:58

a genotype by environment interactions? across

38:00

the different family lines because

38:03

presumably that is the variation

38:07

that then selection acts on among

38:09

these these different lines and I

38:12

could imagine Maybe in this case

38:15

that they either Might

38:17

all have similar slopes similar

38:20

plasticity or there might be you

38:22

know variation in their sensitivity To

38:25

the cues. Yeah, that's a good question. What

38:27

we have is as you pointed out before that And

38:33

now I'm talking about this other layer

38:35

of plasticity, you know, like temperature modulating

38:38

that so just going into Diapause

38:40

by day length. Um,

38:42

there's genetic variation over that plasticity, but

38:45

I do we don't have data

38:47

on The

38:49

role of temperature in that along

38:51

these lines that I'm trying to figure out a way to

38:53

transition You wrote

38:56

about this word evolutionary potential Is

38:58

it is it straightforward to measure that I

39:00

mean if you if you think that evolutionary

39:03

potential is a meaningful concept You've got your

39:05

next graduate student says hey, I agree and

39:07

so what are you going to decide to

39:09

go out to a Controlled

39:11

or an intended to be controlled population and measure

39:14

This is one of those places where it can be

39:18

fascinating intellectually to quantify

39:22

something like heritability or evolvability

39:24

as a form of evolutionary

39:26

potential and That

39:29

on the practical side, we don't actually have

39:31

to do that you

39:34

just have to start with Enough

39:37

individuals of a population

39:39

you have reason to think has

39:41

some genetic variation as enough genetic

39:43

variation and and The

39:46

evolutionary potential will be there It

39:49

won't necessarily so like the the Beatles

39:52

on Tamarisk that have spread from

39:54

Colorado down South through

39:56

all the way to Mexico. They

39:59

were initially released least in Arizona

40:01

and didn't survive. So they

40:05

did not have the evolutionary potential

40:07

to adapt to that environment at

40:09

that time, but doing it more slowly from north

40:11

to south, they were able to. So

40:14

that's not to say that genetics immediately

40:16

solves it all, having a

40:18

large profigial of diverse individuals immediately

40:21

solves it, but it's a start. So

40:23

Ruth, we've been talking a lot about

40:25

genetic variation and the

40:27

potential for populations to evolve.

40:30

And there's this concept of

40:33

genetic load, especially when

40:35

we start to think about the ability

40:38

of a population to adapt to

40:40

a new environment. And I know

40:42

you've thought about that a

40:44

fair amount. Can you talk about

40:46

what genetic load is and why

40:48

it's an important characteristic of populations

40:52

to quantify? Yeah,

40:54

I would say if you're thinking about

40:57

phenotypes, genetic load is the difference in

40:59

kind of fitness or performance otherwise of

41:03

an actual individual relative to

41:05

some ideal individual genotype that

41:07

doesn't have all of the

41:09

deleterious mutations that that actual

41:12

individual does. So

41:14

we all have lots of

41:16

deleterious mutations in us, right? For

41:19

humans, it's something like we have

41:21

somewhere between two and six lethal

41:23

deleterious mutations per individual. And

41:25

these are recessive, thankfully. But

41:27

if they were then as

41:29

diploids, they

41:32

were in a homozygous state instead of

41:34

being recessive and heterozygous, then we'd be

41:36

dead. So genetic

41:39

load is taking to

41:41

account the fitness effects of all of

41:43

those various deleterious mutations that are

41:45

in an organism's genome

41:48

and looking at what is that

41:50

fitness effect, that reduction

41:52

in fitness relative to if you didn't

41:54

have those deleterious alleles.

41:57

And so then how do you estimate that

41:59

option? that you compare to? Yeah,

42:04

I know. Yeah, so I don't have any

42:06

way of creating that magical optimum. If I

42:08

did, you know, humans would stop aging. All

42:11

sorts of things would happen if we could

42:13

do that. So what

42:15

I've done experimentally is if

42:18

you take, for example,

42:20

two populations that have been

42:22

evolving independently and outcross

42:25

them to each other, then

42:28

all of the deleterious

42:30

mutations that have evolved

42:32

through drift, through inbreeding,

42:34

to become homozygous and

42:37

therefore expressed and therefore

42:39

reducing fitness, when you're

42:41

outcrossing them, most of those are going to be masked. You

42:44

know, it's basically kind of hybrid vigor. You

42:46

know, they're going to be paired up

42:48

with an alternative allele and so you're not

42:50

going to have that recessive mutation expressed.

42:52

And so you can use that fitness,

42:54

the fitness of those individuals, to say the

42:56

difference in fitness between the outcrossed individuals

42:59

and the other populations to say, wow,

43:01

this population is suffering from a lot

43:03

of genetic load. I

43:14

want to talk about the sort of

43:16

practical ramifications of this kind of work

43:18

because what I find really neat about

43:20

your research is that it's valuable in

43:23

the basic biological sense, but it also

43:25

has application to biocontrol. First,

43:27

though, can you say something about

43:30

one of the things that we don't think as

43:32

much about as maybe we should, and especially with

43:34

regard to biocontrol we need to think a lot

43:36

about? What's the combination of

43:38

factors at these vanguards or in these

43:40

range expansions? I mean, sometimes it seems

43:42

to be the case that you can get this

43:44

mix of phenomena that

43:47

leads populations to really just run like wildfire.

43:49

I mean, you get, you know, these pioneers

43:52

maybe out on the edge and then you're

43:54

getting some kind of metapopulation structure or, you

43:56

know, the feeding end of this genetic variation

43:58

allows you to do that. for add mixture

44:01

and eventually you get this just sort of

44:03

big rapid like even more rapid than the

44:05

original colonization that things just really speed up

44:07

so what are the kind of conditions that

44:10

that most concerns you in the sense

44:12

of bio control? So bio

44:15

control lots of things

44:17

come under that umbrella of controlling

44:19

a pest whether it's a weed

44:21

or an insect pest or something

44:23

else an arthropod

44:25

with another living

44:28

organism so I'm mostly working on

44:31

insects that are controlled by insects or

44:33

weeds that are controlled by insects but

44:35

there can also be plant pathogens that control

44:37

weeds for example so a

44:40

major subset of biological control

44:43

is having invasive organisms that

44:46

then people bring natural

44:49

enemies so these predators

44:51

or herbivores or parasitoids

44:53

parasites from the

44:55

native range of that invasive organism

44:57

and release them and so that's

44:59

the context that you're talking about

45:01

I think so then that biological

45:03

control agent itself is invading a

45:05

new environment and so yeah it's

45:08

fascinating they're amazing systems to study

45:10

because otherwise it's like you don't

45:12

get to introduce something into an

45:14

entirely new continent right that's pretty

45:16

unethical I'd like to say

45:18

that with modern biological

45:20

control today these things are

45:22

studied really carefully and they

45:25

have especially for weeds

45:27

they have very narrow host

45:30

ranges that surprisingly to

45:32

me seem not to evolve the worry

45:34

would be that you would release something

45:37

into a new environment to

45:39

control one thing and then

45:41

it explodes and starts feeding

45:44

on other things or doing damage in other

45:46

areas so that would be the worry and

45:49

it really doesn't

45:51

happen which is kind

45:53

of blows my mind as an evolutionary biologist

45:55

because I feel like everything can evolve and

45:57

evolution can be rapid yet Even

46:00

when, so one of, some of the things that

46:02

I've studied to know if that kind

46:04

of thing is happening is

46:07

when two different biological

46:10

closely related herbivores

46:12

are introduced to control a weed and

46:14

they hybridize, there's going to be

46:17

an explosion of genetic variation in

46:19

that population. So my thought is, oh

46:21

my gosh, this is a perfect situation for them

46:23

to start eating other plants, non-target

46:26

plants. And in

46:28

two cases, I've studied this

46:30

in detail in two completely

46:32

different biological control systems and

46:35

their host range just doesn't change. It

46:37

just doesn't change. Some call

46:40

these highly specialized herbivorous insects just

46:42

a kind of evolutionary dead ends.

46:44

In terms of what the cues they

46:46

use to find a host, their

46:49

ability to detoxify the compounds that

46:51

the host has for the fences

46:54

are all, it's such a complex system

46:57

that it just seems to not shift

46:59

easily. I mean, clearly it does

47:01

over millions of years, but it's not

47:03

something that is rapidly evolving

47:06

in these specialized insects. You

47:08

can see rapid evolution of

47:10

host use in generalist insects,

47:13

in more generalist insects. There's

47:15

great examples of rapid evolution of

47:18

host use in generalist insects, but

47:20

these specialized insects just seem like

47:22

they just can't change. The

47:24

other thing, I personally have never released

47:27

a biological control agent. As

47:30

an ecologist and evolutionary biologist, that seems like,

47:32

wow, that's a big

47:34

responsibility. I very much

47:37

respect my colleagues who are

47:39

doing the research on host

47:41

range and the

47:43

folks in the USDA and Fish

47:45

and Wildlife Service and tribal

47:49

nations who evaluate all

47:51

those data and say, yes, we think

47:53

that this is safe enough to release

47:55

the benefits that it could have far

47:57

outweigh the costs of not doing so.

48:00

something or trying to use only chemical controls

48:02

or whatever. Biological

48:05

control nonetheless, it has a bad

48:07

reputation. People say things like, but

48:09

what about the cane toad? At

48:11

the time, the cane toads were

48:13

released supposedly to feed on insects

48:15

that were pests in sugar cane.

48:17

There were also people, Europeans coming

48:20

to North America and doing things like,

48:22

let's introduce all of

48:24

the birds that Shakespeare ever mentioned. Heard

48:27

something about that. Yeah. Yeah,

48:30

people were doing some wacky

48:32

stuff and some

48:34

people said that, oh, this is

48:36

like this ferret is going

48:39

to control some rat or something. But

48:41

people were moving vertebrates around

48:43

in crazy ways and that is

48:46

not biological control. So I

48:48

just want to say what

48:50

was happening then is not biocontrol. Yeah,

48:54

I can recall there was a, I

48:57

guess kind of dated now, but there was

48:59

a paper by Dan Simberloff several years ago

49:01

where he took a very dim view of

49:03

biological control. And my take

49:05

on that paper was that most

49:07

cases of biological control resulted in

49:10

lots of collateral

49:12

damage that was

49:14

not intended. Do you feel

49:16

like if you, when you look back

49:19

on that paper that it did

49:21

a fair job of characterizing like what

49:23

currently is going on in terms of

49:26

the more careful study and standards

49:28

that maybe have changed relative

49:30

to like, you know, if you

49:32

go back and you review the whole

49:35

literature, maybe you're including original

49:37

cases that, you know, had

49:39

a much lower threshold or

49:41

standard for what they would

49:43

be willing to release as a biocontrol. Yeah,

49:46

it's been a while since I've read that paper.

49:48

I would say that two things. One,

49:51

biological control of insect

49:53

pests has

49:56

been much less rigorously

49:58

controlled. and

50:00

overseen and regulated than biological control

50:03

of weeds. And that

50:05

is, it's a cultural thing in

50:07

the US and in many other places

50:10

where basically insects are seen as pests,

50:12

like who wants insects around? I do,

50:15

but 50 years ago, people like, you know, you

50:17

had something in your house, you crushed it. You

50:19

didn't put it out, gently put it outside. So

50:22

there's that. And so things

50:24

were released that, okay, yeah, this generalist

50:27

predator is gonna feed on other aphids as well,

50:29

and not just these pest aphids, but who cares,

50:31

they're all aphids. We wanna get rid of all

50:34

of them. There was a kind of

50:36

cultural agreement that

50:38

those things were pests, whether

50:40

they were in our, you know,

50:43

cropping systems or not. And then also,

50:45

because insects are little, arthropods are little

50:47

packets of protein, whereas plants

50:49

are packets of

50:51

complex chemicals defending themselves. The

50:56

packets of protein, all sorts of things

50:58

can attack them. There are

51:00

some very specific predators and

51:02

parasites called parasitoids

51:05

of insects, but it's hard to find them. It's

51:07

harder to find them than it is to find

51:09

a more generalist one. And so

51:11

I think a lot of what Simberloff was talking about

51:13

is biological control of insects.

51:17

And I think some of those

51:19

critiques, especially from like you were

51:22

saying, Cameron, that longer term perspective

51:24

where people felt like, you

51:27

know, insects are bad, let's get rid of them, that

51:30

there's valid critique there. I

51:33

do think that there's a

51:35

tendency to not

51:37

consider the other alternatives.

51:40

There are pros and cons everywhere. You know,

51:42

if we don't control a pest, what

51:44

happens? That's a valid approach to decide that

51:47

this thing is not gonna be controlled. And

51:49

so we're not gonna have this tree

51:52

or this crop grown in this

51:54

area. And that's a choice because

51:56

this pest is gonna demolish

51:58

those trees or make that crop. untenable

52:00

to produce. Or we use

52:03

insecticides to control these things. And there

52:05

are pros and cons there as well.

52:07

Insecticides can be incredibly powerful tools. It

52:09

can cause cancer in humans. And they're

52:11

very difficult to keep in the place

52:13

we want them to be. And they

52:16

get into the water systems. And there's

52:18

all sorts of problems there. But it's

52:20

also a valid choice. Or there's biological

52:22

control. And if that

52:24

can be done safely with

52:26

something that's host specific, then

52:29

it's often a good

52:31

choice. But it's one of

52:33

those three main classes. And I

52:35

think that completely discounting it, the

52:37

way Simberloff does, is

52:40

short-sighted. Yeah. That's

52:42

an interesting point. It makes me also

52:44

wonder about the ecology of

52:49

a lot of these invasive species

52:51

in their introduced range relative to

52:53

their ancestral ranges that

52:56

they were originally native to. And

52:58

just how challenging of a

53:01

question it becomes to also

53:03

figure out, well, what does

53:05

regulate the populations

53:07

in the native range? Even

53:10

for a lot of things, we

53:13

struggle to figure that out. But

53:16

I totally agree with your

53:18

point that these are really big

53:20

options that we have to weigh

53:22

the pros and cons of, and

53:24

big decisions. And it's not

53:27

black and white. There's a lot of gray area

53:29

there, too. So it's good to think about those

53:31

things. Yeah. That's an interesting

53:33

take on the Simberloff paper. I never heard it

53:35

framed that way, the decisions that

53:37

remain historically. And therefore, the way that

53:39

he would write that paper might not

53:42

resonate with what we've learned in the

53:44

time sense and the way that we

53:46

approach biological control now. OK.

53:48

So not we, because I

53:50

don't do it. But when you come up

53:53

with a plan for biological control, presumably

53:56

one of the stages is going to be, you

53:58

know, you're collecting the organisms from. or wherever they

54:00

are in the world, then you're breeding

54:02

them to make enough of them to do

54:04

the introduction. That has to mean

54:07

some period of time when they're

54:09

in completely foreign conditions, right? Before

54:11

they go from native to new,

54:13

right? There's a lab, there's

54:15

a breeding effect. How do you

54:18

account for that? And like, couldn't genetic

54:20

load accumulate in those kinds of contexts?

54:22

I mean, what's the current standard and

54:24

what would be the best practice if

54:26

we could afford it? Yeah, that

54:29

is an outstanding question. Yes, there's

54:31

definitely a reduction in genetic

54:33

variation and potentially build up

54:35

in genetic load as natural

54:38

enemies pass through quarantine. So

54:41

what happens is, so let's go to

54:43

weed biocontrol, because that's what I know

54:45

best. Organisms will be

54:47

found, herbivores will be found in the native

54:50

range. They'll be brought

54:52

to a common garden, also typically in

54:54

the native range, sometimes in a quarantine

54:56

facility, where they're tested against

54:58

a whole range of different plants to see

55:00

what is their host range. Say

55:03

everything looks great and they

55:05

are petitioned to be released.

55:08

And that group that I mentioned

55:10

before, like USDA, wildlife, tribal nations,

55:12

approves it. Then

55:15

you go back to the

55:17

native range and you collect individuals, pass

55:20

them through one generation in

55:22

quarantine to try to

55:24

remove any possible parasites or pathogens they

55:27

might have, and

55:29

then release them. So you try

55:31

to have that part be very

55:33

short. But sometimes

55:35

that ends up being several generations to

55:37

do what you were just saying, Marty, of

55:39

building up the population size. Yeah,

55:42

gotta grow enough of them. You gotta

55:44

grow enough of them, right? So after

55:46

you've done the validation that they're

55:49

only gonna focus on the weeds that you wanna

55:51

control or something close to that, then you bring

55:53

them in to clean them with parasites. Do you

55:55

bring in individuals from different parts

55:57

of the native range? I think... of

56:00

one of your papers you emphasized that that would

56:02

be pretty cool if that could be done and

56:04

it makes sense for the reasons we've been talking

56:06

about. But is it common practice? It

56:08

is not common practice. At this point,

56:10

all of the host range testing has

56:12

to happen for each population. And because

56:14

that can be like 10 years of

56:17

work of a scientist's time and their

56:19

whole team, it

56:21

often doesn't happen. It used

56:23

to happen, but we are

56:25

justifiably concerned with what if

56:27

these populations differ in their

56:30

host range. And that's

56:32

why I did those experiments to say,

56:34

oh, once upon a time people introduced these

56:36

different things and now they're out crossing with

56:39

each other. Is that changing their

56:41

host range? And so far I've found like, no,

56:43

it's not. Their host range is not changing. So

56:46

one kind of related question that I was

56:48

going to ask Ruth is sort of the

56:51

interaction between the underlying

56:53

genetic variation in the population,

56:56

population size, and then the

56:59

kind of demography. So like from

57:02

the evolutionary side, the capacity

57:04

for populations to grow seems

57:07

to be associated with also

57:09

the opportunity to adapt and

57:12

evolve. And so

57:14

a lot of these cases of

57:16

rapid evolution often occur when populations

57:18

are released from like any kind

57:20

of density dependent effect. And

57:22

so they can take off very

57:24

quickly. But then from

57:27

the sort of more ecology side,

57:29

these small populations also can potentially

57:31

be limited by what we call

57:33

like LE effects, where the

57:36

capacity for growth is limited to, you

57:38

just have like a small number of individuals,

57:40

you know, maybe hard for them to find

57:42

each other to mate with. And

57:44

so in like either the natural

57:47

systems that you work on or in

57:49

the experimental systems, do you

57:51

ever see that sort of

57:53

ecological LE effect,

57:55

for example, and the genetic variation

57:57

side with the small populations? kind

58:00

of in conflict with each other or where

58:03

like one constrains the other or does

58:06

it even play out in in any

58:08

of these studies in any meaningful way?

58:11

I think it can for sure.

58:13

So one thing with the tribolium

58:15

system since they're

58:17

obligately sexual reproducers

58:21

with small populations with very

58:23

small numbers of individuals you

58:25

can easily have all

58:28

males or all females and

58:30

no reproduction whatsoever.

58:33

So even within an experimental population when

58:35

they're in little experimental cages and finding

58:37

each other is not a problem which

58:39

is often a lea sex in nature

58:41

often they don't find each other like

58:43

you mentioned. Just the sex

58:46

ratio variation can lead to

58:49

some propagules some groups of in

58:51

founders not being able to establish

58:53

a population. Interestingly I

58:55

did in one experiment

58:58

see an interaction between

59:00

that and the genetic

59:02

diversity of the population.

59:04

So with founder size

59:07

and genetic diversity interacting

59:09

such that low genetic diversity

59:11

and low founder size was

59:13

extremely bad. You know so

59:15

maybe the sex ratio wasn't

59:17

quite all females

59:20

or all males it was you

59:22

know one female and

59:24

then some males and diverse

59:27

populations were able to establish in those

59:30

situations and ones

59:33

experiencing high genetic load the

59:35

low diversity populations were

59:37

not. What do you know about assorted

59:39

mating in the beetles are they are

59:41

they choosy or as long as the

59:43

male or female is there they'll take

59:45

a chance. They mate multiply

59:47

so they're not super choosy

59:49

and they can mate multiply

59:51

very rapidly as well but

59:53

they they are choosy so I haven't

59:55

done this research but others have and given

1:00:00

individuals and unrelated individuals they will

1:00:02

mate with unrelated individuals. So there's

1:00:04

something, some kind of relative

1:00:06

avoidance in breeding avoidance and

1:00:09

I don't know that literature super well. Gotcha.

1:00:11

Okay, it's not one of those challenging

1:00:13

dimensions. It's not just finding a mate,

1:00:15

it's how picky you are what you

1:00:17

found there. Yeah. So Ruth, I

1:00:19

have one last question that I'm curious to get

1:00:22

your opinion on. Obviously when

1:00:24

we look around, you know,

1:00:26

the globe at one of

1:00:29

the main threats to biodiversity

1:00:31

is the movement either

1:00:33

naturally or by humans

1:00:36

of invasive species and this has

1:00:38

consequences obviously for both native

1:00:40

populations in biodiversity but also like

1:00:43

for agriculture and things like that.

1:00:45

Given sort of what you've learned about

1:00:48

these experimental systems and the

1:00:50

kind of combinations of the right cocktail that

1:00:52

you need in order for something to really

1:00:54

take off, do

1:00:56

you get the impression that like there's lots

1:00:59

of organisms that are

1:01:01

being spread all the time but

1:01:04

most of the time they just don't take

1:01:06

off because of things like by chance it just

1:01:08

happens to be all males or

1:01:10

some demographic stochastic event happens and

1:01:12

just you know the population gets

1:01:15

knocked out. Is that

1:01:17

really common? But we only see this

1:01:19

non-random subset of cases where the populations

1:01:21

do end up taking off and they're

1:01:23

detected and then you know we recognize

1:01:26

that there's a problem. But below that

1:01:28

you know there's all these like natural

1:01:30

experiments potentially that are happening all the

1:01:32

time. Is that a fair

1:01:35

assessment of what might be going on or

1:01:37

is it is it really just you know

1:01:39

most cases the invaders

1:01:41

are successful? There's a

1:01:45

the idea that one

1:01:47

in every ten introductions

1:01:49

establishes and one in every

1:01:51

ten of those is able

1:01:53

to start to spread and

1:01:55

one of it in every ten of those is

1:01:58

able to start to invade.

1:02:00

And at the very beginning of that,

1:02:02

you know, maybe one in every 10

1:02:04

of those survives transport. So there's

1:02:07

not a lot of data behind that

1:02:09

idea. But I think it's pretty well

1:02:11

accepted that it's

1:02:13

unlikely that everything is able to make it.

1:02:15

And there's so many

1:02:18

introductions happening all of the time that there must

1:02:20

be lots of failed ones. What that

1:02:22

proportion is, though, I don't know, it's one

1:02:24

in 10 at each of

1:02:26

those stages. Well, Ruth, this has

1:02:29

been fantastic. We always give

1:02:31

guests the chance to mention anything that

1:02:33

that you want to say some topic

1:02:35

some research recent discovery or something. What

1:02:37

did we not give you the opportunity

1:02:39

to to talk about? Oh,

1:02:42

this has been so much fun. It's a little bit

1:02:44

like an oral exam. Sorry

1:02:48

about that. No,

1:02:51

no, no, but mostly like a

1:02:53

conversation among colleagues that is just

1:02:56

a hoot. Yeah, so I've really

1:02:58

appreciated it. And it amazes me that

1:03:00

you all have read any of anything

1:03:02

that I've written at all and that

1:03:05

think any of these issues are important. One

1:03:08

thing that we didn't talk about a

1:03:10

lot is the the flip side of

1:03:12

range expansion range limits, and

1:03:15

more about kind of the expansion dynamics

1:03:17

and genetic load at the expansion front.

1:03:19

But I think there are lots of

1:03:21

other folks working on that you can

1:03:23

interview some of them. Okay.

1:03:28

Good. Well, thank you so much for

1:03:30

taking time out of your schedule to

1:03:32

talk to us. We really appreciate it.

1:03:34

Yeah, thanks. I really appreciate it

1:03:37

too. Thank you so much. This has been

1:03:39

such a pleasure. Thanks

1:03:51

for listening. If you like what you hear, let us know

1:03:53

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1:03:55

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1:03:57

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1:04:01

Thanks to Steve Lane who manages the

1:04:03

website and Molly McGitt for producing the

1:04:05

episode. Thanks also to Dana DeLaCruz for

1:04:07

her amazing social media work and Katie

1:04:09

Shimmeria who produces the fantastic cover art.

1:04:12

Thanks to the College of Public Health at the University

1:04:14

of South Florida and the National Science Foundation for support.

1:04:18

Music on the episode is from Pottington, Barron, Tierra and

1:04:20

Costello.