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Speaker 1: This is me eater podcast coming at you shirtless, severely

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Speaker 1: bog bitten in my case, underwear listening podcast. You can't

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Speaker 1: predict anything presented by on X. Hunt creators are the

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Speaker 1: most comprehensive digital mapping system for hunters. Download the Hunt

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Speaker 1: app from the iTunes or Google play store. Nor where

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Speaker 1: you stand with on X. All right, folks, before I

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Speaker 1: even introduce what we're um, this question is going to reveal.

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Speaker 1: This question will reveal what we're talking about. But before

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Speaker 1: I introduced who we're talking about it with, I just

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Speaker 1: gotta get one of these. I gotta get something out

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Speaker 1: of the way. Um, Dr Bob Reid, Is it true

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Speaker 1: that a Burmes that day? Is this really? They found

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Speaker 1: a Burmese python on that had the remains of three

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Speaker 1: different deer and its lower GI tract that was That's

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Speaker 1: probably the publication I'm proudest of because I got an

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Speaker 1: entire peer reviewed publication out of a single poop. And yeah,

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Speaker 1: so this was a This was a python that was

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Speaker 1: picked up. It was about a forty eight kilo python,

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Speaker 1: so a little over a hundred pounds, and it had

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Speaker 1: a fourteen pound poop inside it. And in that poop

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Speaker 1: were the hoofs of three different deer. Um, and my

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Speaker 1: buddy Scott bo back we we uh, he had his

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Speaker 1: buddies collect deer legs and he made a graph of

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Speaker 1: hoof size of the deer that his friends were shooting

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Speaker 1: and correlated with the cuff size to the tier that

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Speaker 1: we're in the poop. So we figured out how big

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Speaker 1: they were and how many had eaten and uh yeah, um,

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Speaker 1: one dough and two fons all in one python poop

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Speaker 1: from the everglades. Do they feel that that one python

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Speaker 1: had been carrying those like to the hooves last a

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Speaker 1: long time? Like maybe it'd be like if you opened

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Speaker 1: up an alligator and found a bunch of old dog

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Speaker 1: collars because they just never moved through the tract. Yeah,

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Speaker 1: so is that it's like is that a life's collection

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Speaker 1: of deer or is that last week's dear it so

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Speaker 1: carroton doesn't get digested and we you know we pass

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Speaker 1: hair too, um, so hair and hooves get past. But

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Speaker 1: it looks like this snake was actually impacted. That it

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Speaker 1: had eaten a dough that was of its own body mass,

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Speaker 1: followed by two fonds that we estimate with thirty of

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Speaker 1: its body mass and Basically it just got plugged with hair,

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Speaker 1: and so we think this thing was probably gonna die.

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Speaker 1: But based on the fawning period in Florida and when

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Speaker 1: the snake was found, we think that have been in

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Speaker 1: there for a maximum of about six months. So that's

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Speaker 1: about maybe six months worth of eating deer, including during

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Speaker 1: the fawning period. Alright, with that cover, because I had

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Speaker 1: to get that out of the way, tell everyone to

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Speaker 1: tell tell everyone what you what you do. We've had

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Speaker 1: other fellers from the We've had other fellers from the

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Speaker 1: U s G S. On. I think you're our third

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Speaker 1: U s GS guest. Alright, was that right? Honest? Yeah,

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Speaker 1: I was gonna say, at least Brant Mixel U s

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Speaker 1: G S does research on waterfowl. Yeah, Bran, Brant took

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Speaker 1: me salmon fishing last summer. Okay, so you guys run

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Speaker 1: in a pack, and I feel I feel like we

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Speaker 1: had another U s G S guy on. We've had

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Speaker 1: two more the no Steve's he's Wildlife Services. Who you're

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Speaker 1: thinking of is our c w D expert Brian Richards,

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Speaker 1: he's USGS. So go ahead, Bob, all right, Um, while

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Speaker 1: I'm with US Geological Survey based in Fort Collins. I'm

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Speaker 1: the chief of the Invasive Species science branch. We've got

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Speaker 1: a bunch of researchers who work on everything from invasive

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Speaker 1: vertebrates to invasive plants. But my history and expertise is

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Speaker 1: in snake biology, and I've done a lot of work

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Speaker 1: and overseen a lot of work on Burmese pythons in

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Speaker 1: Florida and the brown tree snake on Guam. That's actually

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Speaker 1: where the majority of our staff are is out on Guam,

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Speaker 1: and then we're we dabble in other invasives. Were working

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Speaker 1: on big old tegue lizards that are in southern Florida

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Speaker 1: as well, and invasive water snakes from the Eastern US

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Speaker 1: that are introduced into the Western US. But a big

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Speaker 1: part of our work has focused on invasive pythons in

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Speaker 1: the Everglades for the last decade. Can you can you

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Speaker 1: tell people about the limits of what you're allowed to

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Speaker 1: talk about? Um? Sure? So, like, which I guess encompasses

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Speaker 1: you know what your mandate, what your professional mandate is.

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Speaker 1: I don't want to put it in terms of a negative,

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Speaker 1: but we could sell it as a positive, like what

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Speaker 1: is your mandate as a researcher? So the U s

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Speaker 1: Geological Survey is the research arm of the Department of

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Speaker 1: the Interior. So we do the science, and we stick

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Speaker 1: to the science. And then it's the job of agencies

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Speaker 1: like the U S. Fish and Wildlife Service to take

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Speaker 1: the science and turn it into regulation um and policy.

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Speaker 1: And so we try to keep those two shops really

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Speaker 1: separate so that the policymakers aren't unduly influencing the researchers

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Speaker 1: and vice versa. And so I can talk about anything

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Speaker 1: in regards to biology or research results, but I can't say,

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Speaker 1: for example, that the State of Florida should engage in

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Speaker 1: some particular policy because that's not related to the science.

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Speaker 1: Would you be able to say something like Janice should

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Speaker 1: cut that mohawk off. You know, the headphones help with it,

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Speaker 1: the help help keep it down. Otherwise it would look

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Speaker 1: like some punk rocker from London And like seventy two.

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Speaker 1: The log cabin kind of throws it off too, that

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Speaker 1: he's sitting in a little log cabin with it just

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Speaker 1: makes I'll just get real mixed signals from that haircut.

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Speaker 1: I can't stop talking about it. I'm all mixed up.

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Speaker 1: Oh you know what, I just have another USGS guy,

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Speaker 1: Yanni do you remember the Grizzly bear, the guy that

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Speaker 1: did the population modeling for Yellowstone Grizzlies. He was USGS. Oh,

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Speaker 1: there was the lead of the inter agency team, Frank. Frank,

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Speaker 1: help me out, Bob, you know what I'm talking about.

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Speaker 1: You're you're putting me on the spot. Now I'm blanking.

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Speaker 1: I work with chat Dickinson, um who does grizzly work,

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Speaker 1: but he's also the U s g S Firearms program manager. Uh. Yeah,

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Speaker 1: that was a great show. People to want to go

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Speaker 1: want to want to learn a lot about bears, should

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Speaker 1: go back and find that episode. Um, all right, so

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Speaker 1: let's keep let's keep plugging along. Here's here's my here's

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Speaker 1: my next Burmese python question. And now for people listening.

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Speaker 1: When you're scrolling through social media and all of a

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Speaker 1: sudden you find a picture like eight dudes staying in

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Speaker 1: the road holding a giant snake, you're probably looking at

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Speaker 1: a picture from Florida from the Everglades of Burmese pythons.

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Speaker 1: It's like it's just the same. The media like certain

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Speaker 1: stories about it, where you'll see on social media a

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Speaker 1: Burmese python gagging on something giant that is trying to

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Speaker 1: eat that's a popular one about someone catching one that

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Speaker 1: was bigger, the biggest so far biggest, this biggest thing

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Speaker 1: is a popular story, and people staying in the role

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Speaker 1: to holding up a big one is a popular story.

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Speaker 1: And so people, I think, have this this awareness of

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Speaker 1: how these giant snakes are colonizing, taking over impacting a

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Speaker 1: large swath of Florida. But we're gonna dive in here

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Speaker 1: to sort of what's really going on. How did it

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Speaker 1: come to be, how bad is it? Is there an

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Speaker 1: end in sight? Is this normal now? Um? And and

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Speaker 1: get into some of that. But my first question laying

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Speaker 1: this out, and this is I'm always puzzled by this.

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Speaker 1: How do we not know exactly where they came from

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Speaker 1: and how they got cut loose? If you can look

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Speaker 1: at the genetics, can't you trace it to a population

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Speaker 1: bottleneck of one or two snakes? Or is it more

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Speaker 1: complicated than that? Um, it's it's a little more complicated

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Speaker 1: than that, but maybe not that much. So one of

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Speaker 1: the problems is that there hasn't been any good range

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Speaker 1: wide genetic analysis from the Native range, so we can

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Speaker 1: say that you tell us about the Native ranges. The

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Speaker 1: Native range is a big swath of Asia from UM

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Speaker 1: Indonesia up to southern China and then all the way

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Speaker 1: over through northern India, um barely into Pakistan. So it's

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Speaker 1: a really wide ranging species, lots of different habitats, and

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Speaker 1: no one's really gone through to sample from that whole

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Speaker 1: range to figure out UM where the Florida pythons specifically

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Speaker 1: are from, although we can say that they're almost certainly

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Speaker 1: from Southeast Asia based on the site's import records explain

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Speaker 1: that UM well so UM all boas and pythons are

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Speaker 1: on the sights to list, which means that countries that

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Speaker 1: are trading them have to report the numbers. And that's

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Speaker 1: because python skins are such a big commodity. And then

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Speaker 1: they extend that to UM live animals as well, and

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Speaker 1: we imported tens of thousands of pythons from Southeast Asia, UH,

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Speaker 1: mostly during the eighties and early nineties dead or live,

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Speaker 1: live live. We brought in UM I think a hundred

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Speaker 1: and fifty thousand between what was it nineteen eighty and

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Speaker 1: about nineteen No, about two thousand five for what for

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Speaker 1: the pet trade, So hundred and fifty thousand, yeah, yeah,

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Speaker 1: these wereld So that's where it just gets more complicated.

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Speaker 1: These were one of the most popular snakes during that

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Speaker 1: time period, and it's partially because they are cheap as

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Speaker 1: hell and they're impressive. You know, it's a really it's

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Speaker 1: a gorgeous snake. And I've got to admit that when

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Speaker 1: I was a sophomore in college, I bought a hashling

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Speaker 1: Burn's Python and so you're part of the You're part

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Speaker 1: of the problem absolutely. I mean I was the last

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Speaker 1: person you want buying a Python that's going to get

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Speaker 1: that big, because I was not doing it for good reasons.

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Speaker 1: So yeah, I like snakes a lot, but um, I

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Speaker 1: was doing it because it was going to be impressive

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Speaker 1: and it would probably get girls to my dorm room.

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Speaker 1: And yeah, but I mean, like of what caliber? I mean, well,

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Speaker 1: I mean this was Berkeley, so you know there it's

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Speaker 1: pretty uniformly high. I'm not saying it. I'm not saying

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Speaker 1: it worked. But you had a theory that if you

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Speaker 1: could say, would you like to come up and see Um,

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Speaker 1: I don't even want to say it. Yeah, at that

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Speaker 1: point I was as weill had to try anything. You're desperate,

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Speaker 1: you got a big party thought. Yeah. So anyway, Um,

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Speaker 1: we know we brought lots of Mover, and we know

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Speaker 1: that there were also lots of importers based in the

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Speaker 1: Miami area, and I gotta, we gotta, we gotta back.

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Speaker 1: I can't leave that hanging them. What did you take

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Speaker 1: yours down and let it go on the Everglades or

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Speaker 1: died of old age, or you sold it like what happened?

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Speaker 1: I had mine all the way through my master's degree

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Speaker 1: at Arizona State, and when I left Arizona State to

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Speaker 1: start my PhD at auburn Um, I gave it to

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Speaker 1: a friend of mine whose garage had just burned down

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Speaker 1: and he lost his whole snake collection. So you're helping

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Speaker 1: that rebuilt. Yeah. By that point she was about fourteen

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Speaker 1: and a half feet, about pounds, and I had to

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Speaker 1: go out in the desert and shoot jack rabbits for

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Speaker 1: her um because she was just eating me out of

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Speaker 1: house and home. Huh okay, So go on. So Florida, Yeah, so,

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Speaker 1: So Southern Florida was an epicenter for both importing and breeding.

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Speaker 1: And there's a lot of controversy about how the snakes

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Speaker 1: became established, and so some people say that it was

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Speaker 1: individual snakes that were released by pet owners in the Everglades,

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Speaker 1: you know, trying to find them a nice home after

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Speaker 1: they got too big for their their cages. And then

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Speaker 1: there's people who say that Hurricane Andrew knocked down a

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Speaker 1: bunch of these importer and breeder facilities and released snakes

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Speaker 1: into the Everglades. That that was reported widely, including in

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Speaker 1: the in the New Yorker. Yep, yep. And I've been

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Speaker 1: looking for evidence of that for a decade and there's

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Speaker 1: so far I found no one who can provide eyewitness

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Speaker 1: accounts of these facilities that got down, that got knocked down,

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Speaker 1: and lots of snakes are known to have escaped. Couldn't

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Speaker 1: have happened, absolutely, But it's interesting because some of the

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Speaker 1: folks who um are advocates for pet owners say, hey,

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Speaker 1: don't blame us, it was Hurricane Andrew knocking down the importers.

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Speaker 1: But I just think it's a really silly dichotomy because

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Speaker 1: we know the reason they were there. They were there

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Speaker 1: because we imported them and bred them, and by one

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Speaker 1: means or another, they got out. So there could have

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Speaker 1: been not could have been, probably was, but potentially dozens

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Speaker 1: of release occurrences. Yeah, it's possible. Um, there's a paper

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Speaker 1: that a couple of friends of mine put out recently

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Speaker 1: showing that there's actually, uh, potentially two different populations that

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Speaker 1: were established, one that started in the southern Everglades, one

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Speaker 1: that started closer to Naples that got slight differences um

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Speaker 1: in d na um. But again they're still probably from

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Speaker 1: Southeast Asia, and we know that we brought them in intentionally.

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Speaker 1: What was the first what year was the first known

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Speaker 1: instance of natural wild reproduction? Two thousands? So um uh.

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Speaker 1: There's a paper out there that models generational times and

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Speaker 1: it suggests that they might have been established in the

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Speaker 1: mid eighties at low numbers in the Everglades, and then

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Speaker 1: if so, then Hurricane Andrew would have just augmented it

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Speaker 1: a little bit. But the first hatchlings were found not

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Speaker 1: until two thousand and even then there were people who

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Speaker 1: were trying to say that, oh, that those are just

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Speaker 1: individual releases, and that was true for most pythons until

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Speaker 1: about two thousand three two four, when they started finding more.

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Speaker 1: Up until that point, it was easier for folks to say, oh,

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Speaker 1: that we found a python, But pythons are from tropical

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Speaker 1: areas and they can't survive in Florida, and so this

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Speaker 1: must be a recent release or escape, Tell me why,

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Speaker 1: and you can go on as long as you want.

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Speaker 1: Who cares about these snakes, Like like, why is it

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Speaker 1: such a big issue that they got caught loose? Well,

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Speaker 1: it's not. It's not like legitimately a human safety issue. No, no,

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Speaker 1: we we've actually reviewed that, and the risks to humans

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Speaker 1: are extremely low. Um. We collected reports of so called

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Speaker 1: python attacks from free ranging pythons over the course of

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Speaker 1: a decade, and we found five instance is where people

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Speaker 1: had seen a python strike at a human. The python

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Speaker 1: only made contact on two of those occasions, only broke

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Speaker 1: the skin on one, didn't try to constrict on any

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Speaker 1: of them. And all of those attacks were on professional

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Speaker 1: biologists who are walking through flooded areas in the Everglades,

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Speaker 1: And generally that's not something we'd want the public to

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Speaker 1: be doing anyway, in a place that's full of gators

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Speaker 1: and cotton mouths. So the chances of some visitor to Everglades,

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Speaker 1: and there's a million of them a year, being attacked

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Speaker 1: and killed by a python is extremely low. It's not

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Speaker 1: to say it couldn't ever happen, but in the scope

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Speaker 1: of potential risk to humans, it's pretty much a non factor.

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Speaker 1: So yeah, I mean in twenty years there's bad. I mean,

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Speaker 1: in twenty years of known wild reproduction, there's been zero

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Speaker 1: human fatalities, no human fata. Is not even a human attack,

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Speaker 1: um that I'd consider serious. Now. You know, during that

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Speaker 1: time period, there have been people killed by captive Burmese pythons,

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Speaker 1: but still not many of those, and those are spread

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Speaker 1: throughout the U. S. And Canada. Yeah. Yeah, And he's

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Speaker 1: got a question for you. He was we need to

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Speaker 1: back up a little bit because he's got a good

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Speaker 1: question for you. All right. UM, My question was if

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Speaker 1: is the pet trade and then the affinity for the

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Speaker 1: snake hides is big in the snake's native range as

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Speaker 1: it is here in the United States. Um, So let's see,

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Speaker 1: the great majority of the trade and snake skins has

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Speaker 1: reticulated pythons, and that trade is in the you know,

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Speaker 1: million skins per year range um globally, and Burmese pythons

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Speaker 1: are in much less demand for the skin trade. Um.

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Speaker 1: But it sort of just I'm gonna loop back to

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Speaker 1: the human attacks things. So the reticulated python versus Burmese

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Speaker 1: python question. Reticulated pythons are actually known to attack humans

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Speaker 1: regularly in the native range, whereas even Burmese pythons in

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Speaker 1: the native range aren't. They're very different animals. There is

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Speaker 1: a study of a tribe in the Philippines and of

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Speaker 1: the adult males reported being attacked by reticulated pythons, they

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Speaker 1: were multiple instances of fatalities, and so that there are

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Speaker 1: sort of personality differences among these giant snake species. Um,

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Speaker 1: we can only find two records of a Burmese python

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Speaker 1: ever even eaten any kind of primate, whereas reticulated they

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Speaker 1: just consider a biped as another suitable prey atom. So

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Speaker 1: go back to your question, let me know if I

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Speaker 1: answered it. Okay, Well, no, the hides, you're talking about

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Speaker 1: the hides. That was that answers the hides. But is

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Speaker 1: there a pet trade as well over there in its

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Speaker 1: native range for those snakes. Well, what's happened is that

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Speaker 1: there there's still a pretty big trade in people who

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Speaker 1: catch pythons opportunistically in the fields, and then these animal

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Speaker 1: traders will come around periodically and buy them from them,

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Speaker 1: and those animals might go into the pet trade, might

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Speaker 1: go into the skin trade, depending on where they can

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Speaker 1: get more money. But they've also found that they can

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Speaker 1: farm pythons for both skins and meat, and they've come

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Speaker 1: up with really intensive production of pythons in the last

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Speaker 1: few years. Um. And they can get them to eat

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Speaker 1: things with some amount of training as juveniles that they

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Speaker 1: wouldn't eat in the wild. So things like you know,

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Speaker 1: chicken necks that are waste products. Um, they can get

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Speaker 1: the pythons to eat those. They're they're making giant sausages

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Speaker 1: and feeding these things too pythons and getting really high production.

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Speaker 1: I mean, that might be another Western hook up, but

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Speaker 1: I'm not sure. Man, that sounds like a horror moving

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Speaker 1: movie location in the making. Right there at the place

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Speaker 1: I don't want to go see is the python factory.

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Speaker 1: Oh yeah, I know, it's just like it Just it's

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Speaker 1: kind of the more you think about, the less advertising

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Speaker 1: it becomes. Man. Yeah. Well, the you know, the traditional

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Speaker 1: way um was definitely um, not great. They would they

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Speaker 1: would take a pretty big snake, stick a hose in

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Speaker 1: his mouth and basically fill it up with water and

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Speaker 1: then stick a rubber band around its head and it

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Speaker 1: would suffocate and the water stretches the skin out and

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Speaker 1: makes it easier to skin afterwards. So it was you know,

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Speaker 1: definitely inhumane, would not pass any kind of animal care

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Speaker 1: laws um around here. But apparently they're now going to

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Speaker 1: much more humane methods of euthanizing animals for the skin trade.

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Speaker 1: So tell me why. Okay, it's not a people thing.

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Speaker 1: Explain what the real problem is. So the real problem

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Speaker 1: is that snakes are phenomenally efficient predators. And one thing

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Speaker 1: that people don't realize is that snakes can exist at

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Speaker 1: very high densities. And we don't realize that because they've

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Speaker 1: got low individual detection probabilities. That means we don't see them.

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Speaker 1: So in your backyard on any given day, you might

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Speaker 1: see the same damn squirrel over and over again. That

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Speaker 1: squirrel is a biological exhibitionist. He's letting you see most

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Speaker 1: aspects of his life. But meanwhile, that's a that's a

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Speaker 1: great term, man. Yeah, I mean look, I mean, come on, um, yeah,

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Speaker 1: he's like here, I am a barking at you. In

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Speaker 1: most part to the US, there are twenty snakes for

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Speaker 1: every score at least, but how many of them do

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Speaker 1: you see? In Kansas? There can be over a thousand

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Speaker 1: ringneck snakes per hector. Really, yeah, and that's one species

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Speaker 1: of snake. And so when you look at the total

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Speaker 1: number of snakes in an ecosystem, they can exhibit massive

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Speaker 1: top down effects on pray species. And in a regular ecosystem,

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Speaker 1: those prey have evolved with those snakes, and so there's

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Speaker 1: a trade off. You know, you don't have those pray

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Speaker 1: species going extinct usually because of snake predation, because they've

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Speaker 1: got behaviors that allow them to escape it. But when

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Speaker 1: you take something like a Burmese python and dump it

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Speaker 1: in the everglades with animals that don't have those kinds

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Speaker 1: of adaptations to a large ambush foraging snake, um you

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Speaker 1: can have really big effects. So UM, I just got

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Speaker 1: some data from Christina and Romagosa. There's a colleague at

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Speaker 1: the University of Florida, and we've been sending her all

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Speaker 1: of the stomach examples from the two thousand one pythons

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Speaker 1: that our staff have dissected, and as of now, we're

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Speaker 1: at seventy one native species that have been identified from

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Speaker 1: python guts. Oh it's it's it's forty five birds, twenty

358
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Speaker 1: four mammals, two reptiles. It's everything from rends to alligators.

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Speaker 1: And do they cannabalize each other? No? Now, the only

360
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Speaker 1: way that a python is gonna need another python is

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Speaker 1: if they start at opposite ends of the same prey

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Speaker 1: item and then basically they keep going. Really that happens, Yeah,

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Speaker 1: it with that spaghetti noodle hold on. This is then

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Speaker 1: known occurrence. It mostly happens in captivity. I mean I've

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Speaker 1: had it happened with with captive snakes that I've had.

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Speaker 1: They got so they got a rabbit and they start

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Speaker 1: eating the rabbit. Then they meet and then one of

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Speaker 1: them just keeps eating and eats the other one too. Yeah. Basically,

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Speaker 1: when a snake starts eating, they keep going. Yeah. Um, discussing,

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Speaker 1: I mean, the the range of the range of species

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Speaker 1: is is pretty phenomenal. I mean, you've got the things

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Speaker 1: you'd expect, like rabbits and raccoons, um, most of the herons,

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Speaker 1: but then they eat surprising numbers of rails. And a

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Speaker 1: rail is another bird that we don't see that often, right,

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Speaker 1: you know, they're really good at hiding, but snakes are

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Speaker 1: able to find them easily. Um. There's some records that

377
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Speaker 1: are just bizarre. They got a frigate bird out of

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Speaker 1: a python that was in the middle of the everglades.

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Speaker 1: Even though frigate birds don't land on the mainland in Florida.

380
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Speaker 1: They only land on the offshore Mangrove Islands. So how

381
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Speaker 1: this snake ended up with a frigate bird in it

382
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Speaker 1: three kilometers from the coast is a mystery. Um. They

383
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Speaker 1: can eat very large meals. So the biggest meal is

384
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Speaker 1: a fawn from a python over near Naples, and the

385
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Speaker 1: fawn was a hundred and of the snake's body mass.

386
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Speaker 1: What so it is successfully ate it? Oh yeah, yeah,

387
00:26:23,440 --> 00:26:26,880
Speaker 1: so that's that's like you know, me eating a two

388
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Speaker 1: pound cheeseburger. It's like you eating Janice yep with with

389
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Speaker 1: no hands and one sitting. Yeah. So they're there, ah there, disgusting,

390
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Speaker 1: phenomenally efficient. You have such a freaking ENDOTHERM bias man, dude,

391
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Speaker 1: real bad, real bad, real bad man. You wouldn't even understand.

392
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Speaker 1: It's like real bad. Can you explain when an ENDOTHERM biases? Please?

393
00:26:57,280 --> 00:27:00,960
Speaker 1: It means Steve's scared of scaling and slimy things. I think, No,

394
00:27:01,160 --> 00:27:08,320
Speaker 1: it's not scared, it's repulsion. I have repulsion about Like

395
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Speaker 1: I'll tell you where it came from real quick. You know,

396
00:27:11,280 --> 00:27:15,439
Speaker 1: in high school and you gotta dissect frogs, yep. I

397
00:27:15,520 --> 00:27:19,560
Speaker 1: opened my frog up and I found a giant mouse

398
00:27:19,640 --> 00:27:24,080
Speaker 1: inside my frog and it had like psychological impact. Yeah,

399
00:27:24,160 --> 00:27:29,440
Speaker 1: I had a psychological impact. I've never recovered. Wow, never recovered, Bob.

400
00:27:29,480 --> 00:27:32,840
Speaker 1: I think we should keep going down the diet alright,

401
00:27:32,920 --> 00:27:36,080
Speaker 1: the diet route, but I think beforehand, maybe like, just

402
00:27:36,119 --> 00:27:39,800
Speaker 1: can you explain how a python hunts and how it

403
00:27:40,040 --> 00:27:43,040
Speaker 1: gets like, you know, eight to z of how he

404
00:27:43,080 --> 00:27:46,800
Speaker 1: gets his prey scots. That's a good question because the

405
00:27:46,840 --> 00:27:51,840
Speaker 1: rent one is confusing to me, like a rent is confusing. Yeah,

406
00:27:52,240 --> 00:27:57,520
Speaker 1: So we think of pythons as being primarily ambush foragers,

407
00:27:58,160 --> 00:28:01,879
Speaker 1: and some people think that me and stage sit somewhere,

408
00:28:01,920 --> 00:28:06,480
Speaker 1: but really they're sequential ambushers. So they move around in

409
00:28:06,520 --> 00:28:10,800
Speaker 1: the environment until they detect praise scent, and then they'll

410
00:28:10,800 --> 00:28:14,280
Speaker 1: investigate that area until they find an area with higher

411
00:28:14,320 --> 00:28:18,959
Speaker 1: concentrations of praiscent, and then they'll set up, often perpendicular

412
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Speaker 1: to a game trail. And yeah, they may then sit

413
00:28:23,800 --> 00:28:27,960
Speaker 1: there for ten to fifteen days without moving. But they

414
00:28:28,000 --> 00:28:31,800
Speaker 1: have heat sensing pits on their lips, so they can

415
00:28:31,960 --> 00:28:37,320
Speaker 1: use vision and the body temperature of an approaching prey item,

416
00:28:37,359 --> 00:28:41,840
Speaker 1: and to some degree they'll use smell but that's pretty

417
00:28:41,880 --> 00:28:45,920
Speaker 1: minimal in inducing strikes. Do you have any idea how

418
00:28:45,960 --> 00:28:51,160
Speaker 1: far out they can sense the heat? Uh? You know,

419
00:28:51,320 --> 00:28:56,160
Speaker 1: there are papers on that, but I would say that

420
00:28:56,200 --> 00:28:58,520
Speaker 1: it's unlikely it's going to be effective more than about

421
00:28:58,520 --> 00:29:03,080
Speaker 1: two meters in most environments anyway. And that's that's going

422
00:29:03,120 --> 00:29:04,600
Speaker 1: to be about the limits of a strike for a

423
00:29:04,600 --> 00:29:10,040
Speaker 1: big python anyway. Um. And then they they strike, they

424
00:29:10,080 --> 00:29:16,160
Speaker 1: grab hold and constrict the strike. Though is it usually

425
00:29:16,200 --> 00:29:18,640
Speaker 1: like do you guys know like where the strike is

426
00:29:18,720 --> 00:29:21,440
Speaker 1: aimed on on animals or is it just anywhere to

427
00:29:21,440 --> 00:29:24,840
Speaker 1: get ahold of it? You know, my buddy Scott's been

428
00:29:25,920 --> 00:29:28,680
Speaker 1: looking at that on some deer that have been regurgitated,

429
00:29:28,720 --> 00:29:32,280
Speaker 1: and it does seem like they're more likely to strike

430
00:29:32,320 --> 00:29:35,720
Speaker 1: it up in the chest thorax region than other places.

431
00:29:35,720 --> 00:29:41,400
Speaker 1: But really, if a big snake hits a prey item,

432
00:29:41,440 --> 00:29:44,040
Speaker 1: it usually knocks it off balance and the snake then

433
00:29:44,240 --> 00:29:47,360
Speaker 1: retracts and as soon as it's it's got one good

434
00:29:47,560 --> 00:29:50,280
Speaker 1: wrap around that prey it, Um, it's not going to

435
00:29:50,320 --> 00:29:55,880
Speaker 1: be able to get away. Um. And then death is

436
00:29:56,000 --> 00:30:00,360
Speaker 1: usually not caused by suffocation. UM. There's a lot of

437
00:30:00,400 --> 00:30:05,040
Speaker 1: interesting new evidence now suggesting that the pressure is so

438
00:30:05,120 --> 00:30:09,440
Speaker 1: strong that it raises blood pressure above the level that

439
00:30:09,520 --> 00:30:13,800
Speaker 1: the heart can pump against. So it basically just stops circulation.

440
00:30:14,080 --> 00:30:16,400
Speaker 1: And if you think about it, once you stop circulation

441
00:30:16,600 --> 00:30:20,240
Speaker 1: to the brain, the animal can be unconscious really quickly.

442
00:30:21,000 --> 00:30:26,040
Speaker 1: And so um it. We've learned a lot, probably just

443
00:30:26,080 --> 00:30:28,280
Speaker 1: in the last five years about some of the things

444
00:30:29,120 --> 00:30:34,080
Speaker 1: on how pythons constricting, what causes death. My buddy Scott

445
00:30:34,080 --> 00:30:40,560
Speaker 1: Boback took rats and then inserted little tiny balloons inside

446
00:30:40,600 --> 00:30:45,680
Speaker 1: their chest. This is these are muthanized rats with a

447
00:30:45,800 --> 00:30:50,440
Speaker 1: little tube to a pressure gauge. He would give those

448
00:30:50,480 --> 00:30:55,080
Speaker 1: to the to a bow constrictor. They constricted and then

449
00:30:55,120 --> 00:30:57,880
Speaker 1: they start to relax because it's not moving. And then

450
00:30:57,920 --> 00:31:01,120
Speaker 1: Scott has this pressure gauge starts simulating a heartbeat with

451
00:31:01,160 --> 00:31:03,480
Speaker 1: a little balloon that's inside it. As soon as that

452
00:31:03,560 --> 00:31:07,640
Speaker 1: heartbeat starts, they clamp down again and so they can

453
00:31:07,880 --> 00:31:12,320
Speaker 1: feel the heartbeat. Yeah, they can feel the heartbeat, and

454
00:31:12,360 --> 00:31:20,880
Speaker 1: they squeeze until it's gone. Whoa the um. So anyway,

455
00:31:20,960 --> 00:31:23,720
Speaker 1: let's let's go back to I guess all this stuff

456
00:31:23,720 --> 00:31:30,160
Speaker 1: they're eating. Yeah, so they can eat really large prey items,

457
00:31:30,160 --> 00:31:32,760
Speaker 1: like I said, and you think about it. If you

458
00:31:32,800 --> 00:31:34,720
Speaker 1: don't have your own body heat, it's going to be

459
00:31:34,840 --> 00:31:38,560
Speaker 1: challenging to digest something that big. So a big snake

460
00:31:38,600 --> 00:31:41,680
Speaker 1: will bask that raises its body heat, but it also

461
00:31:41,760 --> 00:31:48,880
Speaker 1: has this enormous metabolic response where it raises its metabolism

462
00:31:48,880 --> 00:31:52,840
Speaker 1: eighteen fold, which is the difference basically between a sleeping

463
00:31:52,840 --> 00:31:55,840
Speaker 1: horse and a galloping horse. So a snake that's digesting

464
00:31:55,840 --> 00:31:59,720
Speaker 1: a really big meal is just raging internally even though

465
00:31:59,720 --> 00:32:03,600
Speaker 1: you can't see that. And within twenty four hours, the

466
00:32:03,840 --> 00:32:06,880
Speaker 1: mass of their heart increases, the mass of their liver increases,

467
00:32:07,320 --> 00:32:11,160
Speaker 1: their gut gets hugely increased in terms of the little

468
00:32:11,160 --> 00:32:14,080
Speaker 1: tiny folds and the gut the villi that increased surface

469
00:32:14,080 --> 00:32:17,880
Speaker 1: area for digestion. So they're taking stored energy from their

470
00:32:17,960 --> 00:32:23,760
Speaker 1: last meal and almost instantaneously turning it into all this

471
00:32:24,080 --> 00:32:26,640
Speaker 1: organ mass that they need to digest this new meal.

472
00:32:27,520 --> 00:32:32,040
Speaker 1: And if it stored stuff, that's that's primarily going to

473
00:32:32,120 --> 00:32:37,920
Speaker 1: be um conversion of fat and conversion of of uh yeah,

474
00:32:38,000 --> 00:32:49,680
Speaker 1: mostly fat. I guess you know. We're done in South

475
00:32:49,720 --> 00:32:56,200
Speaker 1: America and uh we're fishing with some amor Indians and

476
00:32:56,240 --> 00:32:59,000
Speaker 1: they were telling me that they like to use the

477
00:33:01,520 --> 00:33:05,200
Speaker 1: was it the anaconda fat? Johnnie, I don't remember this.

478
00:33:07,440 --> 00:33:14,480
Speaker 1: Probably they as a as a when you're arthritic, they

479
00:33:14,520 --> 00:33:18,000
Speaker 1: say that if you rubbed the anaconda's fat into your joints,

480
00:33:19,400 --> 00:33:22,040
Speaker 1: it's helpful. I'm not I'm not asking you if this

481
00:33:22,120 --> 00:33:24,560
Speaker 1: is like pharmaceutically sound. I'm just telling you it's like

482
00:33:24,560 --> 00:33:27,479
Speaker 1: a weird that that was why they killed them. If

483
00:33:27,560 --> 00:33:31,200
Speaker 1: you killed one, it was to get the fat. Well,

484
00:33:31,440 --> 00:33:35,040
Speaker 1: I mean, there's there's a reason why snake oil salesman

485
00:33:35,280 --> 00:33:38,080
Speaker 1: is a term. That's a good point. Man. It's been

486
00:33:38,120 --> 00:33:41,960
Speaker 1: it's been used as medicinal you know, all kinds of

487
00:33:42,000 --> 00:33:44,080
Speaker 1: cultures around the world. I mean, oh, you know, I

488
00:33:44,200 --> 00:33:46,720
Speaker 1: never that's funny. I never put that together, Like I know,

489
00:33:46,760 --> 00:33:49,280
Speaker 1: the expression selling snake oil. I never thought about like

490
00:33:49,320 --> 00:33:52,640
Speaker 1: actually selling snake oil. Yeah. Yeah. And and something like

491
00:33:52,680 --> 00:33:56,040
Speaker 1: a python. I mean, I've removed ten kilos of fat

492
00:33:56,160 --> 00:33:59,440
Speaker 1: from a single python, you know, twenty two pounds of fat.

493
00:34:00,000 --> 00:34:03,040
Speaker 1: I've got several bars at ball jars of rendered python

494
00:34:03,120 --> 00:34:05,640
Speaker 1: fat in my freezer right now because I'm thinking that

495
00:34:05,680 --> 00:34:08,880
Speaker 1: eventually I could become a snake oil salesman. Can you

496
00:34:08,920 --> 00:34:10,360
Speaker 1: send me? Is it legal for you? To send me

497
00:34:10,400 --> 00:34:13,040
Speaker 1: one of those jars. Absolutely, I just want like a

498
00:34:13,040 --> 00:34:15,920
Speaker 1: little pint sized jar. Yeah. Do you ever cook with

499
00:34:16,360 --> 00:34:22,800
Speaker 1: bomb um? I haven't? You know it? It's not nasty

500
00:34:22,880 --> 00:34:27,160
Speaker 1: smelling by any means, but it doesn't have that nice, clean,

501
00:34:27,280 --> 00:34:31,279
Speaker 1: large smell either. Uh. Do you like eating the meat off?

502
00:34:31,320 --> 00:34:33,560
Speaker 1: These are people into the meat on the in their

503
00:34:33,640 --> 00:34:38,600
Speaker 1: native range, and then also in Florida. I think that

504
00:34:40,200 --> 00:34:42,200
Speaker 1: I think in the native range they're probably eaten, you

505
00:34:42,200 --> 00:34:45,560
Speaker 1: know occasionally when people come across them. Um. I don't

506
00:34:45,560 --> 00:34:47,840
Speaker 1: really know what's done with the carcasses in the skin trade,

507
00:34:48,400 --> 00:34:53,600
Speaker 1: but in Florida, So the Everglades has an interesting atmosphere

508
00:34:53,800 --> 00:34:57,840
Speaker 1: because all that greenery puts out huge amounts of water

509
00:34:58,040 --> 00:35:03,200
Speaker 1: into the air that turns into these towering clouds, and

510
00:35:03,200 --> 00:35:07,280
Speaker 1: those clouds reach so high that they in turn pull

511
00:35:08,000 --> 00:35:11,560
Speaker 1: airborne mercury out of the air and those upper air

512
00:35:11,640 --> 00:35:15,799
Speaker 1: layers and deposit it as rain. And so the Everyglades

513
00:35:15,800 --> 00:35:19,120
Speaker 1: are known for having um fairly high mercury levels for

514
00:35:19,160 --> 00:35:24,680
Speaker 1: a lot of say game fish. And you know, the

515
00:35:24,680 --> 00:35:27,399
Speaker 1: the safe limits for mercury, depending on where you are,

516
00:35:27,520 --> 00:35:30,440
Speaker 1: anywhere between point five and one point five parts per

517
00:35:30,480 --> 00:35:34,719
Speaker 1: million um pythons have come out as high as three

518
00:35:34,719 --> 00:35:39,799
Speaker 1: point five parts per million, So you definitely would want

519
00:35:39,800 --> 00:35:42,600
Speaker 1: to have a python tested before you eat it because

520
00:35:42,680 --> 00:35:45,000
Speaker 1: they can have mercury loads that are insane. Can you

521
00:35:45,040 --> 00:35:51,920
Speaker 1: explain to people bio accumulation, like how that mercury builds up. Yeah,

522
00:35:51,960 --> 00:35:57,880
Speaker 1: So the mercury is deposited um into primarily into waterways,

523
00:35:57,920 --> 00:36:01,799
Speaker 1: and it gets transformed into methyl mercury that can be

524
00:36:01,840 --> 00:36:06,600
Speaker 1: taken up by various small organisms, and then successive layers

525
00:36:06,640 --> 00:36:11,080
Speaker 1: of predators then build up more and more of it

526
00:36:11,200 --> 00:36:13,719
Speaker 1: in their tissues, and so by the time you get

527
00:36:13,760 --> 00:36:16,359
Speaker 1: to something like an alligator or a python that's been

528
00:36:16,360 --> 00:36:22,080
Speaker 1: eating everything from fish to heron's that might have slightly

529
00:36:22,120 --> 00:36:25,600
Speaker 1: elevated mercury. They can end up with pretty high levels themselves.

530
00:36:26,160 --> 00:36:29,520
Speaker 1: But people, but there's no problem eating Florida gator. I mean,

531
00:36:29,600 --> 00:36:31,839
Speaker 1: well maybe there is, but we've eaten it, and it's

532
00:36:32,160 --> 00:36:35,080
Speaker 1: commercially available. You can go online and have it delivered

533
00:36:35,120 --> 00:36:37,719
Speaker 1: in a day or two to your house. Yep. Of course,

534
00:36:37,760 --> 00:36:41,200
Speaker 1: most of those are farmers gaping, so they're they're fed

535
00:36:41,320 --> 00:36:44,439
Speaker 1: controlled food, um, so they might not be as high

536
00:36:44,440 --> 00:36:47,359
Speaker 1: in mercury. Yeah, you'd think they'd probably be very low.

537
00:36:48,000 --> 00:36:50,840
Speaker 1: And then when you go north of the Everglades um,

538
00:36:50,920 --> 00:36:53,640
Speaker 1: you don't have quite those same atmospheric conditions and you

539
00:36:53,680 --> 00:36:56,880
Speaker 1: don't have quite as much build up to the north um.

540
00:36:56,920 --> 00:37:00,680
Speaker 1: You know that said, I would definitely have a really

541
00:37:00,760 --> 00:37:04,399
Speaker 1: big gait or tested before I ate it. So let's

542
00:37:04,440 --> 00:37:06,959
Speaker 1: let's let's jump back into the impact that they're having

543
00:37:07,000 --> 00:37:09,839
Speaker 1: on the landscape. There's a ton of them. We don't

544
00:37:09,840 --> 00:37:11,560
Speaker 1: know how many. I want to talk about that too,

545
00:37:11,640 --> 00:37:13,839
Speaker 1: like how many of these things are there? But let's

546
00:37:13,840 --> 00:37:18,839
Speaker 1: talk first about what have you seen in terms of

547
00:37:18,880 --> 00:37:24,799
Speaker 1: the impact they're having on these dozens of species of

548
00:37:24,960 --> 00:37:29,719
Speaker 1: native wildlife that they feed on. Oh and do they

549
00:37:29,760 --> 00:37:33,719
Speaker 1: like wild pigs? Uh? They do, although there's only a

550
00:37:33,719 --> 00:37:37,600
Speaker 1: few records of wild pigs. Most of the pigs are

551
00:37:38,320 --> 00:37:41,080
Speaker 1: pigs start getting common farther north. There aren't really all

552
00:37:41,120 --> 00:37:44,319
Speaker 1: that many pigs in every Glades National Park itself. But

553
00:37:44,880 --> 00:37:48,319
Speaker 1: more generally, there's three lines of evidence you can use

554
00:37:48,640 --> 00:37:52,640
Speaker 1: for assessing impacts. Ones just the list of species, and

555
00:37:52,760 --> 00:37:54,840
Speaker 1: like I said, we've got seventy one species. Some of

556
00:37:54,840 --> 00:37:58,120
Speaker 1: those are federally endangered, like the Key Largo wood rat um,

557
00:37:58,840 --> 00:38:02,120
Speaker 1: where the wood stork. But that doesn't tell you much

558
00:38:02,120 --> 00:38:06,960
Speaker 1: about impacts to populations, and so the next best step

559
00:38:07,160 --> 00:38:11,080
Speaker 1: is a correlative study, and so UM I was involved

560
00:38:11,080 --> 00:38:14,720
Speaker 1: with one a few years ago, and that involved driving

561
00:38:14,800 --> 00:38:19,640
Speaker 1: roads in areas in every Glades Park with pythons, in

562
00:38:19,719 --> 00:38:23,240
Speaker 1: areas where pythons had just recently reached in in areas

563
00:38:23,239 --> 00:38:27,360
Speaker 1: with no pythons, And I think we ended up with

564
00:38:27,400 --> 00:38:31,879
Speaker 1: about six of driving that we did, and we were

565
00:38:31,920 --> 00:38:38,279
Speaker 1: recording every snake and every native species that we saw.

566
00:38:39,880 --> 00:38:47,120
Speaker 1: And the upshot of that is that in the areas

567
00:38:47,400 --> 00:38:50,920
Speaker 1: with pythons in every Glades National Park, we had a

568
00:38:52,000 --> 00:38:59,080
Speaker 1: decrease in raccoons, decrease in opossums, we had decrease, Yes,

569
00:38:59,520 --> 00:39:03,240
Speaker 1: we had ro marsha rabbits. We had an eighties seven

570
00:39:03,280 --> 00:39:08,600
Speaker 1: percent decrease in bobcats. And so there's there's a range

571
00:39:08,600 --> 00:39:13,080
Speaker 1: of species that are essentially gone from every Glades National Park.

572
00:39:13,200 --> 00:39:19,160
Speaker 1: They tend to be midsized mammals, marsha rabbits. Uh, yeah,

573
00:39:19,200 --> 00:39:22,160
Speaker 1: what you gave it that there was zero So I

574
00:39:22,239 --> 00:39:28,759
Speaker 1: understand like increase, but of these different species, what, um,

575
00:39:28,800 --> 00:39:33,319
Speaker 1: what do you know about it in terms of raw

576
00:39:33,440 --> 00:39:36,200
Speaker 1: numbers for people to think about is there an estimate

577
00:39:36,280 --> 00:39:41,440
Speaker 1: of pre python bobcat population. Yeah, so this was actually

578
00:39:41,560 --> 00:39:44,680
Speaker 1: neglected to mention that this was pre imposed. Um. So

579
00:39:44,760 --> 00:39:46,480
Speaker 1: we we looked at it two ways. We looked at

580
00:39:46,600 --> 00:39:52,040
Speaker 1: it based on surveys from nineteen six before pythons were

581
00:39:52,280 --> 00:39:55,680
Speaker 1: abundant in the park versus surveys from about the mid

582
00:39:55,680 --> 00:39:58,680
Speaker 1: two thousand's, and then we looked at it along that

583
00:39:58,680 --> 00:40:02,960
Speaker 1: that trans act of high python abundance to zero pythons.

584
00:40:03,160 --> 00:40:08,520
Speaker 1: So as far as pre abundance, there are lots of

585
00:40:08,560 --> 00:40:12,879
Speaker 1: anecdotal reports and field field notes from people um in

586
00:40:12,920 --> 00:40:16,759
Speaker 1: the say early nineties driving levees in the Everglades and

587
00:40:16,800 --> 00:40:21,440
Speaker 1: saying saw over a hundred marsh rabbits. They used to

588
00:40:21,480 --> 00:40:28,520
Speaker 1: be incredibly commonly seen because when it's the wet season,

589
00:40:29,120 --> 00:40:31,080
Speaker 1: all the rabbits are on the dry land and that

590
00:40:31,120 --> 00:40:36,000
Speaker 1: means tree islands and levees, so they get concentrated. Um.

591
00:40:36,040 --> 00:40:39,400
Speaker 1: I've been going to the Everglades since two thousand and six.

592
00:40:39,640 --> 00:40:41,840
Speaker 1: I have never seen a marsh rabbit in every Glades

593
00:40:41,960 --> 00:40:47,960
Speaker 1: National Park. They're gone. Well, they got wiped out by pythons.

594
00:40:48,440 --> 00:40:51,319
Speaker 1: They got wiped out, and so that's the question. What

595
00:40:51,440 --> 00:40:55,440
Speaker 1: did it? And so that led to the manipulative experiment

596
00:40:55,480 --> 00:40:58,480
Speaker 1: that we did a few years later. And this was

597
00:40:58,800 --> 00:41:01,719
Speaker 1: led by some college exit University of Florida. And I

598
00:41:01,760 --> 00:41:03,640
Speaker 1: need to give a shout out to A. D. S.

599
00:41:03,600 --> 00:41:05,440
Speaker 1: O Vi who was the grad student who did it,

600
00:41:05,520 --> 00:41:08,799
Speaker 1: because the amount of work she did was inhuman. It

601
00:41:08,920 --> 00:41:12,600
Speaker 1: was I still can't believe she pulled this off. So

602
00:41:12,680 --> 00:41:18,600
Speaker 1: in that study, we took rabbits from north of the

603
00:41:18,600 --> 00:41:23,959
Speaker 1: python distribution marsh rabbits, trapped them. Then we established two

604
00:41:23,960 --> 00:41:27,480
Speaker 1: populations of fifteen rabbits. But I got I got a

605
00:41:27,480 --> 00:41:30,560
Speaker 1: whole bunch of questions, Yeah, how are we catching them?

606
00:41:30,560 --> 00:41:33,520
Speaker 1: How are you catching the marsh rabbits basically have the

607
00:41:33,600 --> 00:41:41,399
Speaker 1: hearts yep, um, So let's see it would be really

608
00:41:41,440 --> 00:41:45,400
Speaker 1: interested in this area. Yeah, where you're getting these marshas,

609
00:41:47,560 --> 00:41:51,040
Speaker 1: Where you're getting these marsh rabbits from? Yeah, So she

610
00:41:51,040 --> 00:41:55,000
Speaker 1: she trapped ninety five rabbits. She's got She established two

611
00:41:55,040 --> 00:41:59,400
Speaker 1: populations of fifteen each in every Glades National Park. She

612
00:41:59,520 --> 00:42:04,560
Speaker 1: established another population of fifteen outside of the python range.

613
00:42:04,560 --> 00:42:08,320
Speaker 1: And that's the procedural control to see whether relocating rabbits

614
00:42:08,400 --> 00:42:11,640
Speaker 1: kills them. Got you? And then she left the remaining

615
00:42:11,920 --> 00:42:19,239
Speaker 1: forty something in place as a regular control and and

616
00:42:19,320 --> 00:42:21,480
Speaker 1: presumably put some kind of track and device on all

617
00:42:21,520 --> 00:42:23,959
Speaker 1: these things. Every single one of them had a radio collar.

618
00:42:24,640 --> 00:42:27,400
Speaker 1: And how do you know you've established a population of

619
00:42:27,520 --> 00:42:33,120
Speaker 1: fift that's that good question. Um. So, marsh rabbits like

620
00:42:33,239 --> 00:42:35,879
Speaker 1: to poop on latrines that they use over and over again,

621
00:42:36,000 --> 00:42:38,400
Speaker 1: just like you know swamp rabbits pooping on logs. You

622
00:42:38,440 --> 00:42:40,600
Speaker 1: walk through the swamp looking for a log that has

623
00:42:40,600 --> 00:42:42,879
Speaker 1: poop on it, and you know there's swamp rabbit around. Oh.

624
00:42:42,920 --> 00:42:47,919
Speaker 1: I thought, okay, okay, this is helpful because I thought

625
00:42:47,960 --> 00:42:50,800
Speaker 1: when you're saying marsh rabbits, I thought you were talking

626
00:42:50,840 --> 00:42:54,880
Speaker 1: about swamp rabbits. Yep, so you swamp rabbits with the

627
00:42:54,880 --> 00:42:57,360
Speaker 1: big boys, marsh rabbits are more the size of a

628
00:42:57,400 --> 00:43:00,560
Speaker 1: cotton tail. Oh so we're not talking big like expound

629
00:43:01,560 --> 00:43:12,600
Speaker 1: Leviathan cotton tales nor um. So, we established artificial latrines,

630
00:43:12,719 --> 00:43:16,200
Speaker 1: which were basically just elevated pieces of plywood with a

631
00:43:16,239 --> 00:43:18,799
Speaker 1: piece of astro turf on top, and the rabbits start

632
00:43:18,880 --> 00:43:21,920
Speaker 1: using them. And we saw that in all these locations.

633
00:43:22,000 --> 00:43:26,000
Speaker 1: Initially we had rabbits using the latrines and we had

634
00:43:26,040 --> 00:43:28,840
Speaker 1: reproduction because they were small pellets that showed up to

635
00:43:29,000 --> 00:43:32,440
Speaker 1: We only translocated adult rabbits, so we knew there was

636
00:43:32,480 --> 00:43:37,359
Speaker 1: reproduction going on, and we tracked them for a year

637
00:43:38,000 --> 00:43:41,320
Speaker 1: and during that year almost all the rabbits died. That's

638
00:43:41,440 --> 00:43:45,200
Speaker 1: expected because they're rabbits, they don't last very long. But

639
00:43:45,360 --> 00:43:48,960
Speaker 1: was what was interesting was that in every Glades National

640
00:43:49,000 --> 00:43:54,160
Speaker 1: Park you had these two rabbit populations, there was some predation.

641
00:43:54,480 --> 00:43:56,200
Speaker 1: Most of it was pythons, and we know it was

642
00:43:56,239 --> 00:43:59,120
Speaker 1: pythons because we would track the rabbit signal and it

643
00:43:59,120 --> 00:44:03,320
Speaker 1: would be inside up ithon. That's that's a dead giveaway. Yeah,

644
00:44:03,480 --> 00:44:07,120
Speaker 1: that's a that's a pretty good indicator. But then towards

645
00:44:07,160 --> 00:44:09,799
Speaker 1: the end, as the water levels rose in the summertime,

646
00:44:10,880 --> 00:44:13,600
Speaker 1: the rabbits get a little more concentrated and they just

647
00:44:13,719 --> 00:44:19,160
Speaker 1: got hammered. So sev of the rabbits in Everglades were

648
00:44:19,200 --> 00:44:22,080
Speaker 1: known to have been eaten by pythons. And at the

649
00:44:22,239 --> 00:44:25,800
Speaker 1: end of the year there were no rabbits left in

650
00:44:25,840 --> 00:44:29,839
Speaker 1: the Everglades, so even all the juveniles were gone, and

651
00:44:29,840 --> 00:44:33,279
Speaker 1: those those little populations had been wiped out. Whereas in

652
00:44:33,360 --> 00:44:37,560
Speaker 1: the areas where we had no pythons. Yeah, most of

653
00:44:37,600 --> 00:44:40,720
Speaker 1: our original rabbits were dead, because that's what happens to rabbits,

654
00:44:40,800 --> 00:44:44,279
Speaker 1: but those latrines were still used because you still had

655
00:44:44,320 --> 00:44:47,520
Speaker 1: lots of rabbits left. And so that was for me

656
00:44:47,680 --> 00:44:49,799
Speaker 1: kind of the nail in the coffin, showing that, yes,

657
00:44:51,239 --> 00:44:53,840
Speaker 1: we had lists of species, we know what they're eating,

658
00:44:54,120 --> 00:44:57,840
Speaker 1: we had correlative evidence that they've suppressed a bunch of species,

659
00:44:58,080 --> 00:45:01,680
Speaker 1: and now we can say mere mentally, they can drive

660
00:45:02,040 --> 00:45:07,400
Speaker 1: this muso mammal population to extinction, which it's pretty amazing.

661
00:45:07,719 --> 00:45:10,719
Speaker 1: Have you thought about replicating that study with something that's

662
00:45:10,760 --> 00:45:15,839
Speaker 1: longer lived, like like getting some coons or something, you know, Yeah,

663
00:45:15,920 --> 00:45:18,080
Speaker 1: you're in my mind. I'd love to do it with raccoons,

664
00:45:18,400 --> 00:45:23,799
Speaker 1: um raccoons. I don't know how much we know about

665
00:45:23,840 --> 00:45:27,640
Speaker 1: translocating raccoons. You know, rabbits tend to like to hang

666
00:45:27,640 --> 00:45:29,680
Speaker 1: out with other rabbits, so if you put them in

667
00:45:29,680 --> 00:45:33,440
Speaker 1: an area, they'll probably stay there. I got you moving raccoons,

668
00:45:34,040 --> 00:45:38,400
Speaker 1: I really might display they're just just take off and

669
00:45:38,440 --> 00:45:41,600
Speaker 1: not find each other, not start. Yeah. On the other hand,

670
00:45:41,600 --> 00:45:44,759
Speaker 1: they might be big enough to take satellite tags, so

671
00:45:45,120 --> 00:45:47,960
Speaker 1: you could actually follow them without having to walk out

672
00:45:48,000 --> 00:45:50,080
Speaker 1: in the marsh um and you can get a satellite

673
00:45:50,080 --> 00:45:52,560
Speaker 1: tag with the mortality sensor and no when it stops moving.

674
00:45:53,280 --> 00:45:55,880
Speaker 1: But yeah, but the problem with that is it wouldn't

675
00:45:55,880 --> 00:46:00,200
Speaker 1: stop moving, It would just move around inside a snake. Yeah,

676
00:46:00,200 --> 00:46:04,600
Speaker 1: and that's a question whether a digesting python moves enough

677
00:46:05,239 --> 00:46:09,759
Speaker 1: to trigger immortality sensor. I don't know. There's a there's

678
00:46:09,760 --> 00:46:13,719
Speaker 1: a massive deer known Fate study that's going on in

679
00:46:13,760 --> 00:46:15,920
Speaker 1: southern Florida right now, and it's been going on for

680
00:46:16,080 --> 00:46:20,520
Speaker 1: three or four years. But unfortunately, all those colored deer

681
00:46:20,880 --> 00:46:24,600
Speaker 1: are almost all of them are north of the Python distribution,

682
00:46:24,680 --> 00:46:27,000
Speaker 1: so we won't be able to say much about whether

683
00:46:27,040 --> 00:46:29,319
Speaker 1: Python's knocked out the deer in the Everglades. Part of

684
00:46:29,320 --> 00:46:33,279
Speaker 1: the reason for this study was that dear populations have

685
00:46:33,400 --> 00:46:37,920
Speaker 1: been decreasing by quite a bit in southern Florida, and

686
00:46:37,960 --> 00:46:42,040
Speaker 1: no one knew why. But they couldn't find enough in

687
00:46:42,120 --> 00:46:47,279
Speaker 1: Everglades to call or to figure out if it was pythons. Uh.

688
00:46:47,800 --> 00:46:54,520
Speaker 1: Are you familiar with the theory? I think you can

689
00:46:54,600 --> 00:46:56,960
Speaker 1: qualify this as a conspiracy theory. I don't mean that

690
00:46:57,000 --> 00:46:59,800
Speaker 1: in a negative way. Are you familiar with the theory

691
00:46:59,840 --> 00:47:06,160
Speaker 1: that at the Florida panther as it recovers and expands,

692
00:47:07,640 --> 00:47:12,759
Speaker 1: is killing all the deer and all the game, and

693
00:47:12,800 --> 00:47:16,200
Speaker 1: all the raccoons everything else right, And the people who

694
00:47:16,239 --> 00:47:20,160
Speaker 1: are pro panther and who don't want any kind of

695
00:47:20,160 --> 00:47:25,040
Speaker 1: mortal control of panthers want to hide the fact of

696
00:47:25,080 --> 00:47:28,320
Speaker 1: the panthers are killing all the game from the public,

697
00:47:29,440 --> 00:47:34,600
Speaker 1: so they blame all the missing game on the pythons

698
00:47:34,760 --> 00:47:42,200
Speaker 1: in order to protect the panthers. I think anytime your

699
00:47:42,200 --> 00:47:45,719
Speaker 1: explanation takes that long to get to what you're trying

700
00:47:45,760 --> 00:47:50,880
Speaker 1: to say. Have you ever heard what we have you

701
00:47:50,880 --> 00:47:57,399
Speaker 1: ever heard what we heard about why wolves were reintroduced? Um,

702
00:47:57,640 --> 00:48:00,759
Speaker 1: there's a theory that there's a it's a long play

703
00:48:00,760 --> 00:48:06,160
Speaker 1: by the Clintons that if they reintroduced wolves, the wolves

704
00:48:06,200 --> 00:48:09,520
Speaker 1: would kill all of the game, No one would have

705
00:48:09,560 --> 00:48:13,160
Speaker 1: a reason to hunt anymore, no one would buy any guns,

706
00:48:13,320 --> 00:48:16,280
Speaker 1: and that would help you take over the country. Wow,

707
00:48:16,560 --> 00:48:18,440
Speaker 1: were they breeding the wolves in the basement of a

708
00:48:18,440 --> 00:48:23,399
Speaker 1: pizza shop and d C? Yes? Yeah, okay, Um, Well,

709
00:48:23,560 --> 00:48:28,120
Speaker 1: going back to your question, you know, yes, there are

710
00:48:28,239 --> 00:48:33,480
Speaker 1: lots of conspiracy theories about pythons, and I would love

711
00:48:33,520 --> 00:48:36,839
Speaker 1: to hear all of them. But I think I think

712
00:48:36,840 --> 00:48:41,920
Speaker 1: that that question can be answered very shortly by saying

713
00:48:41,960 --> 00:48:47,080
Speaker 1: that the highest panther densities are well north of the

714
00:48:47,120 --> 00:48:51,080
Speaker 1: pythons and well west, you know, up in the panther refuge,

715
00:48:51,080 --> 00:48:55,440
Speaker 1: for example. There's no pythons up there, And so trying

716
00:48:55,480 --> 00:48:59,200
Speaker 1: to say that the pipe the panthers are knocking down

717
00:48:59,239 --> 00:49:02,400
Speaker 1: game doesn't make much sense because there's still plenty of

718
00:49:02,440 --> 00:49:05,319
Speaker 1: game in the areas where there's the most panthers. Yeah,

719
00:49:05,400 --> 00:49:08,600
Speaker 1: but did you see that, Uh, this is not conspiracy theory.

720
00:49:09,680 --> 00:49:12,080
Speaker 1: Did you see those mortality studies they did on deer.

721
00:49:13,719 --> 00:49:17,640
Speaker 1: Uh in Florida. Panthers are are I mean, they're not

722
00:49:17,680 --> 00:49:20,960
Speaker 1: out there whistling Dixie. Yeah, Well, that that's that, that

723
00:49:21,120 --> 00:49:23,200
Speaker 1: dear mortality study I was talking about, you know, and

724
00:49:23,400 --> 00:49:26,919
Speaker 1: and but I mean they're not they're not eliminating from

725
00:49:27,000 --> 00:49:31,239
Speaker 1: them from the landscape, but they're definitely eating them. Yep, yep,

726
00:49:31,440 --> 00:49:33,640
Speaker 1: that's that's what they're supposed to do, right, Yeah, I

727
00:49:33,640 --> 00:49:37,759
Speaker 1: would gather I would do that as well. Um, So,

728
00:49:40,360 --> 00:49:44,480
Speaker 1: how I got a couple of questions for you. You're saying,

729
00:49:44,600 --> 00:49:49,040
Speaker 1: you say snakes are hard to count. What is you

730
00:49:49,120 --> 00:49:51,359
Speaker 1: if you had to guess like God's got a gun

731
00:49:51,400 --> 00:49:55,279
Speaker 1: to your head, right, and you had to guess how

732
00:49:55,320 --> 00:49:59,640
Speaker 1: many snakes per unit of space exists in the highest

733
00:49:59,719 --> 00:50:03,400
Speaker 1: ab London's areas? What would you what would you guess

734
00:50:03,440 --> 00:50:08,120
Speaker 1: if you if it was a life or death situation?

735
00:50:08,920 --> 00:50:13,320
Speaker 1: Oh cheese, like if you get it? Like I know, Okay,

736
00:50:13,360 --> 00:50:16,520
Speaker 1: let me paint the picture for you. I'm this omniscient

737
00:50:16,600 --> 00:50:20,240
Speaker 1: being that knows all truth. I'm the boss of all knowledge,

738
00:50:20,600 --> 00:50:22,960
Speaker 1: and I know the truth. And I say to you

739
00:50:23,000 --> 00:50:25,960
Speaker 1: how many are there? And you have to get it

740
00:50:26,040 --> 00:50:28,680
Speaker 1: right or else you have to die and you just

741
00:50:28,719 --> 00:50:31,879
Speaker 1: gotta take a wild stab in the dark. Yeah, this

742
00:50:31,920 --> 00:50:34,480
Speaker 1: is I know as a scientist, this is boiling your blood.

743
00:50:34,920 --> 00:50:36,879
Speaker 1: But what would you what would you throw out there?

744
00:50:37,480 --> 00:50:41,400
Speaker 1: What would you throw out? I think I'd book end

745
00:50:41,440 --> 00:50:43,680
Speaker 1: it by saying that I don't know if I don't

746
00:50:43,680 --> 00:50:47,560
Speaker 1: know if any herpetologists, I don't know if any any

747
00:50:47,600 --> 00:50:52,400
Speaker 1: herpetologists experienced in snake population estimate, who would say that

748
00:50:52,440 --> 00:50:56,040
Speaker 1: there's less than ten thousand pythons in the Everglades and

749
00:50:56,160 --> 00:51:00,440
Speaker 1: so that would mean, you know, for per square kilometer,

750
00:51:01,400 --> 00:51:06,920
Speaker 1: But we know that giant snakes can reach higher densities,

751
00:51:06,920 --> 00:51:09,319
Speaker 1: and that based on some limited studies of you know,

752
00:51:09,400 --> 00:51:13,799
Speaker 1: a similar species in Africa um and some of the

753
00:51:14,520 --> 00:51:18,000
Speaker 1: preliminary work that we've done on removing snakes from levees.

754
00:51:18,400 --> 00:51:21,960
Speaker 1: You know, there are individual levees from which over a

755
00:51:22,000 --> 00:51:24,560
Speaker 1: hundred snakes a year are being removed right now by

756
00:51:24,600 --> 00:51:28,600
Speaker 1: paid python hunters. Those levees might be ten kilometers long,

757
00:51:29,480 --> 00:51:36,480
Speaker 1: So from you know, ten thousand, two hundred thousand, I'm

758
00:51:36,520 --> 00:51:40,000
Speaker 1: really comfortable with anywhere in that range. It's that wide.

759
00:51:43,880 --> 00:51:46,440
Speaker 1: Once you get over a hundred thousand. I know people

760
00:51:46,440 --> 00:51:50,920
Speaker 1: who say absolutely, and I say other people who say, oh, no,

761
00:51:51,040 --> 00:51:55,600
Speaker 1: that's not possible. But that's because generally those people don't

762
00:51:55,680 --> 00:52:01,239
Speaker 1: understand detection probabilities, and detection probability is the most important

763
00:52:01,239 --> 00:52:03,640
Speaker 1: factor you need to understand if you want to know

764
00:52:03,719 --> 00:52:08,040
Speaker 1: something about snakes. They are just phenomenally good at staying

765
00:52:08,080 --> 00:52:11,080
Speaker 1: hidden from us. You know, all the time we get

766
00:52:11,080 --> 00:52:14,400
Speaker 1: people saying, hey, we wiped out most of the bison,

767
00:52:14,520 --> 00:52:17,560
Speaker 1: we wiped out the passenger pigeon. Just you know, let

768
00:52:17,560 --> 00:52:20,800
Speaker 1: the bubbas at them and we'll have no more pythons

769
00:52:20,880 --> 00:52:25,480
Speaker 1: very soon. You know, I can see a bison from

770
00:52:25,760 --> 00:52:30,120
Speaker 1: four miles away out in the prairie. They're easy to

771
00:52:30,200 --> 00:52:34,440
Speaker 1: wipe out. But in contrast, I've had a twelve ft

772
00:52:34,960 --> 00:52:39,359
Speaker 1: python that contains a radio transmitter in it, and we've

773
00:52:39,400 --> 00:52:43,840
Speaker 1: got six people standing in a six ft circle around

774
00:52:43,880 --> 00:52:47,880
Speaker 1: that snake. It's in six inches of water and you

775
00:52:48,000 --> 00:52:53,520
Speaker 1: cannot see it. It is invisible. And then while you're

776
00:52:53,560 --> 00:52:55,960
Speaker 1: standing there talking about how amazing it is that you

777
00:52:55,960 --> 00:52:58,640
Speaker 1: can't see this python, you turn the receiver on again

778
00:52:59,160 --> 00:53:05,480
Speaker 1: and it's fifty ft away. Huh. So they're just incredibly

779
00:53:05,560 --> 00:53:12,640
Speaker 1: stealthy and secretive, and that colors everybody's perception of them

780
00:53:12,719 --> 00:53:16,040
Speaker 1: in one way or the other. If you understand detection probability,

781
00:53:16,120 --> 00:53:18,399
Speaker 1: you understand that there's far more of them out there

782
00:53:18,760 --> 00:53:21,760
Speaker 1: than most people want to believe. And if you don't,

783
00:53:22,120 --> 00:53:24,960
Speaker 1: you think, wow, look at all these snakes were removed.

784
00:53:25,000 --> 00:53:29,799
Speaker 1: We must be really knocking down that population. That makes

785
00:53:29,800 --> 00:53:35,120
Speaker 1: you feel like you're not scratching it. You know, right now,

786
00:53:35,160 --> 00:53:37,080
Speaker 1: there's a lot of effort and a lot of money

787
00:53:37,160 --> 00:53:41,640
Speaker 1: going towards paying people to remove pythons um from the

788
00:53:41,719 --> 00:53:44,720
Speaker 1: Greater Everglades ecosystem, both in and out of the park,

789
00:53:45,640 --> 00:53:50,680
Speaker 1: and um the people who are doing that, they're you know,

790
00:53:50,880 --> 00:53:56,160
Speaker 1: they're mostly great folks. They care a lot there, um,

791
00:53:56,200 --> 00:53:59,520
Speaker 1: spending lots of time out in the field, and the

792
00:53:59,640 --> 00:54:02,719
Speaker 1: removed a lot of pythons, you know, um, over two

793
00:54:02,760 --> 00:54:08,319
Speaker 1: thousand last year. We had a recent study where we

794
00:54:08,440 --> 00:54:13,080
Speaker 1: had several known telmetered pythons along a levee and then

795
00:54:13,120 --> 00:54:20,600
Speaker 1: we did walking surveys um and in I'd have to

796
00:54:20,600 --> 00:54:24,960
Speaker 1: look at how many Yeah, we had about five of

797
00:54:25,040 --> 00:54:27,560
Speaker 1: walking that we did over the course of a few

798
00:54:27,560 --> 00:54:32,920
Speaker 1: months with known snakes that were available for detection. And

799
00:54:33,400 --> 00:54:35,680
Speaker 1: I'll give you up. Let you guess how many times

800
00:54:35,960 --> 00:54:40,680
Speaker 1: we saw one of our kilometered pythons zero. Oh damn,

801
00:54:40,680 --> 00:54:47,080
Speaker 1: you're right man self whisper. So, so that means like

802
00:54:47,400 --> 00:54:51,279
Speaker 1: we we calculated at the chance. It's like, you've got

803
00:54:51,280 --> 00:54:54,560
Speaker 1: this python named George. It's out in the ecosystem in

804
00:54:54,640 --> 00:54:57,080
Speaker 1: an area that that humans can get you along a

805
00:54:57,160 --> 00:55:01,160
Speaker 1: levey our chances of detecting it on any given day

806
00:55:01,440 --> 00:55:06,719
Speaker 1: are probably less than one percent, and probably more than

807
00:55:08,080 --> 00:55:12,480
Speaker 1: of the total area occupied by pythons is way less accessible,

808
00:55:13,400 --> 00:55:15,520
Speaker 1: so it's hard for people to even get in there.

809
00:55:16,280 --> 00:55:21,200
Speaker 1: So if we're taking two thousand pythons off of canal

810
00:55:21,320 --> 00:55:27,560
Speaker 1: edges and roads, which is where the great majority come from.

811
00:55:27,719 --> 00:55:32,320
Speaker 1: Does that mean we're having an impact on the population. Um?

812
00:55:32,360 --> 00:55:35,360
Speaker 1: I think that's we don't have any evidence to suggest

813
00:55:35,480 --> 00:55:40,319
Speaker 1: that we're doing much by removing those snakes. However, there's

814
00:55:40,320 --> 00:55:43,400
Speaker 1: a philosophical difference. You know, people say every snake we

815
00:55:43,440 --> 00:55:47,040
Speaker 1: take out is one less snake that's eating native animals,

816
00:55:48,360 --> 00:55:50,840
Speaker 1: and I'm not going to argue with that. You know,

817
00:55:50,840 --> 00:55:53,920
Speaker 1: it's the difference between people who say that, um, they

818
00:55:53,960 --> 00:55:56,839
Speaker 1: care about the welfare of individual animals versus the people

819
00:55:56,880 --> 00:55:59,520
Speaker 1: who say they care about you know that the persistence

820
00:55:59,680 --> 00:56:03,959
Speaker 1: of dative animal populations and that you know that comes

821
00:56:04,000 --> 00:56:06,680
Speaker 1: up in the hunting world a lot. I know what

822
00:56:06,880 --> 00:56:09,399
Speaker 1: side of the spectrum I fall out on in terms

823
00:56:09,480 --> 00:56:13,760
Speaker 1: of which which one of those I think we should

824
00:56:13,760 --> 00:56:16,120
Speaker 1: be pushing for. But I'm not going to tell those

825
00:56:16,120 --> 00:56:20,479
Speaker 1: people they're wrong. It's more of a philosophical difference than

826
00:56:21,040 --> 00:56:26,439
Speaker 1: a science difference. Yeah, like they're not. Is it's fair

827
00:56:26,520 --> 00:56:30,480
Speaker 1: to say that if you're like a python hunter, you're

828
00:56:30,480 --> 00:56:35,720
Speaker 1: not hurting anything. You you may well be doing good. Um.

829
00:56:35,760 --> 00:56:41,880
Speaker 1: I just think that from an evidentially standpoint, where it

830
00:56:41,920 --> 00:56:45,240
Speaker 1: would be nice if we could get the scientists together

831
00:56:45,280 --> 00:56:47,960
Speaker 1: with those folks and really come up with a way

832
00:56:48,120 --> 00:56:52,960
Speaker 1: two estimate the impacts on overall population size. And I

833
00:56:53,000 --> 00:56:55,600
Speaker 1: think we're moving that way with UM. We're going to

834
00:56:55,680 --> 00:57:00,560
Speaker 1: have some pretty big telemetry studies going on, and we're

835
00:57:00,560 --> 00:57:03,040
Speaker 1: doing that to understand what the snakes are doing. But

836
00:57:03,120 --> 00:57:05,960
Speaker 1: it also means we know the number of known snakes

837
00:57:05,960 --> 00:57:09,000
Speaker 1: out there, and we'll be able to know when one

838
00:57:09,040 --> 00:57:11,359
Speaker 1: of them gets picked up by a python hunter. And

839
00:57:11,440 --> 00:57:13,880
Speaker 1: you compare those you know, known snakes removed to the

840
00:57:13,880 --> 00:57:16,880
Speaker 1: total number removed, maybe we can start zero again in

841
00:57:16,960 --> 00:57:23,320
Speaker 1: a population estimate. How what's a big python? And how

842
00:57:23,360 --> 00:57:27,840
Speaker 1: old is it? When it gets that big? Um? Big python?

843
00:57:28,000 --> 00:57:29,920
Speaker 1: I think the biggest We've got several that are over

844
00:57:29,960 --> 00:57:38,280
Speaker 1: eighteen ft and um fifty pounds, and those are pretty rare,

845
00:57:38,640 --> 00:57:42,000
Speaker 1: you know, once you get up past about the thirteen

846
00:57:42,040 --> 00:57:47,880
Speaker 1: foot range, they're pretty much all females and snakes over

847
00:57:48,000 --> 00:57:52,320
Speaker 1: fourteen ft feet represent probably less than five percent of

848
00:57:52,320 --> 00:58:00,200
Speaker 1: our our data set. UM Yeah, age wise, UM, we

849
00:58:00,200 --> 00:58:04,800
Speaker 1: don't know. Because we remove every snake that's found and

850
00:58:04,840 --> 00:58:09,800
Speaker 1: euthanize it. We don't have individuals that are followed over

851
00:58:09,880 --> 00:58:12,080
Speaker 1: multiple years. So we get a good idea of Asian

852
00:58:12,120 --> 00:58:14,480
Speaker 1: survival things like that. You know, if you've got a

853
00:58:14,480 --> 00:58:17,760
Speaker 1: fifteen foot snake, I'd be surprised if it's less than

854
00:58:18,760 --> 00:58:22,240
Speaker 1: eight or ten years old. And then how much ground

855
00:58:22,280 --> 00:58:28,439
Speaker 1: with one of these snakes covered surprising amounts UM. So

856
00:58:28,640 --> 00:58:32,040
Speaker 1: back in the early days, when people were just starting

857
00:58:32,040 --> 00:58:36,560
Speaker 1: to do some telemetry work, UM, they decided to put

858
00:58:36,640 --> 00:58:39,080
Speaker 1: radios in some pythons, but they wanted to have it

859
00:58:39,200 --> 00:58:41,640
Speaker 1: in a limited area so that they could track every

860
00:58:41,640 --> 00:58:45,200
Speaker 1: snake every day, And so they took snakes from other

861
00:58:45,280 --> 00:58:48,320
Speaker 1: places and brought them into an area east of every

862
00:58:48,320 --> 00:58:51,720
Speaker 1: Glass National Park UM, and the snakes hung out there

863
00:58:52,240 --> 00:58:55,160
Speaker 1: for most of the dry season, had home ranges of

864
00:58:55,400 --> 00:58:59,760
Speaker 1: you know, five to twenty acres, so not a huge amount.

865
00:58:59,800 --> 00:59:04,200
Speaker 1: But then when the wet season came and everything flooded,

866
00:59:05,200 --> 00:59:08,080
Speaker 1: a number of those snakes went back to their original

867
00:59:08,240 --> 00:59:13,360
Speaker 1: capture locations and sometimes to within a couple hundred yards

868
00:59:13,640 --> 00:59:17,960
Speaker 1: distance of how much over twenty miles no way, yep.

869
00:59:18,040 --> 00:59:25,400
Speaker 1: So they were navigating back to an area that you know,

870
00:59:25,520 --> 00:59:28,520
Speaker 1: they've been driven in a long circuitous route from one

871
00:59:28,520 --> 00:59:33,440
Speaker 1: spot to the other. UM. But they navigated not quite

872
00:59:33,520 --> 00:59:38,160
Speaker 1: straight line, but pretty close back to capture locations and

873
00:59:38,160 --> 00:59:41,120
Speaker 1: then landed within a couple hundred yards where they came from.

874
00:59:41,200 --> 00:59:45,000
Speaker 1: Yep ye, many activity they landed where they came from.

875
00:59:46,280 --> 00:59:51,440
Speaker 1: They somehow knew where home was and got back to it. Wow.

876
00:59:52,480 --> 00:59:55,720
Speaker 1: Years ago, I was talking to a buddy mine. He's

877
00:59:55,760 --> 00:59:57,520
Speaker 1: not a snake guy. He's a biologis been, not a

878
00:59:57,560 --> 01:00:02,120
Speaker 1: snake guy. And he had had proximity to or participated

879
01:00:02,160 --> 01:00:09,000
Speaker 1: in some research where they were testing the limits of

880
01:00:09,800 --> 01:00:14,120
Speaker 1: python expansion and he was saying that there's sort of

881
01:00:14,160 --> 01:00:20,120
Speaker 1: a line, um, an invisible line north of which it

882
01:00:20,200 --> 01:00:24,640
Speaker 1: just becomes not suitable for them. What is that line

883
01:00:24,840 --> 01:00:29,000
Speaker 1: like like in in are Do we have them just

884
01:00:29,080 --> 01:00:32,200
Speaker 1: like where we can have them and that's it? Or

885
01:00:32,280 --> 01:00:37,520
Speaker 1: are there expansion potentials for these things? It's a good question.

886
01:00:37,680 --> 01:00:41,880
Speaker 1: I think it's not well answered yet. You know, um

887
01:00:42,160 --> 01:00:45,080
Speaker 1: our research group produced the very first climate matching study

888
01:00:45,120 --> 01:00:49,200
Speaker 1: for pythons, and that was based on native range records.

889
01:00:49,800 --> 01:00:53,240
Speaker 1: Um In hindsight, we may have been a little bit

890
01:00:53,280 --> 01:00:56,600
Speaker 1: too credible in accepting some of those records because that

891
01:00:56,680 --> 01:01:00,200
Speaker 1: produced a pretty large match to the southeast us US.

892
01:01:01,280 --> 01:01:04,000
Speaker 1: Another group then put out a paper showing that no

893
01:01:04,360 --> 01:01:08,720
Speaker 1: based on this modeling approach. They're limited to extreme South

894
01:01:08,760 --> 01:01:13,720
Speaker 1: Florida and only the area that is currently occupied. We

895
01:01:13,800 --> 01:01:17,920
Speaker 1: looked at that found him, found an error, corrected that error,

896
01:01:18,000 --> 01:01:21,200
Speaker 1: and that then their method showed all of Florida. I

897
01:01:21,200 --> 01:01:23,200
Speaker 1: can I tell you what his what his thing was,

898
01:01:23,240 --> 01:01:25,800
Speaker 1: because I'm sure you know about it. I think they

899
01:01:25,800 --> 01:01:31,080
Speaker 1: were actually taking and building these little enclosures, Yeah, and

900
01:01:31,200 --> 01:01:32,840
Speaker 1: just sticking them there and see if they could survive

901
01:01:32,880 --> 01:01:35,440
Speaker 1: the winn or not. You know, this was a long

902
01:01:35,520 --> 01:01:37,800
Speaker 1: time ago. And again this wasn't like his work. You're

903
01:01:37,800 --> 01:01:41,959
Speaker 1: not gonna hurt his feelings. Right, Um, that's been done

904
01:01:42,320 --> 01:01:46,560
Speaker 1: at several locations. Um. One of them was up in uh,

905
01:01:46,800 --> 01:01:50,640
Speaker 1: South Carolina, and all this one he's talking about, Yeah,

906
01:01:51,080 --> 01:01:54,960
Speaker 1: all those snakes died. That was during that enormous cold

907
01:01:55,160 --> 01:01:58,560
Speaker 1: snap of when we had ice even in every Glades

908
01:01:58,640 --> 01:02:03,880
Speaker 1: National Park. Um. But yeah, those snakes died, and I

909
01:02:03,920 --> 01:02:07,160
Speaker 1: would think that that area is almost certainly not suitable.

910
01:02:08,040 --> 01:02:12,080
Speaker 1: The expansion is really slow. It looks like it's always

911
01:02:12,120 --> 01:02:18,400
Speaker 1: been slow. We definitely have snakes farther north, towards places

912
01:02:18,640 --> 01:02:24,120
Speaker 1: um like Lasahatchie National Wildlife Refuge where we didn't have records.

913
01:02:24,680 --> 01:02:28,280
Speaker 1: A few years ago. But still that's only in the

914
01:02:28,440 --> 01:02:32,240
Speaker 1: you know, tens of kilometers north of the National Park,

915
01:02:32,960 --> 01:02:38,200
Speaker 1: so you know, my hunch is they're not going to

916
01:02:38,320 --> 01:02:43,360
Speaker 1: get too much farther north. Um. But there was a

917
01:02:43,440 --> 01:02:48,800
Speaker 1: really cool study with tissue samples from pythons that were

918
01:02:48,840 --> 01:02:52,600
Speaker 1: taken um starting in the early two thousand's in Florida

919
01:02:52,840 --> 01:02:56,600
Speaker 1: and going through that cold snap and afterwards, and they

920
01:02:56,680 --> 01:03:03,720
Speaker 1: found molecular evidence of adaptation in gans that are controlling

921
01:03:03,800 --> 01:03:08,080
Speaker 1: things like response to temperature. And so the snakes appear

922
01:03:08,160 --> 01:03:10,880
Speaker 1: to have gone through a cold snap and there were

923
01:03:10,880 --> 01:03:13,080
Speaker 1: a lot of snakes that died during that period, and

924
01:03:13,120 --> 01:03:15,720
Speaker 1: there may have been a selection event for snakes that

925
01:03:15,960 --> 01:03:21,920
Speaker 1: have a better ability to tolerate cold temperatures. The the

926
01:03:21,960 --> 01:03:24,080
Speaker 1: scale of that, we don't know. Does that mean that

927
01:03:24,120 --> 01:03:28,880
Speaker 1: they're you know, one degree better? Um, I'm not really sure.

928
01:03:30,000 --> 01:03:35,240
Speaker 1: Speaking of the temperature adjustment, I was reading I think

929
01:03:35,280 --> 01:03:36,920
Speaker 1: it was in one of the papers that you shared

930
01:03:36,960 --> 01:03:40,920
Speaker 1: with us, about how the female will increase your body

931
01:03:40,960 --> 01:03:45,160
Speaker 1: temperature eleven and fourteen degrees to regulate her nest. Can

932
01:03:45,200 --> 01:03:48,680
Speaker 1: you talk a little bit about that. Yeah, So there's

933
01:03:48,720 --> 01:03:54,120
Speaker 1: a few species of pythons that engage in shivering thermiogenesis.

934
01:03:54,880 --> 01:03:57,480
Speaker 1: So you know, when you get cold, you shiver, and

935
01:03:57,520 --> 01:04:02,400
Speaker 1: that's because you are um shivering. It's basically a mechanical

936
01:04:02,440 --> 01:04:04,840
Speaker 1: way of increasing the temperature of those muscles that they

937
01:04:04,880 --> 01:04:08,680
Speaker 1: work better. And snakes that are coiled around eggs go

938
01:04:08,840 --> 01:04:13,440
Speaker 1: through these sequencing sequences of shivering and that raises their

939
01:04:13,480 --> 01:04:16,800
Speaker 1: body temperature. They're coiled around the whole pile of eggs,

940
01:04:16,840 --> 01:04:19,880
Speaker 1: that raises the egg body temperature or the egg temperature

941
01:04:19,880 --> 01:04:23,280
Speaker 1: as well, and so that allows them to maintain the

942
01:04:23,320 --> 01:04:26,880
Speaker 1: egg temperature in the range that's best for development. You

943
01:04:26,880 --> 01:04:30,600
Speaker 1: know how you can control like with snap. I know

944
01:04:30,640 --> 01:04:34,160
Speaker 1: this is true with snap and turtles that you can

945
01:04:34,200 --> 01:04:39,680
Speaker 1: control the sex of the turtle by the soil temp

946
01:04:39,960 --> 01:04:42,920
Speaker 1: And it goes in bands, right, it's not like hot

947
01:04:42,960 --> 01:04:46,120
Speaker 1: as male, cold as female. But there's like a band

948
01:04:46,160 --> 01:04:51,640
Speaker 1: of temperature, a temperature band at which you'll get predominantly males,

949
01:04:52,400 --> 01:04:55,000
Speaker 1: and then there's a band of temperature higher than that

950
01:04:55,000 --> 01:04:57,120
Speaker 1: which you'll get predominantly females. But then it could be

951
01:04:57,120 --> 01:05:00,640
Speaker 1: a next band of temperature band they would go back

952
01:05:00,680 --> 01:05:04,640
Speaker 1: to making males. Do they do that? Is that part

953
01:05:04,640 --> 01:05:08,520
Speaker 1: of the is that part of the regulating nest temperature

954
01:05:08,560 --> 01:05:12,440
Speaker 1: or is it just the the need to keep the

955
01:05:12,480 --> 01:05:15,479
Speaker 1: eggs warm so they don't die hear your cold snap? Yeah?

956
01:05:15,520 --> 01:05:20,840
Speaker 1: That that temperature dependent sex determination is typical of UM

957
01:05:20,880 --> 01:05:24,960
Speaker 1: A lot of reptiles, but not the giant snakes, so

958
01:05:25,360 --> 01:05:30,520
Speaker 1: they have straight genetic sex determination. UM. The wrinkle with

959
01:05:30,920 --> 01:05:34,880
Speaker 1: Burmese pythons and several other large pythons and antacondas and

960
01:05:34,880 --> 01:05:40,160
Speaker 1: boas is that they can also be parthenogens. So there

961
01:05:40,200 --> 01:05:44,439
Speaker 1: are records of several of these species producing young with

962
01:05:45,200 --> 01:05:49,000
Speaker 1: no contact with a male m hm, and so that

963
01:05:49,200 --> 01:05:52,880
Speaker 1: that's problematic. You know, as an invasive species biologist, you know,

964
01:05:53,000 --> 01:05:56,040
Speaker 1: we we worry about things like propagule pressure. You know

965
01:05:56,080 --> 01:06:00,480
Speaker 1: that that's the number of potential invasion organisms that are

966
01:06:00,480 --> 01:06:03,960
Speaker 1: reaching a certain in an area, because the more there are,

967
01:06:04,000 --> 01:06:06,320
Speaker 1: the more likely they are to find each other and breed.

968
01:06:07,240 --> 01:06:09,960
Speaker 1: If you have an animal that is capable of being

969
01:06:09,960 --> 01:06:13,160
Speaker 1: a parthenogen, then you could have a population started by

970
01:06:13,200 --> 01:06:16,320
Speaker 1: one female. And that's that's a lot more worrisome to

971
01:06:16,400 --> 01:06:19,880
Speaker 1: me as someone who thinks about this stuff. How are you, like,

972
01:06:19,880 --> 01:06:24,360
Speaker 1: how is that possible? Uh? You know, I mean part

973
01:06:24,400 --> 01:06:30,040
Speaker 1: of genesis, um it you basically you have a hiccup

974
01:06:30,760 --> 01:06:37,280
Speaker 1: in terms of during myosis. You know, during myosis, which

975
01:06:37,280 --> 01:06:42,360
Speaker 1: is the process of making sex cells like sperm, you're

976
01:06:42,360 --> 01:06:46,160
Speaker 1: taking the two copies of DNA, splitting them apart, and

977
01:06:46,600 --> 01:06:49,680
Speaker 1: each sex cell only has one copy, so you sperm

978
01:06:49,760 --> 01:06:54,840
Speaker 1: only has one half of your DNA. But if that

979
01:06:55,120 --> 01:06:58,440
Speaker 1: process has some hiccup in that in it, then you

980
01:06:58,480 --> 01:07:01,600
Speaker 1: can end up with both bees in a sex cell,

981
01:07:02,120 --> 01:07:07,360
Speaker 1: which means that that organism can develop. Yeah, but how

982
01:07:07,400 --> 01:07:11,520
Speaker 1: does it mate with itself? Um? It doesn't. It's it's

983
01:07:11,520 --> 01:07:14,760
Speaker 1: all females that do it. And so it just means

984
01:07:14,840 --> 01:07:20,920
Speaker 1: that the um like, how does this it's producing a sperm, Well,

985
01:07:20,960 --> 01:07:24,120
Speaker 1: the female is not. But so the female has got

986
01:07:24,160 --> 01:07:29,080
Speaker 1: a follicle. Yeah, and so instead of producing a follicle

987
01:07:29,200 --> 01:07:34,680
Speaker 1: that's got um half of the DNA during that biotic process,

988
01:07:34,800 --> 01:07:37,360
Speaker 1: all of it ends up in one half, and so

989
01:07:37,480 --> 01:07:41,040
Speaker 1: that follicle now has both copies of DNA. Oh, I

990
01:07:41,080 --> 01:07:44,160
Speaker 1: got you? Is that a less fit creature because it

991
01:07:44,200 --> 01:07:50,120
Speaker 1: has less genetic diversity going into it, probably because it's

992
01:07:50,120 --> 01:07:56,640
Speaker 1: a clone and we don't know much about it because

993
01:07:57,160 --> 01:08:02,280
Speaker 1: oftentimes it's been reported in captive snakes and we don't

994
01:08:02,280 --> 01:08:05,720
Speaker 1: know how often it happens in wild snakes because we

995
01:08:05,760 --> 01:08:11,200
Speaker 1: don't we don't genetically sample every individual python that comes out, um,

996
01:08:11,200 --> 01:08:22,479
Speaker 1: just because that would get cost prohibitive. Are there are

997
01:08:22,479 --> 01:08:27,920
Speaker 1: other species that that happens in? Um? Yeah, I mean

998
01:08:28,040 --> 01:08:32,760
Speaker 1: it's it's pretty widespread across the animal kingdom altogether, you know.

999
01:08:32,880 --> 01:08:38,120
Speaker 1: But in snakes, it's known from a number of the

1000
01:08:38,160 --> 01:08:41,320
Speaker 1: primitive snakes like uh, some of the boas, some of

1001
01:08:41,320 --> 01:08:45,639
Speaker 1: the pythons. But it's also known from um, some more

1002
01:08:45,680 --> 01:08:51,160
Speaker 1: advanced snakes. Um, you know, some of the colubrid snakes

1003
01:08:51,200 --> 01:08:54,640
Speaker 1: that that's uh, most of the snakes were familiar with

1004
01:08:54,680 --> 01:08:58,080
Speaker 1: in the in the continental US, you know, water snakes,

1005
01:08:58,120 --> 01:09:04,560
Speaker 1: garter snakes, king snakes, things like that. Um. So it's uncommon,

1006
01:09:05,280 --> 01:09:13,600
Speaker 1: but probably more widespread then we know. Ah. Can you

1007
01:09:13,640 --> 01:09:19,560
Speaker 1: tell everybody some of the stories about using using judas,

1008
01:09:20,840 --> 01:09:26,719
Speaker 1: like Judas from the Bible, using judas snakes to catch snakes. Yeah,

1009
01:09:26,760 --> 01:09:32,400
Speaker 1: you know, it's really interesting because so when you have

1010
01:09:32,720 --> 01:09:36,120
Speaker 1: a male python and you put a radio transmitter in

1011
01:09:36,160 --> 01:09:39,639
Speaker 1: it and release it during the breeding season, that male

1012
01:09:39,800 --> 01:09:42,920
Speaker 1: will engage in mate searching behaviors. It'll go and try

1013
01:09:42,920 --> 01:09:46,719
Speaker 1: to find females and in Burmese pythons, you have breeding

1014
01:09:46,800 --> 01:09:50,519
Speaker 1: aggregations of a large female and then several males that

1015
01:09:50,560 --> 01:09:52,840
Speaker 1: are all around it, all vying to mate with her,

1016
01:09:53,439 --> 01:09:58,760
Speaker 1: and those those can persist for over a month sometimes um.

1017
01:09:58,800 --> 01:10:02,280
Speaker 1: And so if you then follow your radio tag mail,

1018
01:10:02,680 --> 01:10:04,920
Speaker 1: it might lead you to a breeding aggregation. You take

1019
01:10:04,960 --> 01:10:08,240
Speaker 1: all those snakes out, let your mail go again, it's

1020
01:10:08,240 --> 01:10:10,960
Speaker 1: going to go search for another one. And so it's

1021
01:10:11,000 --> 01:10:16,000
Speaker 1: potentially a method of increasing the removal rate of your

1022
01:10:16,000 --> 01:10:19,960
Speaker 1: pythons without putting in a whole lot more search effort,

1023
01:10:19,960 --> 01:10:24,000
Speaker 1: because all you gotta do is check where your mail is,

1024
01:10:24,640 --> 01:10:26,479
Speaker 1: say once a week, and see if it's found a

1025
01:10:26,479 --> 01:10:31,840
Speaker 1: female yet. Um. As far as that term, it's really

1026
01:10:31,880 --> 01:10:35,920
Speaker 1: interesting because we had pushback recently from folks who said

1027
01:10:35,960 --> 01:10:42,000
Speaker 1: that the term Judas snake is anti Semitic, and it

1028
01:10:42,080 --> 01:10:44,000
Speaker 1: is a term I've heard and wildlife bothers you for

1029
01:10:44,040 --> 01:10:46,599
Speaker 1: a year and for years, and I've never thought about it,

1030
01:10:47,040 --> 01:10:51,240
Speaker 1: but I actually went back and started looking and historically

1031
01:10:51,920 --> 01:10:54,160
Speaker 1: there's a lot of support for that notion. And so

1032
01:10:54,400 --> 01:10:58,240
Speaker 1: just recently we had a we had a pole among

1033
01:10:58,360 --> 01:11:01,519
Speaker 1: a whole bunch of snake people. What terms shall we use?

1034
01:11:01,640 --> 01:11:05,080
Speaker 1: We gave him all these options. And so because because Judas,

1035
01:11:05,280 --> 01:11:09,080
Speaker 1: Judas betrayed Christ, but but but Christ, but but Christ

1036
01:11:09,160 --> 01:11:12,840
Speaker 1: was a Jew. Yeah, but I guess it's been used

1037
01:11:13,439 --> 01:11:18,840
Speaker 1: um as a pejorative um like betray like someone who

1038
01:11:19,040 --> 01:11:22,720
Speaker 1: betray a Christian. As of as of last month, we

1039
01:11:22,800 --> 01:11:27,519
Speaker 1: now have a scout snake project and uh so anyway

1040
01:11:30,080 --> 01:11:32,920
Speaker 1: it can work. How many how many have you ever?

1041
01:11:33,000 --> 01:11:38,360
Speaker 1: How many have you ever uncovered using this strategy? Uh? Boy?

1042
01:11:38,360 --> 01:11:43,320
Speaker 1: I think the biggest aggregation might still be eight that

1043
01:11:43,439 --> 01:11:46,799
Speaker 1: I know of, So that'd be like six other males

1044
01:11:46,800 --> 01:11:53,080
Speaker 1: and one female yep yep um. In one of those

1045
01:11:53,120 --> 01:11:58,879
Speaker 1: there was there was one aggregation that was six males

1046
01:11:59,800 --> 01:12:04,479
Speaker 1: and one ft female and all of them were in

1047
01:12:04,479 --> 01:12:08,879
Speaker 1: a single gopher tortoise burrow. Uh and they were jammed

1048
01:12:08,920 --> 01:12:12,240
Speaker 1: in there like a tent in a stuff sack man.

1049
01:12:12,360 --> 01:12:17,360
Speaker 1: I mean, there there were so many snakes and I

1050
01:12:17,400 --> 01:12:20,799
Speaker 1: can't imagine that they could have pulled off a breeding event,

1051
01:12:20,960 --> 01:12:25,160
Speaker 1: you know. Um. And then after after pulling all these

1052
01:12:25,160 --> 01:12:27,840
Speaker 1: snakes out in the very back of the borough, there

1053
01:12:27,880 --> 01:12:31,679
Speaker 1: was this poor gopher tortoise who had been stuck there

1054
01:12:31,720 --> 01:12:35,840
Speaker 1: for god knows how long with this you know, python

1055
01:12:36,080 --> 01:12:38,519
Speaker 1: orgy going on right in front unless he's some kind

1056
01:12:38,560 --> 01:12:42,080
Speaker 1: of pervy voyeur who liked the whole thing. Yeah. I mean,

1057
01:12:42,280 --> 01:12:45,080
Speaker 1: you know, Tortoise is probably forty years old. I guaranteed

1058
01:12:45,240 --> 01:12:48,400
Speaker 1: never seen anything like that before. When he goes to

1059
01:12:48,439 --> 01:12:53,599
Speaker 1: tell his buddies about it, they're gonna be like, no way. Yeah,

1060
01:12:54,040 --> 01:12:56,840
Speaker 1: the ask your questions Johnny about the pipe. These are

1061
01:12:56,840 --> 01:12:59,960
Speaker 1: good questions about the python hunters. Yeah, back to the

1062
01:13:00,080 --> 01:13:02,400
Speaker 1: PI python hunters, And I think this can lead into

1063
01:13:02,560 --> 01:13:04,360
Speaker 1: like what are going to be like the ways to

1064
01:13:04,400 --> 01:13:07,520
Speaker 1: actually get rid of some of them? But the python hunters,

1065
01:13:07,560 --> 01:13:10,200
Speaker 1: how do they do their thing? And then can you

1066
01:13:10,240 --> 01:13:12,920
Speaker 1: talk about like what they're actually paid? Like is this

1067
01:13:13,080 --> 01:13:15,120
Speaker 1: something that they make a living at? Is it just

1068
01:13:15,200 --> 01:13:20,719
Speaker 1: a hobby? Yeah? Um, I don't know all the details

1069
01:13:20,720 --> 01:13:23,320
Speaker 1: of it because I'm only you know, on the outskirts

1070
01:13:23,320 --> 01:13:27,680
Speaker 1: of it. I think mostly they're getting a minimum wage

1071
01:13:28,080 --> 01:13:33,160
Speaker 1: plus a certain amount of money per python, plus a

1072
01:13:33,200 --> 01:13:37,120
Speaker 1: certain amount of money per foot, So they get paid

1073
01:13:37,120 --> 01:13:41,400
Speaker 1: for snakes. But it's also scaled by size um. And

1074
01:13:42,479 --> 01:13:44,880
Speaker 1: most of them are going by vehicle. A lot of

1075
01:13:44,920 --> 01:13:47,360
Speaker 1: that is at night, and they're using spotlights. Some of

1076
01:13:47,360 --> 01:13:50,320
Speaker 1: them have towers on the back of their trucks and

1077
01:13:50,479 --> 01:13:57,439
Speaker 1: they are cruising levies primarily. And you know, we they've

1078
01:13:57,439 --> 01:13:59,960
Speaker 1: actually taught us a fair amount about searching for snakes

1079
01:14:00,080 --> 01:14:03,280
Speaker 1: because we used to mostly drive levies in the daytime

1080
01:14:03,560 --> 01:14:05,559
Speaker 1: and look for snakes that are out basking. That still

1081
01:14:05,640 --> 01:14:10,680
Speaker 1: works sometimes, um, but they're finding a lot of their

1082
01:14:10,680 --> 01:14:14,360
Speaker 1: snakes right on the water's edge in ambush positions. But

1083
01:14:14,400 --> 01:14:17,640
Speaker 1: the bodies are in the water and so there are

1084
01:14:17,640 --> 01:14:20,000
Speaker 1: a lot harder to see that way unless you've got

1085
01:14:20,000 --> 01:14:24,160
Speaker 1: a little elevation. Um. But I mean if you you know,

1086
01:14:24,200 --> 01:14:27,160
Speaker 1: you look around online and there's there's uh, there's a

1087
01:14:27,160 --> 01:14:31,120
Speaker 1: lot of coverage of of the python hunting that's going on,

1088
01:14:31,600 --> 01:14:37,640
Speaker 1: and they the media, Yeah, they love that story. And

1089
01:14:37,960 --> 01:14:40,280
Speaker 1: like I said, I mean I only know a few

1090
01:14:40,280 --> 01:14:42,800
Speaker 1: of them personally, but they're all great folks, you know,

1091
01:14:42,840 --> 01:14:47,759
Speaker 1: and they deeply care about the everglaze ecosystem. Now today

1092
01:14:47,760 --> 01:14:50,240
Speaker 1: when they see one, say you see a ten footer

1093
01:14:50,439 --> 01:14:53,000
Speaker 1: and only it's six inch head is sticking out of

1094
01:14:53,040 --> 01:14:56,639
Speaker 1: the water. Did they shoot it? Do they put last

1095
01:14:56,680 --> 01:14:59,000
Speaker 1: all around it? Like, how do you get it? It's

1096
01:14:59,000 --> 01:15:05,280
Speaker 1: almost all handcapps. So um, when most of the time

1097
01:15:05,680 --> 01:15:09,200
Speaker 1: if a snake sees something big and scary like us approaching,

1098
01:15:09,640 --> 01:15:12,520
Speaker 1: it's gonna just freeze because it knows it's well camouflaged

1099
01:15:13,120 --> 01:15:18,439
Speaker 1: and so probably I don't know the time. You can

1100
01:15:19,479 --> 01:15:21,960
Speaker 1: walk up and just grab it behind the head real quick,

1101
01:15:22,720 --> 01:15:27,639
Speaker 1: pull it out of the water, and um, figure out

1102
01:15:27,640 --> 01:15:30,080
Speaker 1: how to control it and get into a bag. Sometimes

1103
01:15:30,120 --> 01:15:32,479
Speaker 1: as you're approaching, they'll turn around and start moving off,

1104
01:15:32,560 --> 01:15:34,760
Speaker 1: and then you grab the tail and pull it out

1105
01:15:34,800 --> 01:15:37,719
Speaker 1: that way. Um. When you've got it by the tail,

1106
01:15:37,960 --> 01:15:41,719
Speaker 1: it's gonna be trying to turn around on you and strike.

1107
01:15:42,400 --> 01:15:45,360
Speaker 1: But if you jerk the tail real hard every time

1108
01:15:45,360 --> 01:15:49,439
Speaker 1: it strikes, basically you'll you'll throw it off. Um, and

1109
01:15:49,479 --> 01:15:54,439
Speaker 1: then they tire out fairly quickly, or at least they

1110
01:15:54,479 --> 01:15:57,200
Speaker 1: calm down fairly quickly, and then you can work your

1111
01:15:57,200 --> 01:15:59,680
Speaker 1: way up to the head and get into bag. Why

1112
01:15:59,680 --> 01:16:01,640
Speaker 1: don't they when when the guys are going after the

1113
01:16:01,680 --> 01:16:03,800
Speaker 1: python hunted, why don't they just run up and chop

1114
01:16:03,840 --> 01:16:07,800
Speaker 1: his head off? Um? There are some animals that are

1115
01:16:07,880 --> 01:16:12,040
Speaker 1: killed by with firearms, Um, chop his head off or

1116
01:16:12,080 --> 01:16:17,320
Speaker 1: something us way harder than you'd think. Um. Yeah, so,

1117
01:16:17,960 --> 01:16:20,080
Speaker 1: especially for the ones that are in the water. But

1118
01:16:22,040 --> 01:16:27,519
Speaker 1: there they're pretty dang muscular. Um. And also the you know,

1119
01:16:27,680 --> 01:16:33,280
Speaker 1: decapitation alone is not considered you know, the acceptable youth

1120
01:16:33,360 --> 01:16:36,280
Speaker 1: in Asia because you have to then destroy the brain

1121
01:16:36,439 --> 01:16:39,799
Speaker 1: right afterwards. So, um, you can do that pretty easily

1122
01:16:39,840 --> 01:16:43,479
Speaker 1: if you just you know, destroy the brain tissue after

1123
01:16:43,520 --> 01:16:46,679
Speaker 1: the heads off. But so if you if you walk

1124
01:16:46,680 --> 01:16:48,680
Speaker 1: out in your yard there's one laying there, what is

1125
01:16:48,720 --> 01:16:56,240
Speaker 1: the best practice to go kill it? Uh? Boy, start

1126
01:16:56,240 --> 01:16:59,719
Speaker 1: getting into the what what should you do? Questions? Um?

1127
01:17:00,120 --> 01:17:06,960
Speaker 1: Never mind? No, I mean I think that probably the

1128
01:17:07,000 --> 01:17:10,559
Speaker 1: best possible thing is to do the same thing as

1129
01:17:10,600 --> 01:17:13,280
Speaker 1: with a rattlesnake, which is just turned around, go back

1130
01:17:13,280 --> 01:17:16,680
Speaker 1: inside and call call animal control or call your game

1131
01:17:16,720 --> 01:17:27,800
Speaker 1: inficial agency. Um. That's you know that that minimizes minimizes risk, Steve.

1132
01:17:29,840 --> 01:17:33,160
Speaker 1: But people, if if you if you shoot a snake

1133
01:17:33,160 --> 01:17:36,360
Speaker 1: in the head, it's going to be dead. But any

1134
01:17:36,400 --> 01:17:40,559
Speaker 1: snake over about seven ft, I would not recommend that

1135
01:17:40,640 --> 01:17:44,679
Speaker 1: someone inexperienced try to catch it by themselves. And that's

1136
01:17:44,720 --> 01:17:47,200
Speaker 1: because you know, a seven snake seven foot snake might

1137
01:17:47,200 --> 01:17:52,479
Speaker 1: only be pounds. But if that snake somehow manages to

1138
01:17:52,520 --> 01:17:56,760
Speaker 1: get a wrap around your neck, you're probably toast you'd

1139
01:17:56,800 --> 01:17:58,320
Speaker 1: be the first guy to get killed by a snake

1140
01:17:58,360 --> 01:18:02,360
Speaker 1: in Florida, by a Burmese parthon Florida. Yep. So what

1141
01:18:02,520 --> 01:18:06,600
Speaker 1: will what will end up? Crystal ball? Right? Crystal ball situation.

1142
01:18:07,800 --> 01:18:12,240
Speaker 1: I'm sure we can all imagine the crystal ball scenario

1143
01:18:12,360 --> 01:18:19,360
Speaker 1: where they kill everything off. There's a greatly reduced food base.

1144
01:18:20,920 --> 01:18:23,680
Speaker 1: You see a reduction in pythons, but they never go

1145
01:18:23,840 --> 01:18:28,760
Speaker 1: all the way away because as they starve off, you know,

1146
01:18:28,880 --> 01:18:31,840
Speaker 1: they're popular prey, population rebounds a little bit and they

1147
01:18:31,880 --> 01:18:35,600
Speaker 1: just kind of hit some equal librium. That's kind of

1148
01:18:36,160 --> 01:18:42,000
Speaker 1: shitty for animals, but it's an equilibrium. Um, what's a

1149
01:18:42,040 --> 01:18:49,360
Speaker 1: better crystal ball scenario? Um? I think in the absence

1150
01:18:49,400 --> 01:18:55,040
Speaker 1: of some silver bullet intervention, you you pretty much outlined it. Um.

1151
01:18:56,120 --> 01:18:59,400
Speaker 1: The main thing to remember about snakes is that they're

1152
01:18:59,479 --> 01:19:04,879
Speaker 1: incredib ofly low energy organisms. So a snake can persist

1153
01:19:05,000 --> 01:19:07,639
Speaker 1: in the environment and and actually a lot of snakes

1154
01:19:07,760 --> 01:19:11,160
Speaker 1: can persist in the environment in a given area even

1155
01:19:11,200 --> 01:19:14,360
Speaker 1: if they don't have that much prey, because they only

1156
01:19:14,400 --> 01:19:17,439
Speaker 1: need a very small number of calories per year to

1157
01:19:17,560 --> 01:19:21,799
Speaker 1: keep them going as cold blooded organisms, so they're really efficient,

1158
01:19:22,240 --> 01:19:25,000
Speaker 1: and so that that whole. You know, the the hair

1159
01:19:25,160 --> 01:19:28,720
Speaker 1: and links cycles that we remember from our biology classes.

1160
01:19:29,840 --> 01:19:34,000
Speaker 1: You know, when the rabbits tank, the links tank even harder,

1161
01:19:34,360 --> 01:19:37,519
Speaker 1: but with a because they feel it immediately. Yeah, with

1162
01:19:37,560 --> 01:19:40,680
Speaker 1: a python, if the prey tanks, the snakes don't go

1163
01:19:40,760 --> 01:19:43,920
Speaker 1: down nearly as far. So it's kind of like having

1164
01:19:43,960 --> 01:19:47,200
Speaker 1: this pathogen that's just hanging out the environment waiting for

1165
01:19:47,240 --> 01:19:50,400
Speaker 1: the conditions to get better, and they can respond really

1166
01:19:50,479 --> 01:19:55,680
Speaker 1: fast when those conditions do get better. So I think, yeah,

1167
01:19:55,840 --> 01:20:02,519
Speaker 1: we don't have a rosy future in terms of those

1168
01:20:02,560 --> 01:20:06,400
Speaker 1: mammals somehow coming back unless we get some sort of

1169
01:20:06,439 --> 01:20:09,280
Speaker 1: silver bullet. And so that's that's the next thing that

1170
01:20:09,360 --> 01:20:14,479
Speaker 1: people are thinking about is all these synthetic biology questions.

1171
01:20:15,120 --> 01:20:19,479
Speaker 1: So can we manipulate genomes in a way that drives

1172
01:20:19,479 --> 01:20:22,040
Speaker 1: the animals extinct? And I don't know if you previously

1173
01:20:22,040 --> 01:20:26,479
Speaker 1: talked about things like crisper or RNA interference or things

1174
01:20:26,520 --> 01:20:30,320
Speaker 1: like that. We have not on this show, but um,

1175
01:20:30,400 --> 01:20:35,520
Speaker 1: well no, I don't think we have like introducing introducing

1176
01:20:36,840 --> 01:20:40,760
Speaker 1: genetically manipulated animals into the environment in order to enter

1177
01:20:40,840 --> 01:20:46,479
Speaker 1: the population and have a long term impact on the population. Yeah,

1178
01:20:46,560 --> 01:20:49,679
Speaker 1: So some some people are familiar with the term gene drive,

1179
01:20:50,640 --> 01:20:56,320
Speaker 1: and in these in these tools, regardless of whether it's

1180
01:20:56,320 --> 01:21:01,040
Speaker 1: the crisper or the RNA interference, what you're trying to

1181
01:21:01,120 --> 01:21:07,160
Speaker 1: do is get one allele in every single organism, and

1182
01:21:07,200 --> 01:21:10,080
Speaker 1: it's the allele that you've manipulated. So, you know, going

1183
01:21:10,120 --> 01:21:13,479
Speaker 1: back to what we talked about earlier, your parents have

1184
01:21:13,600 --> 01:21:17,360
Speaker 1: two copies in their DNA. You get one from each parent.

1185
01:21:19,280 --> 01:21:22,080
Speaker 1: In a gene drive, what we're trying to do is

1186
01:21:23,960 --> 01:21:27,880
Speaker 1: make sure that only one allele has passed on, and

1187
01:21:27,920 --> 01:21:30,280
Speaker 1: we wanted to be the one that we've messed with.

1188
01:21:31,240 --> 01:21:36,840
Speaker 1: So in New Zealand, for example, they're working on daughterless mice,

1189
01:21:39,000 --> 01:21:44,160
Speaker 1: so that you insert a gene in in a male mouse.

1190
01:21:44,720 --> 01:21:47,880
Speaker 1: When it mates with the female, it knocks out the

1191
01:21:47,920 --> 01:21:53,200
Speaker 1: ability to produce female offspring, and so only males are produced.

1192
01:21:53,760 --> 01:21:59,040
Speaker 1: It's like it's like a bar and anchorage man yeah yeah,

1193
01:21:59,160 --> 01:22:03,519
Speaker 1: or guam um. And then all those males have that

1194
01:22:03,560 --> 01:22:06,200
Speaker 1: gene two, and so every female they produced with only

1195
01:22:06,200 --> 01:22:09,840
Speaker 1: produced males, and so you end up swamping the population

1196
01:22:09,880 --> 01:22:13,559
Speaker 1: with these manipulated males and eventually there's no more mice.

1197
01:22:15,520 --> 01:22:19,240
Speaker 1: That works pretty well potentially with something like a mouse

1198
01:22:19,280 --> 01:22:23,639
Speaker 1: that has really fast generation times. UM, it's largely untried

1199
01:22:23,640 --> 01:22:27,080
Speaker 1: in something like a python that has extended generational times.

1200
01:22:27,080 --> 01:22:32,000
Speaker 1: But right now we're working on a research strategy that is,

1201
01:22:32,240 --> 01:22:33,840
Speaker 1: what do we need to know in the next three

1202
01:22:33,920 --> 01:22:38,120
Speaker 1: years to be able to assess whether these tools will

1203
01:22:38,160 --> 01:22:45,080
Speaker 1: work for pythons. What about some kind of disease agent UM,

1204
01:22:45,120 --> 01:22:48,840
Speaker 1: you know, disease. I think if you look at the

1205
01:22:48,920 --> 01:22:53,479
Speaker 1: record of UM diseases introduced to Australia to control rabbits,

1206
01:22:54,479 --> 01:22:57,800
Speaker 1: you find that the initial knockdown is real hard, and

1207
01:22:57,840 --> 01:23:01,479
Speaker 1: then you're left with a resistant population, so you have

1208
01:23:01,560 --> 01:23:05,800
Speaker 1: a really strong selection gradient and the remaining animals don't

1209
01:23:05,800 --> 01:23:08,519
Speaker 1: really have to worry about it that much. UM. We

1210
01:23:08,560 --> 01:23:13,599
Speaker 1: don't know of many diseases that would hit pythons that hard. UM.

1211
01:23:13,680 --> 01:23:20,280
Speaker 1: But the a twist there is that the pythons brought

1212
01:23:20,360 --> 01:23:25,600
Speaker 1: over a penist dome parasite with them from Southeast Asia.

1213
01:23:26,000 --> 01:23:27,880
Speaker 1: We don't know the full life cycle of that thing,

1214
01:23:28,000 --> 01:23:31,320
Speaker 1: but we know that it goes probably from maybe amphibians,

1215
01:23:31,320 --> 01:23:37,000
Speaker 1: two mammals like rats, and then two pythons. And it

1216
01:23:37,080 --> 01:23:41,160
Speaker 1: turns out that native snakes are more competent hosts of

1217
01:23:41,200 --> 01:23:45,960
Speaker 1: this penistone parasites than the pythons are, and the peniston

1218
01:23:46,680 --> 01:23:50,280
Speaker 1: is now over a hundred kilometers north of the python range.

1219
01:23:51,200 --> 01:23:53,680
Speaker 1: So we've got this introduced parasite that came in with

1220
01:23:53,720 --> 01:23:57,760
Speaker 1: an invasive snake that is now infecting native snakes and

1221
01:23:57,800 --> 01:24:01,479
Speaker 1: actually having a pretty strong impact on them that may

1222
01:24:01,520 --> 01:24:05,360
Speaker 1: spread throughout the continent. So we could end up having

1223
01:24:05,360 --> 01:24:10,000
Speaker 1: this this python effect in you know, Arkansas, even though

1224
01:24:10,040 --> 01:24:15,480
Speaker 1: the pythons arement at a thousand miles Oh man huh.

1225
01:24:15,920 --> 01:24:19,320
Speaker 1: And then I know how this one always goes, but

1226
01:24:19,320 --> 01:24:22,880
Speaker 1: I gotta ask it anyway. Let's say you do like

1227
01:24:22,920 --> 01:24:26,679
Speaker 1: the old Hawaii trip where you got a rat problem,

1228
01:24:26,760 --> 01:24:31,920
Speaker 1: so you bring in some mongooses. Um, what likes to

1229
01:24:31,960 --> 01:24:37,679
Speaker 1: eat pythons? Um. The one that I get to email

1230
01:24:37,760 --> 01:24:41,160
Speaker 1: us about is king Cobra's. That that that's a solution,

1231
01:24:41,760 --> 01:24:46,559
Speaker 1: that's yeah, yeah, what you do is you get a

1232
01:24:46,560 --> 01:24:50,360
Speaker 1: big truck of King Cobra's. You sound like my father

1233
01:24:50,400 --> 01:24:55,040
Speaker 1: in law. Um, yeah, I mean that's that's that's a

1234
01:24:55,200 --> 01:24:57,920
Speaker 1: legitimate suggestion that we get. I mean, that's not the

1235
01:24:57,920 --> 01:25:03,400
Speaker 1: best control tool suggestion we get. My absolute favorite is

1236
01:25:03,400 --> 01:25:08,240
Speaker 1: the pig goat raft and the pig goat raft. Since

1237
01:25:08,280 --> 01:25:11,880
Speaker 1: the winds are mostly from the west, you make a

1238
01:25:11,880 --> 01:25:14,760
Speaker 1: whole bunch of rafts on the west end of the

1239
01:25:14,760 --> 01:25:18,920
Speaker 1: everglades during the wet season, and you tie a goat

1240
01:25:19,040 --> 01:25:21,639
Speaker 1: in the front, and then you put a small pig

1241
01:25:21,680 --> 01:25:27,160
Speaker 1: on the back, and the wind starts blowing the raft

1242
01:25:27,200 --> 01:25:31,800
Speaker 1: through the everglades and whenever, whenever it hangs up on vegetation,

1243
01:25:32,120 --> 01:25:35,120
Speaker 1: the goat eats the vegetation and clears the way so

1244
01:25:35,160 --> 01:25:38,360
Speaker 1: the raft can keep going. And then the pig is

1245
01:25:38,400 --> 01:25:41,760
Speaker 1: a lure for your pythons. And so as you move

1246
01:25:41,880 --> 01:25:45,760
Speaker 1: through when a when a snake smells, the pig's going

1247
01:25:45,840 --> 01:25:48,320
Speaker 1: to crawl up and eat the pig, and you get

1248
01:25:48,320 --> 01:25:50,280
Speaker 1: the pig tethered, and then the snake will be stuck.

1249
01:25:51,120 --> 01:25:54,519
Speaker 1: And what's wrong that? I would just I mean, wouldn't

1250
01:25:54,560 --> 01:25:59,040
Speaker 1: that be awesome? Um? I'd just like to take pictures

1251
01:25:59,080 --> 01:26:01,320
Speaker 1: of that solution. I like it. So it took the

1252
01:26:01,360 --> 01:26:04,920
Speaker 1: time to lay that out. Yes, someone really really thought

1253
01:26:04,960 --> 01:26:08,479
Speaker 1: about that. Okay, what have we not asked you that

1254
01:26:08,520 --> 01:26:17,040
Speaker 1: we should have asked you? Oh man, Um, like if

1255
01:26:17,040 --> 01:26:19,639
Speaker 1: you were thinking, if these boys had half a brain,

1256
01:26:19,680 --> 01:26:24,400
Speaker 1: they would have asked me x Well, I mean I

1257
01:26:24,439 --> 01:26:27,720
Speaker 1: feel like, you know, as an invasive species guy and

1258
01:26:27,760 --> 01:26:32,960
Speaker 1: a snake guy, UM, I should say something about the

1259
01:26:33,000 --> 01:26:38,080
Speaker 1: fact that these risks are not over. You know, we

1260
01:26:38,200 --> 01:26:42,920
Speaker 1: continually have new individuals of non native snakes showing up

1261
01:26:42,960 --> 01:26:46,240
Speaker 1: all over the country. UM. Burmese pythons are not the

1262
01:26:46,240 --> 01:26:49,080
Speaker 1: only giant snake that's established in the US. We've got

1263
01:26:49,439 --> 01:26:52,800
Speaker 1: the Northern African python, which is just as big, established

1264
01:26:52,840 --> 01:26:56,400
Speaker 1: in a small area in western Miami. UM we've got

1265
01:26:56,520 --> 01:27:00,760
Speaker 1: boa constrictors, a Central South American version of boat constructors,

1266
01:27:00,760 --> 01:27:03,800
Speaker 1: actually very similar to what you would have seen in

1267
01:27:03,120 --> 01:27:10,000
Speaker 1: u in Guyana um In Park in Miami. UM We've

1268
01:27:10,200 --> 01:27:12,080
Speaker 1: and then we've got a range of smaller snakes that

1269
01:27:12,120 --> 01:27:14,840
Speaker 1: are established too. And so you know, we keep on

1270
01:27:14,880 --> 01:27:20,439
Speaker 1: doing this to ourselves, and we really don't have very

1271
01:27:20,479 --> 01:27:26,519
Speaker 1: good mechanisms for prevention. And prevention is the most important

1272
01:27:26,560 --> 01:27:29,240
Speaker 1: part of invasive species management. If you can keep things

1273
01:27:29,280 --> 01:27:32,360
Speaker 1: from getting established in the first place, then you're gonna

1274
01:27:32,400 --> 01:27:36,639
Speaker 1: save a lot of money. But if you can't do that,

1275
01:27:36,720 --> 01:27:40,479
Speaker 1: you need early detection and rapid response, and you need

1276
01:27:40,520 --> 01:27:42,200
Speaker 1: to be able to say, hey, we found a couple

1277
01:27:42,240 --> 01:27:45,160
Speaker 1: of these, we're gonna go in with all of our resources,

1278
01:27:45,320 --> 01:27:49,679
Speaker 1: we're gonna try to knock them out. And going back

1279
01:27:49,720 --> 01:27:52,439
Speaker 1: to the detection probability, that's really hard to do for

1280
01:27:52,479 --> 01:27:55,400
Speaker 1: snakes because the chances of finding the first one or

1281
01:27:55,439 --> 01:27:58,519
Speaker 1: the second one are just not that good. And so

1282
01:28:00,120 --> 01:28:02,839
Speaker 1: what I tend to tell people, and they're not crazy

1283
01:28:02,840 --> 01:28:06,000
Speaker 1: about hearing it, is that if you find one, you

1284
01:28:06,040 --> 01:28:08,360
Speaker 1: should go and put in a moderate effort and see

1285
01:28:08,400 --> 01:28:11,559
Speaker 1: if there's more. If you find two, you should really

1286
01:28:11,600 --> 01:28:14,120
Speaker 1: go in with all guns blazing. And if you find three,

1287
01:28:14,160 --> 01:28:17,400
Speaker 1: you should assume you have a population. And when you

1288
01:28:17,400 --> 01:28:19,800
Speaker 1: compare that with a lot of other species that people

1289
01:28:19,800 --> 01:28:23,040
Speaker 1: are used to responding to, it's a it's a much

1290
01:28:23,200 --> 01:28:28,680
Speaker 1: lower bar for when you responded when you don't. We

1291
01:28:28,720 --> 01:28:34,240
Speaker 1: had a guy on talking about wild pigs one time,

1292
01:28:34,800 --> 01:28:37,000
Speaker 1: and we're talking about why they live, where they live,

1293
01:28:37,040 --> 01:28:42,240
Speaker 1: and where they could live. He was just saying that

1294
01:28:42,240 --> 01:28:45,400
Speaker 1: that they could live virtually anywhere, like they could they

1295
01:28:45,520 --> 01:28:47,960
Speaker 1: have the potential to colonize any part of the country.

1296
01:28:48,800 --> 01:28:51,639
Speaker 1: But the thing he brought up is it's just easy

1297
01:28:51,680 --> 01:28:56,760
Speaker 1: to detect them and eradicate them in certain landscapes, and

1298
01:28:56,840 --> 01:29:00,560
Speaker 1: certain landscapes you don't have a prayer, yep, of finding them, Like,

1299
01:29:00,600 --> 01:29:02,599
Speaker 1: there's no reason they couldn't be on in the Great Plains.

1300
01:29:02,840 --> 01:29:06,519
Speaker 1: But the thing is you'd find them. Yeah, I mean

1301
01:29:06,600 --> 01:29:09,559
Speaker 1: Colorado CPW just put out a notification that they had

1302
01:29:09,560 --> 01:29:13,479
Speaker 1: eradicated the hogs um from southeast Colorado. You know, they

1303
01:29:13,479 --> 01:29:15,559
Speaker 1: were they were working their way up into the grassland

1304
01:29:15,600 --> 01:29:20,080
Speaker 1: down there and there. They feel pretty confident they got

1305
01:29:20,120 --> 01:29:23,280
Speaker 1: them all. But you know, that's it's kind of whackable.

1306
01:29:23,600 --> 01:29:25,880
Speaker 1: There's no reason to think that they won't be able

1307
01:29:25,920 --> 01:29:29,240
Speaker 1: to get back in. You do, I want to have it?

1308
01:29:30,240 --> 01:29:32,960
Speaker 1: Go ahead? Now, go ahead next time you come on.

1309
01:29:33,040 --> 01:29:35,720
Speaker 1: You know what I want to talk about? What's up

1310
01:29:35,720 --> 01:29:39,400
Speaker 1: with this? Uh, what's up with this invasive monkey in Florida?

1311
01:29:40,000 --> 01:29:48,800
Speaker 1: Oh yeah, um yeah, And and that it's protected what? Um? Yeah,

1312
01:29:49,040 --> 01:29:53,120
Speaker 1: that's the crazy thing. There's an invasive protected monkey in Florida.

1313
01:29:53,200 --> 01:29:57,080
Speaker 1: Well it's it's not it's not considered a species that

1314
01:29:57,280 --> 01:30:00,680
Speaker 1: is a pest that you can legally um removed by

1315
01:30:00,680 --> 01:30:04,519
Speaker 1: any means, as opposed to some other species. Yeah, because

1316
01:30:04,600 --> 01:30:09,599
Speaker 1: monkeys are cute. Monkeys are cute and people care about them,

1317
01:30:09,640 --> 01:30:12,160
Speaker 1: and it's you know, it's the feral cat thing all

1318
01:30:12,200 --> 01:30:14,160
Speaker 1: over again. Um. You know if you want to go

1319
01:30:14,200 --> 01:30:18,800
Speaker 1: down the feral cat road, we can. But um, yeah,

1320
01:30:18,960 --> 01:30:22,719
Speaker 1: I'd love I'd love to get a quick synopsis of it, please.

1321
01:30:24,760 --> 01:30:28,200
Speaker 1: Um you mean that that that that feral cats are

1322
01:30:28,200 --> 01:30:30,400
Speaker 1: bad news and they kill a billion and a half

1323
01:30:30,479 --> 01:30:32,800
Speaker 1: birds in this country every year. But people get taught

1324
01:30:32,800 --> 01:30:37,960
Speaker 1: to you about shooting cats. Um. Absolutely, And that there's

1325
01:30:38,160 --> 01:30:43,040
Speaker 1: a whole lot of people that try to use really

1326
01:30:43,080 --> 01:30:46,519
Speaker 1: bad evidence to suggest that cats aren't that bad. But

1327
01:30:46,920 --> 01:30:52,120
Speaker 1: the you know, the trapped newter return policy, which has

1328
01:30:52,160 --> 01:30:56,439
Speaker 1: been adopted by increasing numbers of municipalities and counties and

1329
01:30:56,479 --> 01:31:03,280
Speaker 1: things like that, UM as a so called control mechanism. UM.

1330
01:31:03,479 --> 01:31:06,880
Speaker 1: Almost no evidence that it works at all. Plenty of

1331
01:31:06,920 --> 01:31:13,080
Speaker 1: evidence that cats in cat colonies live nasty, short, brutish

1332
01:31:13,120 --> 01:31:16,400
Speaker 1: lives for the most part, that it's not a humane

1333
01:31:16,439 --> 01:31:21,600
Speaker 1: thing to do for the cats or the wildlife. UM.

1334
01:31:21,640 --> 01:31:26,600
Speaker 1: And it's you know, in some ways, it's uh just

1335
01:31:26,680 --> 01:31:31,400
Speaker 1: kind of a convenient way for hard decisions to be avoided.

1336
01:31:31,560 --> 01:31:35,680
Speaker 1: Got you, alright, So when this monkey thing blows up,

1337
01:31:35,720 --> 01:31:40,120
Speaker 1: you gotta come back on. That's love too. Yeah. You know,

1338
01:31:40,200 --> 01:31:44,360
Speaker 1: me and Yanni have we've at monkey is that down

1339
01:31:44,360 --> 01:31:48,720
Speaker 1: in South America? That's right loves it. Hey, can I

1340
01:31:49,000 --> 01:31:52,080
Speaker 1: can I say something about your brother real quick? Yeah,

1341
01:31:52,160 --> 01:31:56,080
Speaker 1: I don't care. Yeah, all right, So I know, I

1342
01:31:56,160 --> 01:31:59,280
Speaker 1: just feel like I need to shout out to Dan

1343
01:31:59,360 --> 01:32:04,599
Speaker 1: Ronella because you know, I came to hunting late in life.

1344
01:32:04,640 --> 01:32:06,160
Speaker 1: You know, I didn't kill my first year till I

1345
01:32:06,200 --> 01:32:09,599
Speaker 1: was thirty. And Dan and I overlapped at Auburn when

1346
01:32:09,680 --> 01:32:13,200
Speaker 1: we were in grad school and Dan took me for

1347
01:32:13,479 --> 01:32:18,800
Speaker 1: my first, second, third, fourth, and fifth duck hunts. Huh,

1348
01:32:18,920 --> 01:32:23,760
Speaker 1: and water fowling is now like a really big part

1349
01:32:23,920 --> 01:32:28,120
Speaker 1: of my life. And I'm just really I'm just really

1350
01:32:28,120 --> 01:32:32,000
Speaker 1: grateful that I was such a nube and he took

1351
01:32:32,000 --> 01:32:36,840
Speaker 1: me out, and um, I just always consider that as

1352
01:32:36,880 --> 01:32:41,080
Speaker 1: super generous. Um. And you know, I just reconnected with

1353
01:32:41,120 --> 01:32:42,920
Speaker 1: him again a couple of years ago, and you know,

1354
01:32:44,280 --> 01:32:46,960
Speaker 1: have made a couple of trips to Alaska in the

1355
01:32:47,040 --> 01:32:49,920
Speaker 1: last two years, going again in August, tagged along with

1356
01:32:49,960 --> 01:32:54,040
Speaker 1: him on his sheep hunt last August. And yeah, I mean,

1357
01:32:54,040 --> 01:32:58,559
Speaker 1: I I'm just super appreciative of what a what a

1358
01:32:58,640 --> 01:33:02,439
Speaker 1: sort of giving guy he is is, um, And it's

1359
01:33:02,439 --> 01:33:04,479
Speaker 1: made a lot to me. Oh that's great to hear.

1360
01:33:04,560 --> 01:33:07,160
Speaker 1: What's funny about this? Is that our producer. When I

1361
01:33:07,200 --> 01:33:13,800
Speaker 1: told her to go find a Burmese python guy, the

1362
01:33:13,840 --> 01:33:16,920
Speaker 1: best one out there is what I asked for, she

1363
01:33:18,080 --> 01:33:21,080
Speaker 1: independently found you and then one day said, I found

1364
01:33:21,080 --> 01:33:23,160
Speaker 1: a guy and it turns out I think he knows

1365
01:33:23,200 --> 01:33:25,840
Speaker 1: your brother, which I thought was pretty funny. Which I

1366
01:33:25,920 --> 01:33:29,200
Speaker 1: thought it was funny. Yeah, yeah, Well, you guys had

1367
01:33:29,760 --> 01:33:33,600
Speaker 1: Harry Green on the Hunting Collective podcast, and Harry was

1368
01:33:33,640 --> 01:33:36,640
Speaker 1: my undergrad mentor in Berkeley and he's one of the

1369
01:33:36,680 --> 01:33:40,160
Speaker 1: snake gurus, but he also came to hunting late in life,

1370
01:33:40,320 --> 01:33:43,920
Speaker 1: and it's it's really fun to sit and talk with

1371
01:33:44,000 --> 01:33:50,360
Speaker 1: him and talk about how our non hunting life has

1372
01:33:51,040 --> 01:33:53,360
Speaker 1: informed our hunting life and made us, you know, maybe

1373
01:33:53,400 --> 01:33:57,720
Speaker 1: a lot more empathic with the opinions of people who

1374
01:33:57,720 --> 01:34:00,320
Speaker 1: don't know a lot about it. And way is to

1375
01:34:00,680 --> 01:34:05,080
Speaker 1: engage with him, and that's uh, that's another thing that's

1376
01:34:05,080 --> 01:34:09,479
Speaker 1: been you know, an unexpected benefit of meeting the Freezer.

1377
01:34:09,600 --> 01:34:12,840
Speaker 1: You know that that philosophical side of it um and

1378
01:34:13,280 --> 01:34:16,000
Speaker 1: why we do it and justifying why we do it.

1379
01:34:16,000 --> 01:34:19,479
Speaker 1: It's a it's a fun thing to think about. That's great.

1380
01:34:19,600 --> 01:34:23,200
Speaker 1: Thank you very much for coming on keep us surprised.

1381
01:34:23,479 --> 01:34:27,040
Speaker 1: Keep us surprised at those monkeys. Yep, yep, we'll do

1382
01:34:27,760 --> 01:34:29,920
Speaker 1: Thanks again, all right, take thanks