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Speaker 1: This is the Meat Eater Podcast coming at you shirtless,

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Speaker 1: severely bug bitten, and in my case, underwear listeningcast.

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Speaker 2: You can't predict anything.

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Speaker 1: The Meat Eater Podcast is brought to you by First Light.

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Speaker 1: Whether you're checking trail cams, hanging deer stands, or scouting

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Speaker 1: for el First Light has performance apparel to support every

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Speaker 1: hunter in every environment. Check it out at first light

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Speaker 1: dot com. F I R S T L I t

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Speaker 1: E dot com. Hey guys, it's uh Steve and I

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Speaker 1: have such an important message that it's so important to fill.

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Speaker 1: The engineer advised me on how to hold my phone

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Speaker 1: to get the best quality where it's about six inches

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Speaker 1: from my mouth at an angle so it doesn't go

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Speaker 1: when you talk. That's how important this messages. The auction

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Speaker 1: House of Oddity, our Meat Eater auction House of Oddities

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Speaker 1: is up, live and running right now, probably the most

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Speaker 1: impressive slate of auction items to ever be compiled in

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Speaker 1: the history of Western civilization. You can go right now

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Speaker 1: and bid on a hunt at the Durham family farm

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Speaker 1: with Bubbly Doug durn himself. It's the winner's choice. You

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Speaker 1: do a buck hunt for one or a deer hunt.

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Speaker 1: For one opening day of rifles season, so opening weekend

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Speaker 1: of rifle for one or three people, do a turkey

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Speaker 1: hunt on Doug's place, hanging out with Doug, cruising around

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Speaker 1: with Doug. We have a custom log trappers cabin from

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Speaker 1: Naughty Log Homes. Okay, so this is a totally built

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Speaker 1: trappers cabin that you can have, get shipped out and

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Speaker 1: you get assembled where you want it. Okay. We have

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Speaker 1: a art commission, an original art commission of your choosing.

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Speaker 1: Go on Instagram and go to at Jamie Jamie Underscore

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Speaker 1: Wild Art. Okay, go look at her work. You can

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Speaker 1: get an art commission from her. We have a signed arrow,

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Speaker 1: a chunk of an aluminum arrow signed by Ted Nugent

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Speaker 1: in nineteen ninety one from a whiplash bash. And we

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Speaker 1: have what in the art world they call the provenance.

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Speaker 1: We have how this arrow flowed through ownership to get

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Speaker 1: into your hands, says Ted Nugent ninety one on an

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Speaker 1: aluminum arrow shaft. Great story to go with that. Everybody

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Speaker 1: knows our beloved Seth Morris used to go by the

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Speaker 1: flip Flop Flesher. His wife is a professional artist. She

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Speaker 1: has a gallery. She makes her living with art. We

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Speaker 1: have original artwork from her Kelsey Morris original artwork. We

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Speaker 1: have my weather be Mark five used in many me

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Speaker 1: Eater episodes, forthcoming episode where I shoot a pretty stomper

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Speaker 1: mule deer in Idaho. I killed a bull with on

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Speaker 1: a media episode, a lot of media episodes, a left

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Speaker 1: handed whether it be Mark five and three hundred win

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Speaker 1: mag that exact rifle. We have a Carolina custom rifle

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Speaker 1: that I use in a bunch more Meat Eater episodes,

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Speaker 1: including the famous Moose Charge episode. That rifle is available

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Speaker 1: for auction, and there is a dinner for four at

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Speaker 1: my house. So the winning bidder and three guests will

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Speaker 1: at a time that we picked together, will come to

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Speaker 1: my house to be wined and dined for auction. Doug

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Speaker 1: Duran was bragging up how his auction item was kicking

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Speaker 1: my dinner's ass. But I have to point out right

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Speaker 1: now that it's probably humiliating to Doug the degree to

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Speaker 1: which my dinner is kicking his farm's ass. So you

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Speaker 1: better go help Bubbly out before he gets sad. Got

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Speaker 1: to go to the Meeater dot com and find you know,

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Speaker 1: there's all kinds of links and stuff. Auction house of

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Speaker 1: Oddities or go to at Steve Ranella on Instagram and

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Speaker 1: we will put the Auction House of Oddities link in

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Speaker 1: my bio. You'll find it. Thank you everybody. Now back

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Speaker 1: to regular program. Okay, we're at the Cornell Lab of

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Speaker 1: Oh you know, that's what they call you, guys over

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Speaker 1: in the disease Party. You're the Lab of oh Nice,

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Speaker 1: the Lab of Ornithology. And we are seeded with someone

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Speaker 1: who we've inadvertently talked about a whole bunch. That's not right,

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Speaker 1: we talked about without naming your name, because we have

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Speaker 1: again and again and again on the show on social

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Speaker 1: media otherwise brought up the Merlin bird app and have

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Speaker 1: office said we all get the person that made the

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Speaker 1: Merlin Bird app on the podcast. And here she is,

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Speaker 1: Jesse Barry.

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Speaker 3: Hey, it's great to be here.

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Speaker 1: Ye, joined as well by an ornithologist Chris Wood. And

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Speaker 1: if you are into e Bird, I'm going to go

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Speaker 1: back on Mrlam and explain eBird.

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Speaker 4: Yeah.

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Speaker 5: So, the idea of Ebert is there's thousands, tens of thousands,

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Speaker 5: millions of people around the world that like birds, like

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Speaker 5: watching them. This is a place for those all your

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Speaker 5: observations of birds to go into a centralized database. You

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Speaker 5: can see that information. But even more importantly than a

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Speaker 5: team of scientists can analyze that information, that information is

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Speaker 5: then used by groups like Fish and Wildlife Service and

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Speaker 5: others for policy action.

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Speaker 1: Yep, and we're gonna and Chris is gonna explain how

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Speaker 1: you can exploit eBird to get better bag limits on ducks.

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Speaker 5: Orn's going to talk about that.

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Speaker 1: I'm joke about. We're gonna get We're gonna get into

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Speaker 1: Ebert and then Oron Robinson, another ornithologist.

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Speaker 2: Yeah, so I work with the data after it comes

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Speaker 2: through Ebert and all the filters. We can create these

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Speaker 2: these distribution maps for thousands and thousands of vcs, and

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Speaker 2: we can take that eBird data and use it in

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Speaker 2: conservation or management applications.

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Speaker 1: And you're a duck hunter? Yes, where'd you grow up?

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Speaker 2: Grew up in Mobile, Alabama?

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Speaker 1: Okay, you still hunt there?

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Speaker 2: Not very often?

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Speaker 1: No, already mainly hunt ducks.

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Speaker 2: Now Arkansas. We've got some good family friends in Arkansas.

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Speaker 1: Okay, go over there once a year. Do you often

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Speaker 1: meet other ornithologists who are disappointed to hear that you're

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Speaker 1: a duck hunter? Or is that pretty common.

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Speaker 2: In the duck world. Most of the folks that study

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Speaker 2: ducks are also duck hunters.

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Speaker 1: Yeah, a pictures that I have people that studied the

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Speaker 1: ducks hunt ducks. Yeah, you know.

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Speaker 2: But no, I haven't really gotten any any pushback on

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Speaker 2: the fact that I enjoy duck hunting.

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Speaker 1: It makes sense to people. All Right, we got it.

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Speaker 1: So I'm gonna explain Merlin real quick, or you could

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Speaker 1: probably do a better job explaining Merlin, but I'll explain

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Speaker 1: how it came to me. Jannis Prutellus, who's on the

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Speaker 1: show all the time, you used to be on every

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Speaker 1: episode show, he found out about it and told me

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Speaker 1: about it, and I got into it. It's an app.

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Speaker 1: You open up the app, you can go to sound

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Speaker 1: id and it just starts populating with whatever birds are

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Speaker 1: going on. I got a lot of questions about how

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Speaker 1: this functions and some other problems, but then we started

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Speaker 1: having a lot of fun of raiding each other's turkey calls,

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Speaker 1: whether you can trick Merlin, getting into trying to fool

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Speaker 1: Merlin in other ways, and also a sort of a

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Speaker 1: slight a little bit of a contest about who can

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Speaker 1: get the most birds going at anyone time. And I'm

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Speaker 1: not that high. I had nine going in eastern Kansas

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Speaker 1: this year, which I thought was pretty good. But I've

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Speaker 1: met many people who've been who've had in the teens

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Speaker 1: and anyone sitting. But I had nine and I don't know,

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Speaker 1: you know, twenty thirty minutes leaning against one tree, which

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Speaker 1: I thought was pretty impressive. I've also found it helpful

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Speaker 1: for when you're somewhere and you, like in southeast Alaska,

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Speaker 1: there's this bird I'm always hearing. I can't remember what

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Speaker 1: the help was even though I looked it up. You

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Speaker 1: never in a million years are going to get a

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Speaker 1: chance to see it because it's in the canopy of

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Speaker 1: old growth and able to like figure out what you're

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Speaker 1: listening to. So if you get Merlin's Free, still still free,

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Speaker 1: you guys probably steal people's data.

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Speaker 3: No, no, it doesn't feed back, It doesn't feedback. No,

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Speaker 3: it's still a whole point, totally separate. So we get

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Speaker 3: people excited about watching birds and then hopefully some of

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Speaker 3: them start contributing data to the e bird, and that's

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Speaker 3: where just steal whatever anybody's hearing. Yeah, you know, we're

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Speaker 3: still trying to be like above the line on some

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Speaker 3: of these things. And when we make that shift, you know,

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Speaker 3: make it very intentional that you know, we're not just

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Speaker 3: listening in, we're not eavesdropping on you.

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Speaker 1: Oh no, but I would like so this doesn't report

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Speaker 1: back to you. I thought that'd be the only thing

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Speaker 1: you have to gain from it.

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Speaker 3: It doesn't right now. We'll make that step coming up

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Speaker 3: in the next few years.

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Speaker 1: I've been just assuming, and that's why I felt a

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Speaker 1: little bad when we're messing with it, that I would

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Speaker 1: throw off some data.

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Speaker 3: No, it's okay, Yeah, okay. We want people to have

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Speaker 3: fun just enjoying the app, and then we're collecting real

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Speaker 3: data for science. We want you know, that's a different thing,

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Speaker 3: and we want people know that too.

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Speaker 1: Yeah. Hunting turkeys, you know, it's humiliating. If someone has

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Speaker 1: Merlin open and someone calls and it doesn't recognize it

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Speaker 1: as a wild turkey, it's humiliating. Yeah. Then we got

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Speaker 1: into this spring trying to do a barred owl an

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Speaker 1: owl hoot, which it's humbling, piliated woodpacker calls humbling crow.

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Speaker 3: Tough.

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Speaker 1: Tough, Yeah, raven tough. You can beat it on a

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Speaker 1: bard owl.

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Speaker 3: I can.

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Speaker 1: Yeah, I test you. Yeah, it doesn't like you know what,

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Speaker 1: it doesn't like noises and rooms though. Have you noticed that?

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Speaker 3: Yeah, it's you know, really tuned in for the outdoors.

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Speaker 3: So all the data that Merlin has used to learn

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Speaker 3: these sounds is coming from outside. So yeah, it's not

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Speaker 3: as good on the inside.

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Speaker 1: Do you dare try to do a test?

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Speaker 3: Yeah, sure, you'll do it.

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Speaker 1: Okay, got it on. I can see my voice bouncing

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Speaker 1: up and down. Okay you ready? Oh nice? Right there. Now.

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Speaker 1: My buddy Seth he we had one in southeast Alaska

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Speaker 1: this year, going nuts, couple in the tree and he

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Speaker 1: for two nights sat out with Merlin, open it, calling Merlin,

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Speaker 1: binging it. He couldn't get it. Eventually got it and

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Speaker 1: he feels that it's in the hall.

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Speaker 3: Yeah you got.

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Speaker 1: It's very picky now, yeah, that's where he he had

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Speaker 1: to work on that. And once he worked on that,

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Speaker 1: he was able to get it, but he wasn't quite

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Speaker 1: clear on what he did. But all sudden he got it.

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Speaker 1: Where would trick it? But here's the thing I don't get.

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Speaker 1: I have the bird Song Bible. You've met with the

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Speaker 1: bird Song Bible team.

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Speaker 3: Here, put that up.

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Speaker 1: Those you guys, our sounds are in there. Well, we'redly.

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Speaker 1: My kids like the mess of the bird Song Bible.

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Speaker 1: The bird Song Bible can't fool the app but it

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Speaker 1: must be something. Going through recording and coming out the

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Speaker 1: other side, it loses some essential elements.

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Speaker 3: So the sounds in that particular book with the little

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Speaker 3: speaker are really compressed. And that was you know, ten

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Speaker 3: fifteen years ago when there just wasn't the technology to

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Speaker 3: put a good speaker next to a book. So yeah,

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Speaker 3: those sounds have been shrunk down so much that Merlin

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Speaker 3: can't really see the signal I got.

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Speaker 1: So that's what it is.

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Speaker 3: I think.

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Speaker 1: So I didn't know this was you, but I came

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Speaker 1: into contact years ago with and I want you start

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Speaker 1: by explaining the McAuley library. But years ago I was

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Speaker 1: trying to figure out a game call for a blue grouse,

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Speaker 1: which leads I gotta digress because it leaves me not

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Speaker 1: our question. Do you guys know how they try to

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Speaker 1: the blue grouse into two different grouse? Do you believe that?

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Speaker 1: Or I mean do you buy that? Like who gets

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Speaker 1: to decide? Yeah, So there's like it seems like it's

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Speaker 1: be up to God. That's one. That's one perspective, you

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Speaker 1: know what I mean.

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Speaker 5: I think I think the thing is is there's really

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Speaker 5: no such thing as a species, right, It's an artificial

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Speaker 5: construct that people have invented to try to make sense

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Speaker 5: of the natural world. And there's certain things that are

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Speaker 5: that are really you know, obviously differentiated, and other things

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Speaker 5: that are that are very very close and it's hard

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Speaker 5: to understand. And so in the case of what's now,

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Speaker 5: these two different species of grouse, they're separated, you know

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Speaker 5: a little bit. Basically one of them is in the

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Speaker 5: Pacific North Northwest, the others in.

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Speaker 1: The Rockies sooty and dusky the city in the Pacific Northwest,

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Speaker 1: the dusky and the Rockies.

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Speaker 5: Yeah, and so there have been a series of studies

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Speaker 5: and right now, usually what people are looking at, what

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Speaker 5: scientists are looking for is that they're looking for a

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Speaker 5: combination of voke differences. And so those two those two

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Speaker 5: species as we're considering them now, do have very distinct

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Speaker 5: vocal differences.

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Speaker 1: The number of number of them, yeah, different.

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Speaker 5: Exactly, and one of them, you know, soodies will sit

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Speaker 5: up in trees to give those dusky grouse basically sit

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Speaker 5: very very low to deliver those those vocalizations, and or

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Speaker 5: right on the ground exactly look at at most like

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Speaker 5: a foot up right, And and so those that mating

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Speaker 5: behavior is a very strong sexual selection pressure that that

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Speaker 5: sort of as that happens over hundreds and thousands of years,

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Speaker 5: they really start to become distinct populations.

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Speaker 1: Is the idea that they're is the is the split

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Speaker 1: widening with them, like the connectivity diminishing with them?

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Speaker 5: You know, I don't know. I don't know the specifics

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Speaker 5: of that, but what's likely to happen is is as

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Speaker 5: you're seeing sort of the combined effects of climate change

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Speaker 5: and then just human cause changes in forests, it's likely

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Speaker 5: for that to shift over time. And so that's something

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Speaker 5: that we're increasingly seeing with a lot of kind of

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Speaker 5: species limits. I don't know the specifics of those of

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Speaker 5: those two individuals, but.

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Speaker 1: One of them has a different color eye comb, right,

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Speaker 1: or they one of them has tends to have an

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Speaker 1: orange and one has a tends to have a yellow

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Speaker 1: e comb.

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Speaker 5: Yeah, and there's some differences in the tailband as well.

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Speaker 5: But part of that gets confusing because there's different subspecies

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Speaker 5: within each of those populations as well. And so there's

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Speaker 5: subspecies of dusky grouse that have broad tail bands, others

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Speaker 5: that have narrow tail bands, and so it actually does

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Speaker 5: end up getting to be quite a bit more confusing.

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Speaker 5: And what happens is grouse in general don't travel very

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Speaker 5: long distances. They may migrate, but they're elevational migrants, particularly

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Speaker 5: those in the Rockies. They're not moving, you know, thousands

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Speaker 5: of miles like Swainson's thrush. And so what that means

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Speaker 5: is that there's it's more likely for those populace, for

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Speaker 5: those birds to sort of diverge and not come into

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Speaker 5: contact as much as other Sorry, the lights just went out,

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Speaker 5: I don't know, looks fine, and Warren said, let there

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Speaker 5: be light there we go, so go on, and yeah,

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Speaker 5: I mean so, So because of that that that's there's

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Speaker 5: a whole bunch of different things that come at play

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Speaker 5: as sort of how things end up end up diverging

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Speaker 5: into separate species. But it's a very very dynamic process.

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Speaker 1: Our Fish and Game Agency they have not made the switch,

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Speaker 1: so they are still blue grouse. They've not moved them

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Speaker 1: to dusky grouse. They have Uh when you go in,

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Speaker 1: do do bison? They do bison, but not buffalo, pronghorn

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Speaker 1: or antelope but not pronghorn, and blue grouse or blue

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Speaker 1: grouse but not dusky grouse. I made the switch, but

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Speaker 1: then I switched back. Not imhing about switching back again.

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Speaker 1: I think you should, so I'll continue with switching. I

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Speaker 1: wanted to make a cutie grouse call because in Alaska

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Speaker 1: you can hunt sudi grouse in the spring, but they're

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Speaker 1: hard to find up in the tree, but they'll call.

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Speaker 1: It's the mail up there, right, And I thought you

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Speaker 1: could hide and then draw it down. So I had

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Speaker 1: a guy who that's a game call maker, and I

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Speaker 1: was sending him all I was trying to find good vocalizations,

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Speaker 1: and I wound up sending him all the McCauley Library

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Speaker 1: blue grouse. So do excuse me, Citi grouse records. And

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Speaker 1: I had no until the day, I had no idea

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Speaker 1: that was you guys.

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Speaker 5: Yeah.

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Speaker 3: So mcaulay Library started in nineteen twenty nine. The very

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Speaker 3: first bird songs were recorded right here in Ithico.

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Speaker 1: Uh huh.

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Speaker 3: And so yeah, it was the collaboration between Arthur Allen

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Speaker 3: and Peter Paul Kellogg. And Arthur Allen was an ornithologist,

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Speaker 3: Peter Paul Kellogg an engineer, and they were just trying

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Speaker 3: to figure out how they could study you know, birds,

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Speaker 3: you know, more carefully and so they're using the technology

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Speaker 3: of the day to go out and record those behaviors,

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Speaker 3: which ended up being a bunch of equipment used for films.

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Speaker 3: So that early film technology enabled us to start to

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Speaker 3: document these wildlife sounds. And you know, we're still have

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Speaker 3: folks all over the world who are going out and

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Speaker 3: recording birds and other wildlife and bring them to the

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Speaker 3: archive here.

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Speaker 1: Does anyone have they must? But early we got to

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Speaker 1: go hold you know, the extinct you have three was

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Speaker 1: it two or three? I rebuild woodpackers three two males

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Speaker 1: and females in your collections. So we were able to

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Speaker 1: hold the extinct ivybuild a woodpacker, the extinct Carolina parakeet, observe,

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Speaker 1: but did not hold the heath hen, which was like

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Speaker 1: a basically, oh man, it'd be like a sharp tail

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Speaker 1: grouse that lived in Martha's vineyard. You know, uh, does

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Speaker 1: any are there recordings of their calls?

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Speaker 3: So there are recordings of ivorybuild woodpecker, and a little

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Speaker 3: bit of motion picture film too, which we have in

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Speaker 3: the back room.

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Speaker 1: As well, those from the Singer forest, wasn't it.

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Speaker 3: We don't have any heathens or Eskimo curlew was never recorded.

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Speaker 3: I don't think Carolina parakeet was either. So yeah, a

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Speaker 3: lot of those you know, species that were one extinct

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Speaker 3: in the early nineteen hundreds missed that window of being recorded.

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Speaker 3: Ivybuilds one of the few that we were able to capture.

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Speaker 1: Yeah, so walk me through from from starting from the

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Speaker 1: mcaulay library, how did like how many birds sounds are

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Speaker 1: in Merlin?

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Speaker 3: Merlin can cover a thousand species in the sound i

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Speaker 3: D feature where you can hit record and Merlin can

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Speaker 3: identify them. And then there's now ten five hundred species

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Speaker 3: of birds that you can explore and listen to right

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Speaker 3: from the app. Now, so basically all the birdies can

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Speaker 3: recognize the thousand yep and as we get more data

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Speaker 3: coming into the archive, we'll be able to add more species.

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Speaker 3: So it takes a lot of example recordings and experts

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Speaker 3: to help Merlin really dig into learning what those that

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Speaker 3: species sounds like.

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Speaker 1: Yeah, when you you know, like many people, I'm reflexively

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Speaker 1: anti AI, but this is very much.

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Speaker 3: AI, right, Yeah, this is definitely machine learning. This is

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Speaker 3: a we're ten plus years into really trying to understand

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Speaker 3: how to incorporate machine learning into orthological research and ways

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Speaker 3: to engage people with birds. So we try to think

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Speaker 3: like we're the good guys and the AI side of things.

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Speaker 1: So explain how it works and how you ever populated it,

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Speaker 1: and how it can sort through all the background noise

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Speaker 1: or that you could be in in a room. I

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Speaker 1: realized it doesn't like a ton of unusual background noise,

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Speaker 1: but you can be in a room where people are conversing,

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Speaker 1: or on a patio with people conversing, and it's picking

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Speaker 1: out some bird off in the distance, and like, it's

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Speaker 1: just so hard to picture how it's sorting out. Right, Like,

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Speaker 1: you can sit because you have a you know, your

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Speaker 1: ears have kind of a three sixty cents of what's

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Speaker 1: going on around you, and you're able to sort things

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Speaker 1: by distance. But when it's coming through into a microphone,

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Speaker 1: like something's losing track of well that's to my left right,

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Speaker 1: it gets flattened. Yeah, you know, all the sound is

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Speaker 1: flattened into a single sphere and it's not like, oh no,

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Speaker 1: that's a thing that has nothing to do with this

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Speaker 1: way over there, but the person talking to me is

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Speaker 1: right here. I just can't even picture how you begin

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Speaker 1: to get a thing that could pluck it out.

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Speaker 3: Yeah, So one way to think about it is, you know,

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Speaker 3: sounds can be converted into a picture. So we basically

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Speaker 3: take you know, that recording and it turns into a

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Speaker 3: graph where you've got the time and the frequency and

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Speaker 3: that will you know, produce a signal. So if a

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Speaker 3: cardinal's got those introductory notes, you can see exactly where

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Speaker 3: all the notes are in the cardinal song. And so

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Speaker 3: what we did is basically look at the all the

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Speaker 3: recordings of them, call a library archive or a subset,

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Speaker 3: and then you know, essentially train Merlin to identify those

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Speaker 3: recordings by having experts draw boxes around all of the

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Speaker 3: times that a bird sings in that particular recording. So

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Speaker 3: again we're taking the sound, we're moving it into a

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Speaker 3: picture and then going online and annotating every time a

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Speaker 3: cardinal sings, every time a chickadee sings in the background.

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Speaker 3: And originally we're just you know, picking out Okay, if

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Speaker 3: we're trying to teach you how to identify cardinals, we'll

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Speaker 3: just drop boxes around the cardinals. But then we realized

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Speaker 3: that if we drew boxes and labeled everything in that soundscape,

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Speaker 3: then Merlin was able to figure out exactly what was

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Speaker 3: going on in that landscape that was a jump we

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Speaker 3: didn't really expect, but it's really thanks to like all

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Speaker 3: the folks who put those recordings into the archive and

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Speaker 3: then the experts who took you know, hours and hours,

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Speaker 3: like in a very time consuming way making those annotations,

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Speaker 3: and then it goes into the machine learning system. And

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Speaker 3: so Grant Van Horne is the leading computer scientist on

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Speaker 3: this project, and he's really figured out how you can

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Speaker 3: you know, tweak this system to put those real time

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Speaker 3: predictions back out. So part of the fun is that

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Speaker 3: Merlin's like lighting up, and and any as any species

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Speaker 3: starts to vocalize, it's picking that up. And that's really

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Speaker 3: Grant's hard work testing out how to make a system

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Speaker 3: that will do that and also run on a phone.

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Speaker 3: So that's the other really cool thing is that we

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Speaker 3: been able to leverage Apple's hardware to put these algorithms

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Speaker 3: right on a device that can work without a connection

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Speaker 3: to the internet.

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Speaker 1: Have you guys been surprised by how many like it's

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Speaker 1: its rate of adoption with users?

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Speaker 3: Yeah, pleasantly surprised to me, Yeah, Like.

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Speaker 1: How can you say how many people are out there

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Speaker 1: using it?

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Speaker 3: Three and a half million in July. Yeah, on a

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Speaker 3: given weekend.

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Speaker 1: On me Turkey hunters.

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Speaker 3: I don't know, Turkey was like a little lower on

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Speaker 3: the list of frequently identified birds, and.

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Speaker 1: I might have guessed, but oh really yeah, okay, so

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Speaker 1: it's not all turkey hunters. So this is the thing

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Speaker 1: that really surprised me. I just assumed that the whole

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Speaker 1: point of Merlin was that it would allow ornithologists to

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Speaker 1: get these location specific, time specific snapshots of birds sounds,

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Speaker 1: and that any time I was using I never you know,

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Speaker 1: I was just like everybody, you never read terms of service.

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Speaker 1: You just are like, Okay. I just assumed that when

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Speaker 1: I had it open and it was listening, that it

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Speaker 1: went into some database somewhere of you know, at blank date,

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Speaker 1: at blank time, at blank location, here's what was going on.

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Speaker 1: But you're not doing that, not yet.

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Speaker 3: That is the next step, okay, Because that ability to

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Speaker 3: monitor a landscape for like three minutes of a nice

436
00:24:27,320 --> 00:24:29,600
Speaker 3: recording is going to give us a tremendous amount of

437
00:24:29,600 --> 00:24:32,720
Speaker 3: information about the species that are there, and so we're

438
00:24:33,040 --> 00:24:35,840
Speaker 3: going to soon be preparing kind of the eBird you know,

439
00:24:36,040 --> 00:24:39,200
Speaker 3: data model and how all that those observations are handled

440
00:24:39,520 --> 00:24:43,399
Speaker 3: and incorporate this kind of different type of data into

441
00:24:43,440 --> 00:24:45,960
Speaker 3: that system. But it's a pretty big jump to go

442
00:24:46,040 --> 00:24:51,359
Speaker 3: from birdwatchers putting in a sighting to now here's basically

443
00:24:51,400 --> 00:24:54,439
Speaker 3: an automated system collecting that information. So I've got a

444
00:24:54,440 --> 00:24:57,920
Speaker 3: lot of research and experiments to do, but certainly that's

445
00:24:57,960 --> 00:25:00,720
Speaker 3: where it's heading. And you're going to imagine in years

446
00:25:00,720 --> 00:25:04,480
Speaker 3: to come, you know, putting devices out on the landscape

447
00:25:04,520 --> 00:25:07,879
Speaker 3: to really monitor spots where you know people don't go

448
00:25:08,000 --> 00:25:08,480
Speaker 3: very often.

449
00:25:09,240 --> 00:25:11,320
Speaker 1: But that's going on right now, right yeah, because I

450
00:25:11,359 --> 00:25:13,320
Speaker 1: know people do it with Turkey researchers that just put

451
00:25:13,359 --> 00:25:15,120
Speaker 1: stuff up to pick up gobbles yep.

452
00:25:15,359 --> 00:25:17,560
Speaker 3: And you know, I think it's going to become something

453
00:25:17,600 --> 00:25:21,199
Speaker 3: that more people can do. And hopefully it doesn't have

454
00:25:21,280 --> 00:25:23,879
Speaker 3: to be like a researcher putting those devices out, but

455
00:25:24,240 --> 00:25:28,080
Speaker 3: you know, somebody backpacking up into the high peaks would

456
00:25:28,080 --> 00:25:28,840
Speaker 3: be able to do it too.

457
00:25:28,880 --> 00:25:31,520
Speaker 1: And get yourself one of them Chinese spy balloons man,

458
00:25:32,040 --> 00:25:34,639
Speaker 1: and just pull that thing around sucking up bird sounds.

459
00:25:34,680 --> 00:25:35,240
Speaker 1: It'd be great.

460
00:25:36,520 --> 00:25:36,639
Speaker 6: Uh.

461
00:25:38,240 --> 00:25:41,280
Speaker 1: One thing that we had dinner last night, we talked

462
00:25:41,400 --> 00:25:42,840
Speaker 1: and I've seen it in some of the stuff you

463
00:25:42,840 --> 00:25:49,240
Speaker 1: guys do that with with the with the stuff you

464
00:25:49,320 --> 00:25:51,960
Speaker 1: gather through e bird and maybe it'll be demonstrated in

465
00:25:52,040 --> 00:25:58,560
Speaker 1: Merlin that it's just it seems like you guys say

466
00:25:58,720 --> 00:26:06,160
Speaker 1: that bird numbers are just declining and just that's it.

467
00:26:06,240 --> 00:26:10,600
Speaker 5: Is that there's a lot more complexity to it, and

468
00:26:10,640 --> 00:26:12,440
Speaker 5: I think I think one of the things that we're

469
00:26:12,480 --> 00:26:14,399
Speaker 5: able to see now is we have people all around

470
00:26:14,400 --> 00:26:17,399
Speaker 5: the world. You know, there's almost a million people who've

471
00:26:17,440 --> 00:26:19,720
Speaker 5: contributed their sightings to eBird.

472
00:26:19,840 --> 00:26:19,919
Speaker 4: Uh.

473
00:26:20,200 --> 00:26:23,280
Speaker 5: They're in every country in the world. And so from that,

474
00:26:23,440 --> 00:26:26,720
Speaker 5: because they're in different habitats, they're in different geographic locations,

475
00:26:26,760 --> 00:26:30,160
Speaker 5: they're they're there all year round, we're able to see

476
00:26:30,560 --> 00:26:33,600
Speaker 5: not just that a species is necessarily declining, but this

477
00:26:33,760 --> 00:26:37,000
Speaker 5: sort of tapestry and complexity of how that decline might

478
00:26:37,040 --> 00:26:40,399
Speaker 5: be taking place. And so it might be that some

479
00:26:40,560 --> 00:26:44,439
Speaker 5: birds a pattern is that, you know, some birds are

480
00:26:44,480 --> 00:26:48,840
Speaker 5: shifting their populations to the north with climate change, and

481
00:26:48,960 --> 00:26:52,960
Speaker 5: so that's part of what is really interesting for us

482
00:26:53,000 --> 00:26:56,760
Speaker 5: to figure out, is is just exactly how that's happened,

483
00:26:56,800 --> 00:27:00,520
Speaker 5: because then we can start to understand what the d are,

484
00:27:00,560 --> 00:27:04,160
Speaker 5: what the causes of these declines might be that otherwise

485
00:27:04,160 --> 00:27:07,080
Speaker 5: would be really really hard to figure out. I mean,

486
00:27:07,080 --> 00:27:09,720
Speaker 5: we often think about birds as being the canary and

487
00:27:09,760 --> 00:27:12,280
Speaker 5: the coal mine, if you will, and that they're really

488
00:27:12,320 --> 00:27:17,760
Speaker 5: good proxies for understanding natural systems overall, because natural systems

489
00:27:17,800 --> 00:27:21,160
Speaker 5: are very, very complicated, and we wanted to think about

490
00:27:21,200 --> 00:27:23,959
Speaker 5: how do you kind of measure the heartbeat of the

491
00:27:24,000 --> 00:27:27,359
Speaker 5: planet and an ecosystem. It turns out that birds end

492
00:27:27,440 --> 00:27:29,679
Speaker 5: up being a really really good way to do that.

493
00:27:29,720 --> 00:27:31,920
Speaker 5: Because you have hundreds of thousands of people that are

494
00:27:31,960 --> 00:27:34,679
Speaker 5: watching birds, they can report the sightings that they see.

495
00:27:35,080 --> 00:27:37,480
Speaker 5: Then we can take that information sort of like Orn

496
00:27:37,640 --> 00:27:43,120
Speaker 5: was talking about, and link other that information with remote

497
00:27:43,160 --> 00:27:46,719
Speaker 5: sensed imagery. So since we know where you are, we

498
00:27:46,760 --> 00:27:49,879
Speaker 5: can link that and then understand information about the habitat

499
00:27:49,880 --> 00:27:52,240
Speaker 5: at a variety of scales. You know, we can understand

500
00:27:52,240 --> 00:27:55,879
Speaker 5: what the habitat's like within you know, one hundred meters

501
00:27:55,920 --> 00:27:58,080
Speaker 5: of where you were, within a mile of where you were,

502
00:27:58,119 --> 00:28:01,320
Speaker 5: and within ten miles, and can start to say, like, okay,

503
00:28:01,320 --> 00:28:04,560
Speaker 5: it seems like this bird during the breeding season requires

504
00:28:04,600 --> 00:28:08,080
Speaker 5: this type of habitat during another type of you know,

505
00:28:08,119 --> 00:28:10,919
Speaker 5: maybe post breeding, you know, after the young leave the

506
00:28:11,000 --> 00:28:13,320
Speaker 5: nest and are moving around, they may switch to a

507
00:28:13,359 --> 00:28:17,080
Speaker 5: slightly different type of habitat and being able to understand

508
00:28:17,119 --> 00:28:20,679
Speaker 5: how those connections happen can be really important for conservation.

509
00:28:21,560 --> 00:28:24,639
Speaker 1: But I mean, are there you know, I mean, are

510
00:28:24,680 --> 00:28:27,119
Speaker 1: there as many birds in the US as there was

511
00:28:27,760 --> 00:28:31,560
Speaker 1: twenty five years ago. No, they're not. Okay, that's what

512
00:28:31,600 --> 00:28:32,359
Speaker 1: I'm trying. That's what.

513
00:28:32,480 --> 00:28:37,840
Speaker 5: Yeah, there's but you know, overall, there's there's basically based

514
00:28:37,840 --> 00:28:40,440
Speaker 5: on some of the work that teams here and others others,

515
00:28:40,480 --> 00:28:42,840
Speaker 5: did three billion birds have been lost in like the

516
00:28:42,920 --> 00:28:45,320
Speaker 5: last forty years, three billion with a.

517
00:28:45,280 --> 00:28:48,240
Speaker 1: Bee of all sorts, of.

518
00:28:48,640 --> 00:28:50,920
Speaker 5: All sorts, And I mean it's a very very common.

519
00:28:51,440 --> 00:28:54,920
Speaker 5: It's it's the most species are declining.

520
00:28:55,320 --> 00:28:55,520
Speaker 1: You know.

521
00:28:55,560 --> 00:28:57,680
Speaker 5: If you go on to our website, we have a

522
00:28:57,720 --> 00:29:00,200
Speaker 5: science section on there and you can basically go through

523
00:29:00,240 --> 00:29:03,600
Speaker 5: and for more than four hundred species see exactly what's

524
00:29:03,600 --> 00:29:06,520
Speaker 5: happening and where those birds are declining, where they might

525
00:29:06,560 --> 00:29:10,800
Speaker 5: be increasing. So it is complex, and there are areas

526
00:29:11,120 --> 00:29:14,800
Speaker 5: that birds are increasing, and there's certain species and groups

527
00:29:14,840 --> 00:29:19,760
Speaker 5: of birds that are increasing, but the overall pattern right

528
00:29:19,800 --> 00:29:24,160
Speaker 5: now is one where birds are really declining, particularly things

529
00:29:24,200 --> 00:29:30,040
Speaker 5: that might be migrants that are traveling long distances, shore birds,

530
00:29:30,080 --> 00:29:34,080
Speaker 5: grassland birds. Those are some of the major groups of

531
00:29:34,080 --> 00:29:36,560
Speaker 5: birds that have very very significant declines.

532
00:29:37,160 --> 00:29:40,200
Speaker 1: I was surprised to learn recently that there's only five

533
00:29:40,280 --> 00:29:43,640
Speaker 1: species of birds that have a global population of one billion.

534
00:29:46,680 --> 00:29:47,560
Speaker 5: Where did you read that?

535
00:29:48,920 --> 00:29:51,800
Speaker 1: I read it in a book called Wild New World

536
00:29:51,840 --> 00:29:58,080
Speaker 1: by the historian dam Floores. Huh, the English sparrow, a goal,

537
00:29:58,640 --> 00:30:01,920
Speaker 1: which surprised me. I can't remember what goal it was.

538
00:30:02,680 --> 00:30:09,680
Speaker 1: What else is in there? The European starling? Maybe, Yeah,

539
00:30:09,760 --> 00:30:11,840
Speaker 1: there's five spieces of birds that they think have a

540
00:30:11,880 --> 00:30:13,760
Speaker 1: global population more than one billion.

541
00:30:14,040 --> 00:30:18,760
Speaker 5: Yeah, it's measuring the absolute number of birds ends up

542
00:30:18,800 --> 00:30:21,280
Speaker 5: being a very very difficult thing.

543
00:30:21,520 --> 00:30:22,120
Speaker 1: You're not buying it.

544
00:30:23,720 --> 00:30:26,400
Speaker 5: All I'm saying is a very difficult thing to figure out.

545
00:30:26,400 --> 00:30:30,520
Speaker 5: You know, it's actually easier to understand the percent decline

546
00:30:30,520 --> 00:30:33,960
Speaker 5: in a total population of birds because you're looking at

547
00:30:34,160 --> 00:30:37,920
Speaker 5: the relative difference than saying sort of the absolute number

548
00:30:37,960 --> 00:30:40,240
Speaker 5: of birds. Because if you think about some birds, like

549
00:30:40,280 --> 00:30:43,040
Speaker 5: a bald eagle, it they tend to sit out in

550
00:30:43,080 --> 00:30:47,600
Speaker 5: the open or they're flying around. They're big, they're easily detectable.

551
00:30:48,600 --> 00:30:52,120
Speaker 5: But something like a red eyed vireo or a Swainson's thrush.

552
00:30:52,160 --> 00:30:54,480
Speaker 5: I think that's the example of a bird that's sort

553
00:30:54,480 --> 00:30:57,720
Speaker 5: of hiding up in the woods. If you're an expert birdwatcher,

554
00:30:57,760 --> 00:31:00,000
Speaker 5: maybe you're hearing that and identifying it as a swains

555
00:31:00,040 --> 00:31:02,360
Speaker 5: and strutch, but a lot of other people would wouldn't

556
00:31:02,400 --> 00:31:09,400
Speaker 5: see those birds because of those detection differences. It can

557
00:31:09,440 --> 00:31:13,120
Speaker 5: be really difficult to sort of compare one species to

558
00:31:13,320 --> 00:31:16,560
Speaker 5: another species. And some other birds, you know, if you

559
00:31:16,600 --> 00:31:20,360
Speaker 5: think about red wing blackbird, they can have very clumped distribution,

560
00:31:20,600 --> 00:31:23,400
Speaker 5: So around marshes they're very very abundant, but if you

561
00:31:23,440 --> 00:31:26,640
Speaker 5: get away from their habitats, they're much less common at

562
00:31:26,680 --> 00:31:29,360
Speaker 5: certain times of the year than they gather in massive

563
00:31:29,400 --> 00:31:33,080
Speaker 5: flocks of you know, tens of thousands to millions of birds,

564
00:31:34,560 --> 00:31:37,040
Speaker 5: and so it's just it's a very difficult thing to

565
00:31:37,160 --> 00:31:40,720
Speaker 5: sort of say, this is the absolute number of any species.

566
00:31:41,280 --> 00:31:44,240
Speaker 1: Yeah, you know, it's rough though, over a billion, over

567
00:31:44,280 --> 00:31:46,959
Speaker 1: a billion? Can you I'm going to look this up,

568
00:31:47,000 --> 00:31:49,840
Speaker 1: but just to ask, are you surprised because you would

569
00:31:49,840 --> 00:31:52,720
Speaker 1: think that it would be more than that. Are you

570
00:31:52,760 --> 00:31:54,800
Speaker 1: surprised to hear there are birds that have a global

571
00:31:54,880 --> 00:31:57,320
Speaker 1: that might have a global population, or you just think

572
00:31:57,320 --> 00:32:00,720
Speaker 1: that anyone saying that is overreach.

573
00:32:01,080 --> 00:32:04,720
Speaker 5: Any time that that there's a definitive statement that there

574
00:32:04,760 --> 00:32:08,880
Speaker 5: are x number of species that meet some threshold that's

575
00:32:08,920 --> 00:32:12,360
Speaker 5: in comparison to others is often something where I would

576
00:32:12,440 --> 00:32:16,200
Speaker 5: kind of pause and raise my eyebrows and think about

577
00:32:16,320 --> 00:32:20,560
Speaker 5: just because of the difficulty in understanding that there's there's

578
00:32:20,600 --> 00:32:24,520
Speaker 5: also a lot of systems that may not be heavily covered,

579
00:32:24,560 --> 00:32:26,479
Speaker 5: or where there's a lot of people that are actually

580
00:32:26,480 --> 00:32:29,160
Speaker 5: going and looking at birds, and so just to sort

581
00:32:29,200 --> 00:32:32,720
Speaker 5: of be able to figure that out is a complicated

582
00:32:33,320 --> 00:32:37,480
Speaker 5: you know, it's something that I mean that is difficult

583
00:32:38,000 --> 00:32:41,200
Speaker 5: to do with a level of confidence that you know,

584
00:32:41,240 --> 00:32:43,320
Speaker 5: I'd be comfortable making a statement.

585
00:32:43,000 --> 00:32:44,800
Speaker 1: Like that writing into a book.

586
00:32:45,000 --> 00:32:47,880
Speaker 5: Yeah, I probably wouldn't. I probably wouldn't do that.

587
00:32:48,160 --> 00:32:50,000
Speaker 1: Well, I'm going to follow up with you because we've

588
00:32:50,000 --> 00:32:52,360
Speaker 1: talked about that was even a trivia one of Spencer's

589
00:32:52,400 --> 00:32:53,120
Speaker 1: trivia questions.

590
00:32:53,120 --> 00:32:55,880
Speaker 3: If I remember was maybe I wasn't on that something.

591
00:32:55,680 --> 00:32:57,800
Speaker 1: About that or no, no, I think I maybe suggested

592
00:32:57,800 --> 00:33:01,160
Speaker 1: it to them. I'll get back to you on that.

593
00:33:11,160 --> 00:33:18,360
Speaker 1: With E bird, you feel that E bird where people

594
00:33:18,360 --> 00:33:20,160
Speaker 1: from around the world. How many people use E bird?

595
00:33:21,200 --> 00:33:21,680
Speaker 1: A million?

596
00:33:21,840 --> 00:33:24,280
Speaker 5: There's yeah, there's about a million people who have used

597
00:33:24,320 --> 00:33:27,360
Speaker 5: the bird, and in you know, any year about a

598
00:33:27,440 --> 00:33:28,440
Speaker 5: quarter million people.

599
00:33:28,960 --> 00:33:31,840
Speaker 1: So prior to E bird, and it seems like you

600
00:33:31,840 --> 00:33:37,560
Speaker 1: guys are really optimistic about what you can garner from that.

601
00:33:38,560 --> 00:33:43,040
Speaker 1: Prior to E bird, how was anyone ever taking a

602
00:33:43,080 --> 00:33:45,160
Speaker 1: stab at what's going on with bird population?

603
00:33:45,280 --> 00:33:48,040
Speaker 5: Yeah, that's a really good question. There's a there's a

604
00:33:48,200 --> 00:33:50,200
Speaker 5: survey that's been going on in the US for a

605
00:33:50,200 --> 00:33:53,800
Speaker 5: long a long time called the Breeding Bird Survey, which

606
00:33:53,840 --> 00:34:00,200
Speaker 5: is basically a series of of of points that people

607
00:34:00,200 --> 00:34:03,280
Speaker 5: are doing along roads where they go out every spring.

608
00:34:03,520 --> 00:34:04,120
Speaker 1: And they.

609
00:34:05,720 --> 00:34:10,400
Speaker 5: They are able to infer what's happening with with bird

610
00:34:10,440 --> 00:34:14,520
Speaker 5: populations during the breeding season. And so that's that's one

611
00:34:14,560 --> 00:34:16,480
Speaker 5: way that people have been doing it. One of the

612
00:34:16,520 --> 00:34:19,680
Speaker 5: longest citizen science projects is something called the Christmas Bird

613
00:34:19,680 --> 00:34:22,520
Speaker 5: Count that's been going on for over one hundred years.

614
00:34:22,520 --> 00:34:25,560
Speaker 5: So the idea that you know, citizen scientists and people

615
00:34:25,640 --> 00:34:29,520
Speaker 5: can contribute to science is not a new idea at all.

616
00:34:29,600 --> 00:34:31,839
Speaker 5: I mean, it's something that's been happening for a long time.

617
00:34:31,920 --> 00:34:35,120
Speaker 5: But with the advent of the Internet and the ability

618
00:34:35,160 --> 00:34:39,560
Speaker 5: to exchange information very quickly, it's been a lot easier

619
00:34:39,600 --> 00:34:44,640
Speaker 5: for us to very quickly be able to amass you know,

620
00:34:44,800 --> 00:34:47,879
Speaker 5: over one and a half billion records from all over

621
00:34:47,920 --> 00:34:52,800
Speaker 5: the world, and that scale of large amounts of data

622
00:34:53,200 --> 00:34:56,480
Speaker 5: then allows us to get much more insight than we

623
00:34:56,520 --> 00:34:59,279
Speaker 5: would be able to get with with other methods. And

624
00:34:59,360 --> 00:35:01,880
Speaker 5: one of the things it's exciting now is it's not

625
00:35:01,960 --> 00:35:06,520
Speaker 5: necessarily that we're only using eBird data. We're often integrating

626
00:35:06,560 --> 00:35:11,040
Speaker 5: that data with other data sets as well, so we're

627
00:35:11,080 --> 00:35:13,080
Speaker 5: able to bring in some of the information that Fish

628
00:35:13,080 --> 00:35:15,319
Speaker 5: and Wildlife Service may have gathered and then bring that

629
00:35:15,400 --> 00:35:19,839
Speaker 5: together with information that we have. But it turns out

630
00:35:19,880 --> 00:35:22,000
Speaker 5: one of the things that's a little surprising that people

631
00:35:22,480 --> 00:35:28,320
Speaker 5: may not appreciate is usually people are doing surveys where

632
00:35:28,400 --> 00:35:31,319
Speaker 5: birds are, and so if you're doing a survey for

633
00:35:31,440 --> 00:35:34,479
Speaker 5: bald eagles, there tends to be an emphasis on saying, Okay,

634
00:35:34,560 --> 00:35:36,840
Speaker 5: these are places where eagles are, We're going to do

635
00:35:36,920 --> 00:35:41,840
Speaker 5: my survey where the eagles are. What happens then, is

636
00:35:41,880 --> 00:35:45,719
Speaker 5: if you're trying to extrapolate that across everywhere and you

637
00:35:45,800 --> 00:35:50,520
Speaker 5: have no information on where eagles are not, then the

638
00:35:50,600 --> 00:35:54,480
Speaker 5: models basically fall apart because they you don't have that information.

639
00:35:55,080 --> 00:35:57,960
Speaker 5: eBird ends up being a really good resource for this,

640
00:35:58,080 --> 00:36:01,600
Speaker 5: because we're asking people to record all the birds that

641
00:36:01,640 --> 00:36:05,440
Speaker 5: they're able to detect and identify and then put an

642
00:36:05,480 --> 00:36:09,239
Speaker 5: account for. And that gives us sort of not just

643
00:36:09,360 --> 00:36:12,600
Speaker 5: information about where birds are, but where they're.

644
00:36:12,360 --> 00:36:15,920
Speaker 1: Not because you've asked them to enter all birds exactly.

645
00:36:16,360 --> 00:36:19,239
Speaker 5: And we know that there's differences between people, right, Like

646
00:36:19,640 --> 00:36:22,760
Speaker 5: we know if you go out with Jesse, you're gonna

647
00:36:22,800 --> 00:36:25,520
Speaker 5: see you're going to detect and identify way way more

648
00:36:25,560 --> 00:36:28,279
Speaker 5: birds than if you just you know, go out on

649
00:36:28,320 --> 00:36:30,320
Speaker 5: your own, because she's one of the best bird people

650
00:36:30,360 --> 00:36:31,040
Speaker 5: in the world.

651
00:36:32,200 --> 00:36:34,799
Speaker 1: One of the best. Do you have a life list?

652
00:36:35,400 --> 00:36:35,640
Speaker 5: Yeah?

653
00:36:35,719 --> 00:36:38,560
Speaker 3: Yeah, I've been keeping a lifeless since I was nine. Yeah.

654
00:36:39,000 --> 00:36:40,719
Speaker 1: How many birds are on the life list?

655
00:36:40,960 --> 00:36:43,800
Speaker 3: It's not a really impressive number. It's a couple thousand.

656
00:36:44,200 --> 00:36:46,279
Speaker 3: But I think the thing that we've done this.

657
00:36:46,280 --> 00:36:51,319
Speaker 1: Spit how many are ten there's like one birds.

658
00:36:51,120 --> 00:36:52,560
Speaker 3: Almost made it to ten thousand.

659
00:36:53,600 --> 00:36:55,840
Speaker 1: But yeah, like he throws a lot of money at it.

660
00:36:56,880 --> 00:37:00,279
Speaker 3: Yeah, his life is dedicated to traveling, but we do

661
00:37:00,400 --> 00:37:01,880
Speaker 3: that's kind of fun, is try to figure out how

662
00:37:01,920 --> 00:37:04,640
Speaker 3: many species we can see in twenty four hours. And

663
00:37:04,719 --> 00:37:07,640
Speaker 3: so you know, for example, we went down to Texas

664
00:37:07,640 --> 00:37:12,600
Speaker 3: in April, so the corner leve Morphology actually has a

665
00:37:12,640 --> 00:37:17,920
Speaker 3: birding team where compete. We do compete and but you know,

666
00:37:18,040 --> 00:37:23,480
Speaker 3: conservation Alise wins. We're raising money for conservation, so donors pledge,

667
00:37:23,560 --> 00:37:25,640
Speaker 3: you know, traditionally by the number of species that we

668
00:37:25,680 --> 00:37:28,839
Speaker 3: would see, and we would spend you know, a week

669
00:37:28,880 --> 00:37:31,880
Speaker 3: planning a route to like pick up as many birds

670
00:37:31,920 --> 00:37:34,720
Speaker 3: and then take twenty four hours to run that route

671
00:37:34,760 --> 00:37:38,120
Speaker 3: time down to the minute to see how many birds

672
00:37:38,160 --> 00:37:41,440
Speaker 3: we could areas probably Yeah, so you're you're getting the

673
00:37:41,480 --> 00:37:44,640
Speaker 3: coastal areas you're gonna get you know, the grasslands or

674
00:37:44,640 --> 00:37:47,600
Speaker 3: any habitat you can put on that route. It's kind

675
00:37:47,600 --> 00:37:49,759
Speaker 3: of the goal because you know, different habitats, you're gonna

676
00:37:49,760 --> 00:37:54,520
Speaker 3: get different species and them eithers get And one of

677
00:37:54,520 --> 00:37:56,920
Speaker 3: the other rules is that ninety five percent of the

678
00:37:56,960 --> 00:38:00,359
Speaker 3: species all team members need to see. So if you've

679
00:38:00,400 --> 00:38:02,000
Speaker 3: got five people on the team, you're trying to make

680
00:38:02,040 --> 00:38:05,200
Speaker 3: sure everybody's picking up all the birds all day long,

681
00:38:05,280 --> 00:38:07,600
Speaker 3: so it's not just like one guy, can you know,

682
00:38:07,719 --> 00:38:08,319
Speaker 3: find them all?

683
00:38:08,360 --> 00:38:11,240
Speaker 1: And what's a good twenty four hour period?

684
00:38:12,040 --> 00:38:15,560
Speaker 3: A good total anywhere, I mean, you know, two hundred

685
00:38:15,640 --> 00:38:18,360
Speaker 3: or something like that is something people work towards in

686
00:38:18,440 --> 00:38:22,040
Speaker 3: most regions. But there's one day in Texas we pulled

687
00:38:22,040 --> 00:38:24,840
Speaker 3: off two hundred and ninety four species and set the

688
00:38:24,880 --> 00:38:26,560
Speaker 3: record for North America.

689
00:38:26,600 --> 00:38:31,960
Speaker 1: That's all yeah, yeah, humming to ninety four yeah, in

690
00:38:32,000 --> 00:38:32,840
Speaker 1: one day in Texas.

691
00:38:32,880 --> 00:38:35,799
Speaker 3: One day in Texas on a really epic migration day.

692
00:38:35,880 --> 00:38:38,359
Speaker 3: So we had watched the weather. We knew that their

693
00:38:38,480 --> 00:38:41,840
Speaker 3: you know front was coming in, which was dropping birds

694
00:38:41,840 --> 00:38:45,680
Speaker 3: on the Texas coast. But the conditions were really good

695
00:38:45,680 --> 00:38:48,160
Speaker 3: that year. We had some lingering waterfall that wasn't expected

696
00:38:48,200 --> 00:38:50,560
Speaker 3: to be in the region, and then just a lot

697
00:38:50,600 --> 00:38:52,560
Speaker 3: of practice. But on those days you get to put

698
00:38:52,560 --> 00:38:55,359
Speaker 3: your birding skills to the test by like finding things

699
00:38:55,400 --> 00:38:57,640
Speaker 3: that weren't expected on the route and just being the

700
00:38:57,640 --> 00:39:00,640
Speaker 3: first guy to like pick the merlin. Flying over unexpected

701
00:39:00,640 --> 00:39:02,160
Speaker 3: paragrin is part of them.

702
00:39:02,400 --> 00:39:04,359
Speaker 5: It's really similar to hunting in a lot of ways,

703
00:39:04,400 --> 00:39:06,239
Speaker 5: because what you do is you know that you spend

704
00:39:06,280 --> 00:39:09,840
Speaker 5: this time scouting and understanding exactly what you know. In

705
00:39:09,880 --> 00:39:12,359
Speaker 5: this case, about three hundred species of birds are doing

706
00:39:12,440 --> 00:39:15,040
Speaker 5: sometimes to the level of that individual, Like if it's

707
00:39:15,040 --> 00:39:17,640
Speaker 5: something that's rare in a region, you know, maybe there's

708
00:39:17,680 --> 00:39:21,000
Speaker 5: a cinnamon teal that's that's lingering that wouldn't usually be

709
00:39:21,040 --> 00:39:23,879
Speaker 5: in that area. You know, it's not just that it's there.

710
00:39:24,000 --> 00:39:25,879
Speaker 5: You kind of need to figure out, well, like when

711
00:39:26,000 --> 00:39:28,560
Speaker 5: is it actually like swimming out where you can see

712
00:39:28,600 --> 00:39:31,200
Speaker 5: it versus hiding in the in the reeds. And so

713
00:39:31,719 --> 00:39:34,040
Speaker 5: you have people that are basically trying to get these

714
00:39:34,200 --> 00:39:39,440
Speaker 5: very intimate understandings of exactly what's happening sometimes with individual birds.

715
00:39:40,280 --> 00:39:42,800
Speaker 5: And then there's also kind of the magic that happens,

716
00:39:42,960 --> 00:39:46,440
Speaker 5: you know, like like when you're hunting that's just completely unexpected,

717
00:39:46,560 --> 00:39:50,000
Speaker 5: like something that you didn't know was there happens. And

718
00:39:50,040 --> 00:39:53,240
Speaker 5: so there's this element where you sort of know there's

719
00:39:53,239 --> 00:39:56,040
Speaker 5: some things about these birds at a place that that

720
00:39:56,200 --> 00:39:58,960
Speaker 5: you're you're trying to put those birds together on a route.

721
00:39:59,320 --> 00:40:02,960
Speaker 5: But then there's also you know that you can create

722
00:40:03,360 --> 00:40:07,440
Speaker 5: almost opportunities around dusk and down in particular where you

723
00:40:07,480 --> 00:40:09,920
Speaker 5: know things are moving more. You know that there is

724
00:40:09,960 --> 00:40:12,719
Speaker 5: these different factors, and so you're trying to sort of

725
00:40:12,840 --> 00:40:16,279
Speaker 5: fit these things together and pull together a route that

726
00:40:16,800 --> 00:40:18,640
Speaker 5: you know, you see how many birds you can see?

727
00:40:18,640 --> 00:40:20,640
Speaker 5: You should we should do one of these together, see

728
00:40:20,640 --> 00:40:22,360
Speaker 5: if we can have you see two hundred spieces of

729
00:40:22,400 --> 00:40:23,080
Speaker 5: birds in a day.

730
00:40:23,120 --> 00:40:25,279
Speaker 1: I'd like it, But I'm trying to think of some

731
00:40:25,360 --> 00:40:31,480
Speaker 1: things have you had? Do you have you gotten all ptarmigans.

732
00:40:30,480 --> 00:40:31,600
Speaker 3: Like in a lifetime?

733
00:40:31,719 --> 00:40:34,879
Speaker 5: Yeah, okay we did in our Alaska big day.

734
00:40:35,520 --> 00:40:37,120
Speaker 3: Yeah yeah we uh.

735
00:40:37,360 --> 00:40:40,239
Speaker 1: You got all on one day. I know friends that

736
00:40:40,280 --> 00:40:42,120
Speaker 1: have gotten gotten one on one day.

737
00:40:42,640 --> 00:40:43,120
Speaker 3: That's awesome.

738
00:40:43,360 --> 00:40:48,560
Speaker 1: Collected. Yeah, does anybody is that still a thing like

739
00:40:48,600 --> 00:40:50,640
Speaker 1: when people used to go out with their following piece

740
00:40:52,760 --> 00:40:54,000
Speaker 1: and like shotgun birds.

741
00:40:54,440 --> 00:40:57,640
Speaker 3: Yeah, that's still there's a limited amount of scientific collecting

742
00:40:57,680 --> 00:40:59,120
Speaker 3: that goes on a limited amount.

743
00:40:59,280 --> 00:41:01,640
Speaker 1: Yeah, that would be a circumstance. Would I still like

744
00:41:01,680 --> 00:41:04,640
Speaker 1: an acceptable thing to do? Like back in the Audubon

745
00:41:04,840 --> 00:41:06,600
Speaker 1: or Darwin days and they just run around with their

746
00:41:06,600 --> 00:41:08,440
Speaker 1: fowling piece and like collect birds.

747
00:41:08,520 --> 00:41:12,240
Speaker 3: So these days museums tend to pick up more salvage birds.

748
00:41:12,400 --> 00:41:16,200
Speaker 3: So birds hit a window roadkill those are going to museums.

749
00:41:17,120 --> 00:41:19,520
Speaker 3: But there are certain species where there's some really interesting

750
00:41:19,600 --> 00:41:22,640
Speaker 3: questions around what is driving the changes in their population.

751
00:41:23,320 --> 00:41:26,319
Speaker 3: Where you know, if you collect you know, ten individuals

752
00:41:26,400 --> 00:41:29,360
Speaker 3: or even something like that can really start to unlock

753
00:41:29,360 --> 00:41:32,480
Speaker 3: those questions of how those populations are changing and why.

754
00:41:32,880 --> 00:41:35,520
Speaker 1: Have you ever been out with someone doing that. Yeah, yeah,

755
00:41:36,000 --> 00:41:37,360
Speaker 1: it feels like real kind of naughty.

756
00:41:37,400 --> 00:41:40,279
Speaker 3: I feel like it kind of does, because it's like

757
00:41:40,480 --> 00:41:43,320
Speaker 3: you've got this very specific permit for just a little

758
00:41:43,400 --> 00:41:46,520
Speaker 3: bit of sampling, and you kind of know in the

759
00:41:46,520 --> 00:41:48,800
Speaker 3: back of your head, you know, it's been hundreds of

760
00:41:48,880 --> 00:41:51,360
Speaker 3: years in some of those species since they've been targeted

761
00:41:51,360 --> 00:41:51,759
Speaker 3: in that way.

762
00:41:51,800 --> 00:41:53,040
Speaker 1: So oh yeah, for sure.

763
00:41:53,280 --> 00:41:55,960
Speaker 2: Several years ago, when I was doing with Masters, we

764
00:41:56,280 --> 00:41:57,640
Speaker 2: had a permit to kill.

765
00:41:57,480 --> 00:42:01,240
Speaker 1: Brown pelicans, uh huh.

766
00:41:59,760 --> 00:42:03,439
Speaker 2: And that was pretty uh, pretty intell. There's some big bird.

767
00:42:03,880 --> 00:42:05,720
Speaker 1: And they were collecting them for what purpose?

768
00:42:05,800 --> 00:42:10,200
Speaker 2: Collecting him for to look for environmental toxins and so

769
00:42:10,200 --> 00:42:11,680
Speaker 2: they're still toxins. So they would take you know, a

770
00:42:11,719 --> 00:42:14,680
Speaker 2: little piece of breast tissue, little liver, and some brain tissue.

771
00:42:15,360 --> 00:42:17,040
Speaker 2: The only way to get that is to you know,

772
00:42:17,280 --> 00:42:18,200
Speaker 2: you know, go shoot them.

773
00:42:18,200 --> 00:42:23,080
Speaker 1: I have to do like some mortal Yeah. What uh

774
00:42:23,440 --> 00:42:28,080
Speaker 1: what birds right now? Or like in the US, what

775
00:42:28,200 --> 00:42:31,719
Speaker 1: classifications of birds are most imperiled. You hear a lot

776
00:42:31,760 --> 00:42:35,880
Speaker 1: about grassland, which is you know, I guess obvious because

777
00:42:35,880 --> 00:42:40,520
Speaker 1: of loss of grassland habitat, But are there classifications of

778
00:42:40,520 --> 00:42:43,880
Speaker 1: birds where it's just not like clearly understood things that

779
00:42:43,920 --> 00:42:48,480
Speaker 1: you guys look at that you're that you're puzzled about.

780
00:42:49,160 --> 00:42:55,600
Speaker 2: Shore birds are are declining pretty rapidly. We have We've

781
00:42:55,600 --> 00:42:57,800
Speaker 2: done some studies on shore birds in the Pacific Flyway

782
00:42:58,200 --> 00:43:02,960
Speaker 2: and it's it's almost all bad news there. And it's

783
00:43:03,040 --> 00:43:06,719
Speaker 2: hard to know why. It could be loss of habitat

784
00:43:07,000 --> 00:43:10,719
Speaker 2: on the overwintering grounds that in South America that is

785
00:43:10,760 --> 00:43:13,960
Speaker 2: a definite possibility, crowding on the beaches, things like that,

786
00:43:14,880 --> 00:43:17,440
Speaker 2: and it could be you know, breeding habitat up. You

787
00:43:17,440 --> 00:43:18,960
Speaker 2: know a lot of these shore birds breed up in

788
00:43:19,000 --> 00:43:23,799
Speaker 2: the boreal regions and that's you know, being affected by

789
00:43:23,800 --> 00:43:27,839
Speaker 2: climate change things like that. But shore birds, I would say,

790
00:43:27,880 --> 00:43:31,280
Speaker 2: along with the grassland birds are pretty imperiled.

791
00:43:31,520 --> 00:43:34,319
Speaker 1: The ones that are more alarming. Yep. Do you know

792
00:43:34,560 --> 00:43:41,360
Speaker 1: there was a there was a ten thousand year period

793
00:43:42,000 --> 00:43:47,560
Speaker 1: when basically from the end of the place has scene extinctions.

794
00:43:48,560 --> 00:43:50,960
Speaker 1: It was a ten thousand year period where only one

795
00:43:51,239 --> 00:43:54,840
Speaker 1: creature that they know about and what is now the

796
00:43:54,920 --> 00:43:59,719
Speaker 1: US one extinct. You know, some like coastal goose in California.

797
00:44:01,520 --> 00:44:02,880
Speaker 2: Uh.

798
00:44:02,920 --> 00:44:05,160
Speaker 1: And then we had all you know, with with European

799
00:44:05,200 --> 00:44:07,160
Speaker 1: arrival in the New World. We then launched in this

800
00:44:07,200 --> 00:44:12,480
Speaker 1: whole other epic of extinctions. But uh, do you think

801
00:44:12,560 --> 00:44:15,880
Speaker 1: things like do you see things that that would suggest

802
00:44:15,960 --> 00:44:20,560
Speaker 1: that we're that we're headed for more extinctions in the

803
00:44:20,680 --> 00:44:23,359
Speaker 1: US with bird species? Oh?

804
00:44:23,440 --> 00:44:27,000
Speaker 2: Man, that's that's it's hard to say. You know, there

805
00:44:27,120 --> 00:44:31,120
Speaker 2: there's a lot of a lot of species for which

806
00:44:31,160 --> 00:44:33,440
Speaker 2: we we do these trends, these these you know, long

807
00:44:33,520 --> 00:44:38,040
Speaker 2: term trends that are declining. You know, very few are

808
00:44:38,200 --> 00:44:43,359
Speaker 2: are increasing. Uh, And it's uh, you know it it

809
00:44:43,400 --> 00:44:46,120
Speaker 2: does seem like more bad news than good, that's for sure.

810
00:44:47,520 --> 00:44:50,200
Speaker 2: You know, there are we we see. So one of

811
00:44:50,239 --> 00:44:52,400
Speaker 2: the things you see in uh, you know, when you

812
00:44:52,480 --> 00:44:55,600
Speaker 2: do these these studies on population trends is that you'll

813
00:44:55,640 --> 00:44:59,759
Speaker 2: see that it's not all decline or all growth. They'll

814
00:44:59,760 --> 00:45:07,680
Speaker 2: be you know, it's spatially you know, different the declines

815
00:45:07,719 --> 00:45:09,680
Speaker 2: and the growth. So they'll be growing in one region

816
00:45:09,960 --> 00:45:15,040
Speaker 2: and declining in another. You know, you maybe that's evidence

817
00:45:15,080 --> 00:45:18,319
Speaker 2: of a range shift. Maybe they're declining in one region

818
00:45:18,400 --> 00:45:20,560
Speaker 2: and growing in another, not because they're dying over here,

819
00:45:20,600 --> 00:45:22,440
Speaker 2: but because they're moving from one place to the other.

820
00:45:22,520 --> 00:45:25,520
Speaker 2: So you see more over here. So sometimes it's hard

821
00:45:25,520 --> 00:45:28,439
Speaker 2: to tease that apart just looking at this population data

822
00:45:29,520 --> 00:45:32,680
Speaker 2: and then of course things that are you know, super

823
00:45:32,719 --> 00:45:34,759
Speaker 2: reliant on people like snow geese. We looked at snow

824
00:45:34,760 --> 00:45:39,440
Speaker 2: geese earlier, you know, the white fronted geese. Those populations

825
00:45:39,480 --> 00:45:43,280
Speaker 2: are exploding because of farms. They can exploit farms things

826
00:45:43,320 --> 00:45:49,360
Speaker 2: like that, whereas other waterfowl aren't doing so well, you know.

827
00:45:49,440 --> 00:45:55,080
Speaker 2: So it's it's hard to say like that we're heading

828
00:45:55,120 --> 00:46:02,279
Speaker 2: towards another mass extinction, but more birds that not are

829
00:46:02,320 --> 00:46:03,960
Speaker 2: definitely in decline right now.

830
00:46:04,120 --> 00:46:09,759
Speaker 1: Yeah, what uh if you when when you hear about client.

831
00:46:09,840 --> 00:46:11,600
Speaker 1: You guys have mentioned climate change a couple of times,

832
00:46:12,719 --> 00:46:16,279
Speaker 1: and it's easy to picture how things can lose from

833
00:46:16,320 --> 00:46:19,400
Speaker 1: climate changes because shifting habitats, and it takes them a

834
00:46:19,400 --> 00:46:22,759
Speaker 1: long time to adjust to shifting habitats. But does it

835
00:46:23,280 --> 00:46:26,759
Speaker 1: wouldn't it have to be that? I mean, there's got

836
00:46:26,880 --> 00:46:30,360
Speaker 1: to be like birds who are having new area opened

837
00:46:30,400 --> 00:46:34,319
Speaker 1: up to them, Like who's on the winning end? I mean,

838
00:46:34,320 --> 00:46:37,120
Speaker 1: I imagine you know, you talk about all these birds

839
00:46:37,120 --> 00:46:39,719
Speaker 1: that that have woron out from human activity, means like

840
00:46:39,800 --> 00:46:44,960
Speaker 1: Canada geese have really done well thanks to humans. English

841
00:46:44,960 --> 00:46:48,960
Speaker 1: sparrows have done really well thanks to humans starlings, crows

842
00:46:49,000 --> 00:46:53,120
Speaker 1: have done well thanks to humans. Are there things that

843
00:46:53,120 --> 00:46:56,480
Speaker 1: that you see that you'd see on the increase because

844
00:46:56,560 --> 00:46:58,839
Speaker 1: there used to be areas that that that they had

845
00:46:58,840 --> 00:47:02,319
Speaker 1: a northern limit that's opening up to them. Yeah.

846
00:47:02,360 --> 00:47:05,799
Speaker 2: I think northern mockingbird is one that has pushed north

847
00:47:05,840 --> 00:47:08,640
Speaker 2: in the last twenty years or so, and it's it's

848
00:47:08,760 --> 00:47:12,600
Speaker 2: likely due to you know, it can now tolerate what's

849
00:47:12,640 --> 00:47:16,000
Speaker 2: going on further north than than it's previous range limit, So.

850
00:47:16,000 --> 00:47:18,640
Speaker 1: It's opening up habitat. Yeah. Yeah.

851
00:47:19,080 --> 00:47:21,239
Speaker 5: Part of it also is that it's it's often not

852
00:47:21,440 --> 00:47:26,239
Speaker 5: just climate change in isolation, but also other human land use,

853
00:47:26,480 --> 00:47:29,840
Speaker 5: so that the what what can happen is when you

854
00:47:29,960 --> 00:47:34,200
Speaker 5: have sort of changes in climate and the connectivity between

855
00:47:34,320 --> 00:47:37,640
Speaker 5: habitat has been altered and so that there isn't that

856
00:47:37,680 --> 00:47:40,919
Speaker 5: connectivity that there may have that there may have once been.

857
00:47:42,719 --> 00:47:47,760
Speaker 5: That creates a different type of problem for for birds

858
00:47:47,760 --> 00:47:51,040
Speaker 5: and other and particularly less mobile animals to be able

859
00:47:51,040 --> 00:47:52,120
Speaker 5: to respond.

860
00:47:51,640 --> 00:47:58,000
Speaker 1: To explain that, like we give give that in terms

861
00:47:58,000 --> 00:47:59,400
Speaker 1: of a particular bird.

862
00:48:00,080 --> 00:48:04,279
Speaker 5: Yeah, So if you think about me, let me think

863
00:48:04,560 --> 00:48:08,239
Speaker 5: if I can come up with something. If you're a

864
00:48:08,320 --> 00:48:11,200
Speaker 5: grassland bird, I'll be sort of general, so it applies

865
00:48:11,760 --> 00:48:14,200
Speaker 5: more than sort of any specifically. If you're a grassland

866
00:48:14,200 --> 00:48:20,520
Speaker 5: bird and you are things are getting warmer, so generally,

867
00:48:20,560 --> 00:48:22,880
Speaker 5: what would happen is you would be shifting to the

868
00:48:22,920 --> 00:48:26,440
Speaker 5: north and you may be doing pretty well in things

869
00:48:26,440 --> 00:48:30,000
Speaker 5: like Pawnee National Grasslands or Commanche National Grasslands or some

870
00:48:30,040 --> 00:48:33,040
Speaker 5: of these different grasslands that have been set aside. What

871
00:48:33,200 --> 00:48:38,080
Speaker 5: happens is as as things become more warmer and warmer,

872
00:48:39,160 --> 00:48:42,279
Speaker 5: that habitat in that place may not be right for

873
00:48:42,360 --> 00:48:46,560
Speaker 5: them anymore. You always have some percentage of birds that

874
00:48:46,680 --> 00:48:50,440
Speaker 5: are are quote unquote making mistakes maybe or going to

875
00:48:50,520 --> 00:48:55,359
Speaker 5: places where they wandering around to varying degrees, and it

876
00:48:55,440 --> 00:48:58,480
Speaker 5: really varies depending on the species the degree to which

877
00:48:58,520 --> 00:49:02,760
Speaker 5: that happens. What happens is if there are fewer places

878
00:49:02,880 --> 00:49:06,000
Speaker 5: nearby for those birds to find, if those are all

879
00:49:06,560 --> 00:49:10,719
Speaker 5: corn and soybean farms, now they may not disperse to

880
00:49:10,800 --> 00:49:14,560
Speaker 5: another area that has sort of the appropriate habitat that

881
00:49:14,640 --> 00:49:17,120
Speaker 5: you might that you might move to. So it might

882
00:49:17,200 --> 00:49:20,480
Speaker 5: be like, you know, maybe there is this other habitat

883
00:49:20,680 --> 00:49:23,759
Speaker 5: that would be you know, work out really well for them,

884
00:49:24,239 --> 00:49:26,319
Speaker 5: but they have to get through a whole bunch of

885
00:49:26,360 --> 00:49:28,840
Speaker 5: other stuff in order to reach there, and there just

886
00:49:28,880 --> 00:49:32,240
Speaker 5: aren't enough individuals that would sort of quote unquote figure

887
00:49:32,280 --> 00:49:34,400
Speaker 5: out that this other place exists.

888
00:49:34,520 --> 00:49:39,160
Speaker 1: Yeah, I got you. What generally is, do you guys

889
00:49:39,200 --> 00:49:40,319
Speaker 1: look at rough grouse much?

890
00:49:42,320 --> 00:49:43,880
Speaker 5: Do we have rough grouse on there?

891
00:49:44,920 --> 00:49:48,840
Speaker 1: Because here's the thing that just that it's one of

892
00:49:48,880 --> 00:49:52,160
Speaker 1: those kind of frustrating things where you have this bird

893
00:49:52,400 --> 00:49:57,840
Speaker 1: it's widely recognized that or the bob white quail, it's

894
00:49:58,120 --> 00:50:02,120
Speaker 1: widely understood that it's just tribution is shrinking. States that

895
00:50:02,200 --> 00:50:04,839
Speaker 1: used to have great quail hunting aren't having it now.

896
00:50:05,000 --> 00:50:07,279
Speaker 1: States that used to have great rough grouse hunting aren't

897
00:50:07,320 --> 00:50:09,919
Speaker 1: having it now. And you talk to people in Pennsylvania

898
00:50:09,960 --> 00:50:13,680
Speaker 1: about it, right, And then when you ask someone what's

899
00:50:13,719 --> 00:50:18,399
Speaker 1: going on? And I'm not saying people should clean it up,

900
00:50:18,600 --> 00:50:20,319
Speaker 1: but you say what's going on, it's like it's too

901
00:50:20,360 --> 00:50:24,279
Speaker 1: many things. There's so many things that's unknown. You know,

902
00:50:26,680 --> 00:50:30,040
Speaker 1: is there any way to kind of like tackle with

903
00:50:30,239 --> 00:50:34,000
Speaker 1: rough grouse? For instance, between what you hear about not

904
00:50:34,360 --> 00:50:39,160
Speaker 1: is it as what's the mosquito born pathogen that seems

905
00:50:39,200 --> 00:50:44,759
Speaker 1: to kill them? West Nile kills a lot habitat right,

906
00:50:44,840 --> 00:50:46,719
Speaker 1: And you get into quail, And some people will tell

907
00:50:46,760 --> 00:50:53,960
Speaker 1: you fire ants habitat destruction. Whatever you know, is that

908
00:50:53,960 --> 00:50:55,680
Speaker 1: a thing you guys can speak to at all with

909
00:50:55,760 --> 00:50:57,919
Speaker 1: some of these birds that just have bad news after

910
00:50:58,000 --> 00:51:00,799
Speaker 1: bad news after bad news, and it's like death by

911
00:51:00,840 --> 00:51:01,760
Speaker 1: a thousand cuts.

912
00:51:02,120 --> 00:51:04,720
Speaker 5: Yeah, I mean, that's exactly right. I mean, and usually,

913
00:51:05,520 --> 00:51:08,600
Speaker 5: at least in the US, and I think broadly across

914
00:51:08,640 --> 00:51:14,040
Speaker 5: the world, loss of habitat or changes in habitat tend

915
00:51:14,040 --> 00:51:18,560
Speaker 5: to be the major driver for loss of number, either

916
00:51:18,600 --> 00:51:24,960
Speaker 5: abundance or in rain shifts. Then you have additive effects,

917
00:51:25,239 --> 00:51:29,480
Speaker 5: so as forests in the east are maturing, Basically what

918
00:51:29,520 --> 00:51:32,520
Speaker 5: you're having is a lot of loss of that early

919
00:51:32,600 --> 00:51:36,520
Speaker 5: successional habitats that things like American woodcock and ruffed grouse

920
00:51:36,520 --> 00:51:40,480
Speaker 5: are using. And there's also a lot of need to

921
00:51:40,520 --> 00:51:45,080
Speaker 5: have connectivity between different types of habitats that you may

922
00:51:45,120 --> 00:51:46,839
Speaker 5: not have to the same degree.

923
00:51:46,560 --> 00:51:47,680
Speaker 1: That you once had.

924
00:51:47,719 --> 00:51:50,840
Speaker 5: And so what happens is at different stages in a

925
00:51:50,840 --> 00:51:56,560
Speaker 5: bird's life they need different types of habitat. The reality

926
00:51:56,680 --> 00:52:01,080
Speaker 5: is is that that information we bill don't have that

927
00:52:01,400 --> 00:52:06,000
Speaker 5: for most species, it's really hard to understand exactly what

928
00:52:06,200 --> 00:52:10,680
Speaker 5: birds need and then how that varies across both space

929
00:52:10,960 --> 00:52:13,560
Speaker 5: and time. Most studies tend to be done when birds

930
00:52:13,560 --> 00:52:16,040
Speaker 5: are most detectable, so you know, you might be able

931
00:52:16,080 --> 00:52:19,360
Speaker 5: to go out and do surveys for one rough grouse

932
00:52:19,440 --> 00:52:22,520
Speaker 5: or drumming. You understand a lot about the needs for

933
00:52:22,640 --> 00:52:26,200
Speaker 5: what rough grouse needs when they're displaying. But then you

934
00:52:26,200 --> 00:52:28,839
Speaker 5: you know, to go and put you know, radio, whether

935
00:52:28,880 --> 00:52:33,240
Speaker 5: it's radio telemetry or newer methods of monitoring individual birds

936
00:52:33,760 --> 00:52:40,480
Speaker 5: is a much more expensive, time intensive to really understand

937
00:52:40,520 --> 00:52:42,839
Speaker 5: what's happening. And then there's a question of to what

938
00:52:42,960 --> 00:52:46,520
Speaker 5: extent does what you found out in that study area

939
00:52:46,600 --> 00:52:50,839
Speaker 5: with maybe thirteen fifteen birds apply to other areas. Are

940
00:52:50,880 --> 00:52:53,080
Speaker 5: they using that same thing or is it that that's

941
00:52:53,120 --> 00:52:58,720
Speaker 5: already an area that the birds are limited and maybe

942
00:52:58,760 --> 00:53:01,400
Speaker 5: not doing exactly what they want, but they're doing something

943
00:53:01,440 --> 00:53:03,800
Speaker 5: that they can. And so that's kind of the idea

944
00:53:04,800 --> 00:53:08,360
Speaker 5: behind Ebert is to get enough people that are in

945
00:53:08,520 --> 00:53:11,239
Speaker 5: every habitat every time of the year that we can

946
00:53:11,360 --> 00:53:15,240
Speaker 5: start to understand sort of how these things are playing

947
00:53:15,280 --> 00:53:20,399
Speaker 5: out and how birds are responding, and then figuring out

948
00:53:20,400 --> 00:53:23,160
Speaker 5: how to link that with other types of data and

949
00:53:23,239 --> 00:53:26,239
Speaker 5: these integrated population models, which is really what Warren does

950
00:53:26,280 --> 00:53:26,560
Speaker 5: a lot.

951
00:53:27,200 --> 00:53:29,920
Speaker 1: But when people are using Ebert, are you asking them

952
00:53:29,920 --> 00:53:32,279
Speaker 1: about is it a juvenile or not a juvenile? Are

953
00:53:32,280 --> 00:53:33,520
Speaker 1: you just going by the time of year.

954
00:53:34,040 --> 00:53:37,480
Speaker 5: So we're mostly using the time of year. But people

955
00:53:37,880 --> 00:53:40,439
Speaker 5: are able, you know, they can they can take photographs

956
00:53:41,000 --> 00:53:44,360
Speaker 5: upload those photographs in the McCauley library. So there's researchers

957
00:53:44,400 --> 00:53:46,759
Speaker 5: that have used the photos in the McCauley library to

958
00:53:46,840 --> 00:53:50,320
Speaker 5: look at things like what are the percentage of juveniles,

959
00:53:50,320 --> 00:53:54,200
Speaker 5: how does it compare to the year before understanding that

960
00:53:54,840 --> 00:53:59,040
Speaker 5: the idea is that the relative percentages of what people

961
00:53:59,040 --> 00:54:01,640
Speaker 5: are photographing for one year to the next should probably

962
00:54:01,680 --> 00:54:04,640
Speaker 5: be pretty similar. There shouldn't be a reason that one

963
00:54:04,719 --> 00:54:07,960
Speaker 5: year people are really deciding to just photograph juveniles, in

964
00:54:08,000 --> 00:54:10,279
Speaker 5: the next year they're just photographing adult There should be

965
00:54:10,320 --> 00:54:13,640
Speaker 5: some ratio that's that's consistent, and so trying to look

966
00:54:13,680 --> 00:54:15,920
Speaker 5: at that over time is something that people can do.

967
00:54:16,000 --> 00:54:18,480
Speaker 5: And then people that are interested. There is a there's

968
00:54:18,520 --> 00:54:21,040
Speaker 5: an age and sex scrid where people can put how

969
00:54:21,040 --> 00:54:24,360
Speaker 5: many adult males, how many adult females, how many juveniles.

970
00:54:24,800 --> 00:54:27,760
Speaker 5: There's breeding codes that people can enter, so they can

971
00:54:28,080 --> 00:54:30,080
Speaker 5: they can say that this is this is an area

972
00:54:30,120 --> 00:54:33,200
Speaker 5: where a bird had a brood of young that were

973
00:54:33,200 --> 00:54:36,080
Speaker 5: with it. There's a lot of different information that people

974
00:54:36,120 --> 00:54:39,200
Speaker 5: can put in beyond just sort of the species and

975
00:54:39,440 --> 00:54:40,920
Speaker 5: number of individuals, And.

976
00:54:40,880 --> 00:54:45,840
Speaker 2: There are specific surveys run through ebirds. So various states

977
00:54:45,880 --> 00:54:49,120
Speaker 2: will have their breeding bird surveys done through ebirds, so

978
00:54:49,160 --> 00:54:51,879
Speaker 2: they will be putting in you know, we saw black

979
00:54:51,920 --> 00:54:55,600
Speaker 2: throat of blue warbler breeding on a nest, you know,

980
00:54:56,000 --> 00:54:58,960
Speaker 2: or something, we saw this bird with chicks, things like that,

981
00:54:59,560 --> 00:55:03,400
Speaker 2: and those individual projects that use Ebert for that data

982
00:55:03,440 --> 00:55:06,480
Speaker 2: will absolutely use that stuff to uh, you know, in

983
00:55:06,520 --> 00:55:07,480
Speaker 2: their in their studies.

984
00:55:08,200 --> 00:55:11,160
Speaker 1: There's an annual bird there's an annual raptor count in

985
00:55:11,200 --> 00:55:14,239
Speaker 1: a mountain range near our house. I can't remember what

986
00:55:14,320 --> 00:55:17,080
Speaker 1: date it is, but it's a migratory raptor count. My

987
00:55:17,160 --> 00:55:20,320
Speaker 1: kids have gone out to participate in it. So that's

988
00:55:20,360 --> 00:55:22,640
Speaker 1: the kind of thing that gives you like an annual snapshot,

989
00:55:22,840 --> 00:55:24,560
Speaker 1: and they probably measure some kind of like thing like

990
00:55:24,640 --> 00:55:28,080
Speaker 1: effort yep, you know. Uh, I was asking you guys before,

991
00:55:29,160 --> 00:55:32,560
Speaker 1: is there ever a birder who's so bad they get

992
00:55:32,600 --> 00:55:34,240
Speaker 1: eighty six off eBird?

993
00:55:35,760 --> 00:55:38,160
Speaker 2: If I haven't gotten booted, then I don't know if

994
00:55:38,160 --> 00:55:39,200
Speaker 2: anybody will.

995
00:55:39,120 --> 00:55:41,440
Speaker 1: Mean You're like, there's no way you saw that. You know,

996
00:55:42,200 --> 00:55:44,319
Speaker 1: there's no cock of the rock. You can't keep saying

997
00:55:44,360 --> 00:55:44,920
Speaker 1: you saw that.

998
00:55:45,600 --> 00:55:52,040
Speaker 5: There's no cock of the rocks in Ithaca. Usually it's

999
00:55:52,200 --> 00:55:56,640
Speaker 5: very very rare that we end up booting somebody, and

1000
00:55:56,760 --> 00:55:59,879
Speaker 5: usually it's it's for the same reasons that somebody might get,

1001
00:56:00,120 --> 00:56:03,040
Speaker 5: you know, booted from a social media site. Right, it's

1002
00:56:03,120 --> 00:56:07,680
Speaker 5: not that it's an intentional error that somebody's made if

1003
00:56:07,680 --> 00:56:10,040
Speaker 5: they're trying to you know, if somebody says that they

1004
00:56:10,080 --> 00:56:12,319
Speaker 5: saw a rare bird and then they take a photograph

1005
00:56:12,360 --> 00:56:15,200
Speaker 5: that they snatched off the Internet of that bird at

1006
00:56:15,239 --> 00:56:18,160
Speaker 5: a different place, you know, in that case, you know,

1007
00:56:18,200 --> 00:56:20,360
Speaker 5: we'll figure it out. If it's a kid or somebody

1008
00:56:20,400 --> 00:56:23,799
Speaker 5: that's kind of screwing around and just messing around, but

1009
00:56:24,200 --> 00:56:27,719
Speaker 5: that's really really rare. Usually the mistakes that people are

1010
00:56:27,760 --> 00:56:30,600
Speaker 5: making are kind of honest mistakes, and we have we

1011
00:56:30,640 --> 00:56:34,440
Speaker 5: have methods that allow us to just understand the differences

1012
00:56:35,200 --> 00:56:38,520
Speaker 5: and understand that people are learning. And what people sometimes

1013
00:56:38,520 --> 00:56:43,960
Speaker 5: forget is these same challenges apply to basically any professional

1014
00:56:44,000 --> 00:56:47,520
Speaker 5: survey too. You're going to have people that maybe they

1015
00:56:47,560 --> 00:56:51,000
Speaker 5: all are experts, but for whatever reason. People might be

1016
00:56:51,040 --> 00:56:55,440
Speaker 5: more tuned in and really know certain vocalizations of certain birds.

1017
00:56:55,480 --> 00:56:59,239
Speaker 5: They may focus more on forests, they may understand the

1018
00:56:59,239 --> 00:57:03,120
Speaker 5: grasslands a little bit more. And so we were able

1019
00:57:03,160 --> 00:57:05,960
Speaker 5: to because there's one and a half billion records, we

1020
00:57:06,000 --> 00:57:09,080
Speaker 5: can start to understand those differences and not say, well, dude,

1021
00:57:09,080 --> 00:57:10,120
Speaker 5: this guy is an idiot.

1022
00:57:10,200 --> 00:57:13,040
Speaker 1: We had to get them off right.

1023
00:57:13,440 --> 00:57:16,760
Speaker 5: We can still be a pretty inclusive place where just

1024
00:57:16,800 --> 00:57:21,040
Speaker 5: about everybody can participate, and then we can have different

1025
00:57:21,200 --> 00:57:24,400
Speaker 5: weights in terms of how we use that data.

1026
00:57:25,840 --> 00:57:32,560
Speaker 2: Yeah, we learn about the individual Ebert users by you know,

1027
00:57:32,640 --> 00:57:35,439
Speaker 2: the the time it takes them to accumulate so many

1028
00:57:35,480 --> 00:57:39,720
Speaker 2: species in a particular habitat in a particular region, and

1029
00:57:39,800 --> 00:57:42,400
Speaker 2: we can use that in these models, just like we

1030
00:57:42,440 --> 00:57:47,320
Speaker 2: do effort to determine, you know, the the the expertise

1031
00:57:47,440 --> 00:57:50,160
Speaker 2: essentially of an individual eburter.

1032
00:57:51,000 --> 00:57:54,040
Speaker 1: So you can tier it out to be you can

1033
00:57:54,080 --> 00:57:56,360
Speaker 1: tear it out to be people who are like high

1034
00:57:56,360 --> 00:58:02,240
Speaker 1: consistent users scoring a lot, and then like audit those

1035
00:58:02,240 --> 00:58:04,560
Speaker 1: individuals in somewhere and not audit them, but go and

1036
00:58:04,600 --> 00:58:05,560
Speaker 1: look at what they're up to.

1037
00:58:06,320 --> 00:58:10,640
Speaker 2: Yes, And we can also do that regionally and seasonally,

1038
00:58:11,040 --> 00:58:13,640
Speaker 2: Like I'm I would be a pretty decent birder in

1039
00:58:13,680 --> 00:58:17,080
Speaker 2: the northeastern United States. But if I went to Spain,

1040
00:58:17,400 --> 00:58:21,960
Speaker 2: I would be terrible. And we would account.

1041
00:58:21,560 --> 00:58:24,520
Speaker 1: For that, meaning because you'd lose familiarity.

1042
00:58:23,960 --> 00:58:26,320
Speaker 2: Right, I would not know most of the birds over there.

1043
00:58:26,520 --> 00:58:28,400
Speaker 1: Are you guys aware of the Himalayan snowcock?

1044
00:58:28,680 --> 00:58:28,960
Speaker 5: Yeah?

1045
00:58:29,160 --> 00:58:32,600
Speaker 1: Okay, so they live in the Ruby Range of Nevada.

1046
00:58:33,560 --> 00:58:38,160
Speaker 1: And some years ago I was thinking about going hunting

1047
00:58:38,160 --> 00:58:41,480
Speaker 1: for one. It's a high effort, low reward kind of thing,

1048
00:58:42,640 --> 00:58:46,800
Speaker 1: fairly low success rates. But I wound up finding online

1049
00:58:48,200 --> 00:58:51,919
Speaker 1: this little birder club. And I don't think they would

1050
00:58:51,920 --> 00:58:53,400
Speaker 1: have done this if they knew there's people like me

1051
00:58:53,480 --> 00:58:55,840
Speaker 1: out there. But there's this little birder club that would

1052
00:58:55,920 --> 00:58:58,800
Speaker 1: talk about where exactly people over the years had seen

1053
00:58:59,280 --> 00:59:07,560
Speaker 1: Himalayan snow cocks, which is gold. I haven't gone into Eburn, look,

1054
00:59:07,640 --> 00:59:09,640
Speaker 1: but but would that be helpful for someone trying to

1055
00:59:09,640 --> 00:59:12,920
Speaker 1: find a Himalayan snowcock in the Ruby Mountains? Yeah, I

1056
00:59:12,960 --> 00:59:15,000
Speaker 1: mean you can be like, oh man, two days ago,

1057
00:59:16,160 --> 00:59:17,400
Speaker 1: there's one right there.

1058
00:59:17,480 --> 00:59:19,160
Speaker 5: Yeah, I mean, I think I think the thing is

1059
00:59:19,160 --> 00:59:21,280
Speaker 5: is in the US. You know, hunting is a highly

1060
00:59:21,360 --> 00:59:26,560
Speaker 5: regulated pursuit. People understand what's happening with these birds. And

1061
00:59:26,800 --> 00:59:30,440
Speaker 5: you know, if anything, what we need is more hunters,

1062
00:59:30,680 --> 00:59:33,000
Speaker 5: more people that are out fishing that you know, we

1063
00:59:33,000 --> 00:59:35,600
Speaker 5: were talking about Merlin a little bit earlier about you

1064
00:59:35,640 --> 00:59:38,840
Speaker 5: know what, what are you really using that for? For us?

1065
00:59:38,840 --> 00:59:41,520
Speaker 5: It's this huge engagement window. You know, if one in

1066
00:59:41,640 --> 00:59:45,200
Speaker 5: seventy people in the US have used Merlin in the

1067
00:59:45,320 --> 00:59:49,200
Speaker 5: last in the last year, that's a lot of people

1068
00:59:49,520 --> 00:59:53,040
Speaker 5: that are being exposed to birds, being exposed to natural systems.

1069
00:59:53,080 --> 00:59:56,760
Speaker 5: And sometimes it's easy to focus on maybe differences that

1070
00:59:56,840 --> 01:00:00,240
Speaker 5: exist either within the birding community, within the hunting community,

1071
01:00:00,600 --> 01:00:03,120
Speaker 5: or between those and say like there could be points

1072
01:00:03,120 --> 01:00:06,320
Speaker 5: of friction that you know, that's certainly true. There also

1073
01:00:06,400 --> 01:00:09,720
Speaker 5: can be incredible points of overlap because at the end

1074
01:00:09,760 --> 01:00:12,600
Speaker 5: of the day, we're interested in the same thing. You know,

1075
01:00:12,720 --> 01:00:15,280
Speaker 5: we want there to be natural systems that can support

1076
01:00:15,400 --> 01:00:19,280
Speaker 5: vibrant population of birds and mammals and have natural places

1077
01:00:19,880 --> 01:00:24,160
Speaker 5: and so so this is a there's a lot of

1078
01:00:24,240 --> 01:00:27,280
Speaker 5: benefit I think to thinking about, you know, just what

1079
01:00:27,360 --> 01:00:29,480
Speaker 5: are the ways that we can get more people interested

1080
01:00:29,520 --> 01:00:30,440
Speaker 5: in the natural world.

1081
01:00:30,880 --> 01:00:35,480
Speaker 1: Yeah, see, I have persecution complex. So when we were

1082
01:00:35,520 --> 01:00:38,880
Speaker 1: talking and you're talking about as your database grows in

1083
01:00:40,600 --> 01:00:47,240
Speaker 1: Cornell Ornithology Lab and eBird would share information with management

1084
01:00:47,320 --> 01:00:51,280
Speaker 1: agencies right where my head went right away, as there's

1085
01:00:51,280 --> 01:00:53,080
Speaker 1: a thing that will happen now and in for instance,

1086
01:00:53,120 --> 01:00:57,080
Speaker 1: there was a one of the times recently when it

1087
01:00:57,120 --> 01:01:00,080
Speaker 1: looked like they were the US Fish and Wildlife of

1088
01:01:00,160 --> 01:01:02,760
Speaker 1: US will now and then petition to get the grizzly

1089
01:01:02,800 --> 01:01:06,919
Speaker 1: bear delisted. So the agency that manages the grizzly bear

1090
01:01:07,800 --> 01:01:14,720
Speaker 1: as an endangered species will occasionally actually move to unload

1091
01:01:14,840 --> 01:01:18,520
Speaker 1: their management, meaning they're saying, as the management agency, we

1092
01:01:18,640 --> 01:01:24,680
Speaker 1: suggest that these be removed from federal oversight, and they'll

1093
01:01:24,680 --> 01:01:28,120
Speaker 1: get it'll get litigated. One of the times it will

1094
01:01:28,280 --> 01:01:33,440
Speaker 1: seems so close to be moving to state management that

1095
01:01:34,320 --> 01:01:38,800
Speaker 1: a couple states did a draw they were going to

1096
01:01:39,360 --> 01:01:42,320
Speaker 1: issue I think Wyoming was going to issue twenty grizzly

1097
01:01:42,360 --> 01:01:46,680
Speaker 1: bear tags. Idaho is going to issue one grizzly bear tag.

1098
01:01:46,960 --> 01:01:52,840
Speaker 1: Hunters Montana chicken shit it out and wasn't going to

1099
01:01:52,920 --> 01:01:56,920
Speaker 1: do any and there was a movement for people to

1100
01:01:57,400 --> 01:02:00,560
Speaker 1: it was like, hey, go and put in these. So

1101
01:02:00,800 --> 01:02:05,720
Speaker 1: within the non hunting world, they were encouraging people to

1102
01:02:05,760 --> 01:02:09,040
Speaker 1: apply for the tags, try to get the tags so

1103
01:02:09,080 --> 01:02:12,760
Speaker 1: that they don't fall into hunter's hands. So the menu

1104
01:02:12,840 --> 01:02:15,800
Speaker 1: you told me that as some of the persecution complex,

1105
01:02:16,520 --> 01:02:19,600
Speaker 1: I immediately thought, those sons of bitches are going to

1106
01:02:19,760 --> 01:02:27,919
Speaker 1: underreport game birds in order to in order to deprive opportunity. Yeah,

1107
01:02:27,960 --> 01:02:29,960
Speaker 1: you know, luckily the.

1108
01:02:31,560 --> 01:02:34,360
Speaker 5: You know, the just like the hunting community, there there

1109
01:02:34,400 --> 01:02:39,160
Speaker 5: can be friendly competition. You know, the burden community kind

1110
01:02:39,160 --> 01:02:41,720
Speaker 5: of has the same thing. People want to be the

1111
01:02:41,760 --> 01:02:42,800
Speaker 5: person who's seen a.

1112
01:02:42,720 --> 01:02:43,680
Speaker 1: Lot of different speceis.

1113
01:02:43,960 --> 01:02:47,680
Speaker 5: They want to have that and so there's often enough

1114
01:02:48,040 --> 01:02:50,200
Speaker 5: enough pressure. And I mean I do think that there's

1115
01:02:50,200 --> 01:02:53,840
Speaker 5: a sincere belief too that when you have better information

1116
01:02:53,960 --> 01:02:56,920
Speaker 5: and can do better science, you're gonna have better decisions

1117
01:02:56,920 --> 01:02:59,040
Speaker 5: that people can that people can make. And I mean

1118
01:02:59,080 --> 01:03:02,280
Speaker 5: that's kind of the idea of what we're trying to

1119
01:03:02,320 --> 01:03:04,720
Speaker 5: do and that all these people are trying to help

1120
01:03:04,760 --> 01:03:09,120
Speaker 5: with is you know, right now, state agencies don't have

1121
01:03:09,520 --> 01:03:12,360
Speaker 5: the information that they need. You know, if RAWA passes,

1122
01:03:12,640 --> 01:03:16,080
Speaker 5: how are state agencies going to figure out exactly where

1123
01:03:16,120 --> 01:03:19,120
Speaker 5: they want to make the investment to have the biggest impact.

1124
01:03:20,120 --> 01:03:23,200
Speaker 5: As we have data sets like eBird, I can look

1125
01:03:23,240 --> 01:03:26,360
Speaker 5: at this at sort of comprehensive levels. You can really

1126
01:03:26,400 --> 01:03:29,240
Speaker 5: start identifying like, well, these are the places that you know,

1127
01:03:29,240 --> 01:03:32,040
Speaker 5: if you want to bring back populations of birds, these

1128
01:03:32,040 --> 01:03:34,200
Speaker 5: are the places where you could focus on and really

1129
01:03:34,240 --> 01:03:36,640
Speaker 5: have impact. And it's a way of figuring out how

1130
01:03:36,640 --> 01:03:39,200
Speaker 5: we take the sort of scarce resources that we have

1131
01:03:39,320 --> 01:03:43,240
Speaker 5: within the conservation community and really use them most effectively.

1132
01:03:44,120 --> 01:03:46,400
Speaker 1: I would just think that, like, as I think about

1133
01:03:46,400 --> 01:03:54,360
Speaker 1: it from a citizen scientist tool, it just seems to

1134
01:03:54,400 --> 01:04:01,760
Speaker 1: me that the sound recordings somehow more valuable, do you

1135
01:04:01,800 --> 01:04:03,800
Speaker 1: know what I mean? Because you're not running it through

1136
01:04:05,320 --> 01:04:11,200
Speaker 1: you're not running it through It's just less fallible than

1137
01:04:11,280 --> 01:04:13,919
Speaker 1: running it through people. I mean, and I'm not trying.

1138
01:04:13,960 --> 01:04:16,120
Speaker 1: I don't want anybody want to criticize like Ebert as

1139
01:04:16,160 --> 01:04:20,240
Speaker 1: a tool. But let's say Ebert had total adoption in America. Okay,

1140
01:04:20,280 --> 01:04:24,440
Speaker 1: so three hundred some million Americans are making a daily

1141
01:04:24,640 --> 01:04:29,600
Speaker 1: chore of chronicling what birds they see around them. I

1142
01:04:29,640 --> 01:04:32,520
Speaker 1: think you'd hit like such at that point, you'd hit

1143
01:04:32,560 --> 01:04:37,320
Speaker 1: that there was just so much information that any that

1144
01:04:37,720 --> 01:04:41,800
Speaker 1: mistakes and things would be drowned out right. But I

1145
01:04:41,840 --> 01:04:44,400
Speaker 1: can't picture in its current form how it could be.

1146
01:04:46,360 --> 01:04:48,480
Speaker 1: And you know, better me. But I can't picture in

1147
01:04:48,520 --> 01:04:52,920
Speaker 1: its current form spread globally that number of people that

1148
01:04:52,960 --> 01:04:56,400
Speaker 1: you could actually, like, really with confidence make management decisions

1149
01:04:56,440 --> 01:04:58,960
Speaker 1: about it by it. Like you even mentioned me one

1150
01:04:58,960 --> 01:05:01,760
Speaker 1: time the bird you were saying we were taking last

1151
01:05:01,840 --> 01:05:07,640
Speaker 1: night that flies down the center of a waterway and

1152
01:05:07,680 --> 01:05:09,800
Speaker 1: that they would they would not be reported because they're.

1153
01:05:09,600 --> 01:05:13,200
Speaker 3: Flying well the limitations to the aerial surveys in Montreal.

1154
01:05:13,200 --> 01:05:14,800
Speaker 1: Ok. Yeah, so.

1155
01:05:16,360 --> 01:05:18,760
Speaker 2: I get that question all the time, you know, how

1156
01:05:18,800 --> 01:05:20,640
Speaker 2: can we trust this?

1157
01:05:20,960 --> 01:05:22,800
Speaker 1: Yeah, that's a that's a quicker way, and that's a

1158
01:05:22,880 --> 01:05:25,400
Speaker 1: quick way of saying, is like, how would you ever

1159
01:05:25,440 --> 01:05:29,760
Speaker 1: get to where you trusted enough that you're really looking

1160
01:05:29,800 --> 01:05:33,840
Speaker 1: at bird populations and not that you're looking at human habits? Right.

1161
01:05:34,000 --> 01:05:37,800
Speaker 2: That's so that's almost exactly how I phrase it. Is

1162
01:05:37,920 --> 01:05:40,000
Speaker 2: we got to make sure we're modeling the birds and

1163
01:05:40,040 --> 01:05:43,840
Speaker 2: not the birders, right, and we do that with all

1164
01:05:43,880 --> 01:05:46,840
Speaker 2: of the effort that's put in, so we know that

1165
01:05:46,960 --> 01:05:49,960
Speaker 2: if you go out for two and a half hours

1166
01:05:50,440 --> 01:05:53,200
Speaker 2: and you travel three miles, you are far more likely

1167
01:05:53,240 --> 01:05:55,360
Speaker 2: to see a given species than if I'm just sitting

1168
01:05:55,400 --> 01:05:57,840
Speaker 2: on my porch and make a checklist for five minutes.

1169
01:05:59,120 --> 01:06:02,400
Speaker 2: We account for that, we account for the expertise, like

1170
01:06:02,440 --> 01:06:06,640
Speaker 2: we talked about, so we know and you'll notice on

1171
01:06:06,960 --> 01:06:11,520
Speaker 2: our maps these are relative abundance, where we're not estimating

1172
01:06:11,760 --> 01:06:16,280
Speaker 2: actual abundance that would be you know, that's that's a

1173
01:06:16,400 --> 01:06:21,040
Speaker 2: huge ask of of E bird data because you know,

1174
01:06:21,960 --> 01:06:23,840
Speaker 2: in general, people are bad at counting.

1175
01:06:24,920 --> 01:06:26,880
Speaker 1: I get where you're like dive in a little bit

1176
01:06:26,880 --> 01:06:28,320
Speaker 1: when you say relative abundance.

1177
01:06:28,680 --> 01:06:32,560
Speaker 2: So relative abundance as people understand, Yeah, as we have

1178
01:06:32,720 --> 01:06:40,080
Speaker 2: defined it is. Uh, it is the average number of

1179
01:06:40,600 --> 01:06:43,760
Speaker 2: space or average number of individuals of a given species

1180
01:06:44,360 --> 01:06:48,160
Speaker 2: an expert E birder would expect to see in one

1181
01:06:48,200 --> 01:06:54,480
Speaker 2: of these three by three pixels at the optimal time

1182
01:06:54,480 --> 01:06:56,840
Speaker 2: of day for detection. And that's defined by the model

1183
01:06:57,640 --> 01:07:04,360
Speaker 2: traveling one kilometer and birding for one hour. So that's

1184
01:07:04,360 --> 01:07:06,640
Speaker 2: a that's a long definition of relative abundance.

1185
01:07:06,680 --> 01:07:08,560
Speaker 1: Well that's what it is. Hit me with one. Let's

1186
01:07:08,600 --> 01:07:14,320
Speaker 1: take the red breasted rob and okay in June or

1187
01:07:14,360 --> 01:07:16,200
Speaker 1: just give me any kind of example of what the

1188
01:07:16,320 --> 01:07:17,000
Speaker 1: number might be.

1189
01:07:17,160 --> 01:07:22,160
Speaker 2: So for roughed grouse here we're looking at it. This

1190
01:07:22,400 --> 01:07:27,080
Speaker 2: ranges from you know, almost nothing to fourteen is the

1191
01:07:27,200 --> 01:07:31,240
Speaker 2: darkest purple we can see on this map. So that

1192
01:07:31,280 --> 01:07:31,760
Speaker 2: would be.

1193
01:07:32,040 --> 01:07:34,360
Speaker 1: Man, I grew up in a really good gross spot. Yeah,

1194
01:07:36,480 --> 01:07:40,360
Speaker 1: I knew it, you know.

1195
01:07:40,400 --> 01:07:43,640
Speaker 2: So that's and again relative abundance. You can think about

1196
01:07:43,680 --> 01:07:48,880
Speaker 2: that as there are more here than there are here. Yeah,

1197
01:07:49,160 --> 01:07:52,280
Speaker 2: and then this puts numbers to just how many more

1198
01:07:52,560 --> 01:07:55,160
Speaker 2: there are in a given spot.

1199
01:07:55,240 --> 01:07:57,520
Speaker 1: So if we look at what's interesting about this is

1200
01:07:57,520 --> 01:08:02,479
Speaker 1: you could have you could uniformally reduced by fifty rough

1201
01:08:02,480 --> 01:08:06,040
Speaker 1: grouse populations and that stays the same because it's relative, right.

1202
01:08:06,800 --> 01:08:09,040
Speaker 2: Yes, if you just cut it in half, so we

1203
01:08:09,120 --> 01:08:14,200
Speaker 2: can look at one, this is hooded merganzer. So rough

1204
01:08:14,200 --> 01:08:17,080
Speaker 2: grouse are not migratory. Hooded merganser is. So we can

1205
01:08:17,120 --> 01:08:21,840
Speaker 2: see this change across the year. And that's what we're

1206
01:08:21,880 --> 01:08:25,920
Speaker 2: looking at. Here is relative abundance at each week of

1207
01:08:25,960 --> 01:08:26,360
Speaker 2: the year.

1208
01:08:28,240 --> 01:08:28,559
Speaker 5: Back of.

1209
01:08:30,920 --> 01:08:33,040
Speaker 1: Man. This is the thing. So I want to explain

1210
01:08:33,080 --> 01:08:34,960
Speaker 1: people what we're looking at. We're looking at, this is

1211
01:08:35,000 --> 01:08:38,560
Speaker 1: the court, we're looking at. This is all off Ebert.

1212
01:08:38,840 --> 01:08:44,840
Speaker 2: So this is run through some very sophisticated models that

1213
01:08:44,960 --> 01:08:47,280
Speaker 2: account for all those things we've talked about as far

1214
01:08:47,320 --> 01:08:52,960
Speaker 2: as effort and account for environmental variables different land cover.

1215
01:08:53,040 --> 01:08:57,840
Speaker 2: We have more than eighty four variables in this model

1216
01:08:57,840 --> 01:09:00,120
Speaker 2: that account for things like elevation.

1217
01:09:02,040 --> 01:09:02,360
Speaker 1: Weather.

1218
01:09:02,439 --> 01:09:07,959
Speaker 2: We have hourly weather in this model attached to a checklist.

1219
01:09:08,000 --> 01:09:14,160
Speaker 2: We've got various water layers. We have tidal layers, mudflats,

1220
01:09:14,439 --> 01:09:16,679
Speaker 2: those kinds of things. We have all kinds of things

1221
01:09:16,720 --> 01:09:19,640
Speaker 2: that are accounted for in this to make sure that

1222
01:09:19,760 --> 01:09:23,200
Speaker 2: we're not just modeling where people go, but where they're

1223
01:09:23,240 --> 01:09:24,679
Speaker 2: actually seeing these birds.

1224
01:09:25,560 --> 01:09:27,519
Speaker 1: What you can watch here and so if people go

1225
01:09:27,560 --> 01:09:30,160
Speaker 1: to Eber they can find all this. Yes, yeah, what

1226
01:09:30,200 --> 01:09:34,799
Speaker 1: you're watching here is is like a color coded map

1227
01:09:35,800 --> 01:09:41,559
Speaker 1: showing bird densities as they migrate across the continent. And

1228
01:09:44,280 --> 01:09:45,840
Speaker 1: a thing I like about when I talk about like

1229
01:09:45,920 --> 01:09:51,559
Speaker 1: human error is when they're all gone. They're all gone, right,

1230
01:09:52,520 --> 01:09:55,320
Speaker 1: meaning when when everything migrates, nor if you're not seeing

1231
01:09:55,360 --> 01:09:58,559
Speaker 1: like little mistakes pop up, or is that because they've

1232
01:09:58,560 --> 01:09:59,360
Speaker 1: been filtered out?

1233
01:10:00,800 --> 01:10:04,040
Speaker 2: They may have been filtered out by the modeling process.

1234
01:10:04,439 --> 01:10:08,400
Speaker 2: Because at you know, where we are here in you

1235
01:10:08,400 --> 01:10:12,080
Speaker 2: know June or July into June is where we are now,

1236
01:10:12,800 --> 01:10:16,360
Speaker 2: You're not going to see any hooded morganzas on the

1237
01:10:16,680 --> 01:10:17,679
Speaker 2: Texas Gulf Coast.

1238
01:10:18,439 --> 01:10:22,280
Speaker 1: You shouldn't. That's the thing that surprises me is that

1239
01:10:22,280 --> 01:10:25,679
Speaker 1: that the migrations are so complete that you don't have more.

1240
01:10:25,720 --> 01:10:28,679
Speaker 1: We're just like some for some weird reason that hangs around,

1241
01:10:28,840 --> 01:10:29,160
Speaker 1: you know.

1242
01:10:29,280 --> 01:10:32,960
Speaker 2: And there may be a reporter or to from there.

1243
01:10:35,400 --> 01:10:38,519
Speaker 2: But if it's just a reporter or to the way

1244
01:10:38,800 --> 01:10:41,879
Speaker 2: the modeling works the way, there's all this spatial filtering

1245
01:10:41,880 --> 01:10:45,559
Speaker 2: that goes on to make sure that you know, because

1246
01:10:45,600 --> 01:10:47,400
Speaker 2: if there is a hooded merganser there at this time

1247
01:10:47,400 --> 01:10:48,760
Speaker 2: of year, a lot of people are going to go

1248
01:10:48,760 --> 01:10:50,560
Speaker 2: see it, and a lot of people are going to

1249
01:10:50,600 --> 01:10:53,160
Speaker 2: put that on their checklist. So it in the raw

1250
01:10:53,160 --> 01:10:56,080
Speaker 2: Eyburn data, it's going to look like, you know, several

1251
01:10:56,240 --> 01:10:58,799
Speaker 2: hundred people saw hood in Merganza in that one spot

1252
01:10:58,920 --> 01:11:01,000
Speaker 2: that day. We're only going to use one of those

1253
01:11:01,080 --> 01:11:02,400
Speaker 2: checklists across that.

1254
01:11:02,439 --> 01:11:06,800
Speaker 1: Well, I filtered that stuff out, okay, right, that makes sense. Yeah,

1255
01:11:07,160 --> 01:11:09,639
Speaker 1: remember what was it a few years ago? Last year

1256
01:11:10,680 --> 01:11:16,120
Speaker 1: some birds showed up off the northeast, that stellar sea eagle. Yeah,

1257
01:11:16,120 --> 01:11:17,080
Speaker 1: the stellar sea eagle.

1258
01:11:17,240 --> 01:11:20,000
Speaker 5: Everybody was going to see. Yeah, and what.

1259
01:11:19,960 --> 01:11:22,640
Speaker 1: So were people when that stellar's see he took a

1260
01:11:22,680 --> 01:11:26,519
Speaker 1: wrong turn or screwed something up right and north of

1261
01:11:26,680 --> 01:11:27,479
Speaker 1: New England or something.

1262
01:11:27,560 --> 01:11:30,800
Speaker 5: Yeah, bird a bird in Japan, probably jumped over, went

1263
01:11:30,840 --> 01:11:34,640
Speaker 5: into Alaska, kept going going across, and then wound up

1264
01:11:34,680 --> 01:11:37,160
Speaker 5: in the northeast, maybe went down to Texas, maybe went

1265
01:11:37,200 --> 01:11:40,040
Speaker 5: back up. That bird then is something that you know,

1266
01:11:40,200 --> 01:11:42,200
Speaker 5: there's only one of them in the US.

1267
01:11:42,280 --> 01:11:44,840
Speaker 1: So if you want to see that a lot on

1268
01:11:44,920 --> 01:11:45,360
Speaker 1: the bird.

1269
01:11:45,800 --> 01:11:48,840
Speaker 5: Yeah, I mean at times, hundreds of reports of that

1270
01:11:48,960 --> 01:11:50,240
Speaker 5: individ gone.

1271
01:11:53,240 --> 01:11:55,719
Speaker 1: Look hell, yeah, did you really?

1272
01:11:56,479 --> 01:11:59,040
Speaker 5: We actually didn't because we had to manage a bunch

1273
01:11:59,080 --> 01:12:00,320
Speaker 5: of stuff, so we weren't to go.

1274
01:12:00,479 --> 01:12:05,320
Speaker 3: But you left to go see that Birden got in

1275
01:12:05,360 --> 01:12:07,080
Speaker 3: their car right away as soon.

1276
01:12:06,960 --> 01:12:10,519
Speaker 5: As and then when it changed states, they wanted not

1277
01:12:10,560 --> 01:12:12,360
Speaker 5: only did they want to see it in Massachuse, they

1278
01:12:12,360 --> 01:12:14,240
Speaker 5: wanted to make sure that they saw it in Maine.

1279
01:12:14,360 --> 01:12:17,160
Speaker 1: Because what's the advantage of that, Because it's a different states.

1280
01:12:17,520 --> 01:12:20,160
Speaker 5: It's a different lists, it's the same, it's the same thing. Like,

1281
01:12:20,240 --> 01:12:22,519
Speaker 5: you know, why would you want to you know, maybe

1282
01:12:22,520 --> 01:12:25,400
Speaker 5: shoot different different species of animals.

1283
01:12:24,920 --> 01:12:26,040
Speaker 1: Like it's in different.

1284
01:12:27,320 --> 01:12:31,120
Speaker 5: Maybe maybe I mean different species's whitetail.

1285
01:12:31,680 --> 01:12:35,640
Speaker 3: We can sponsor and we can initiate the sport of.

1286
01:12:35,200 --> 01:12:35,719
Speaker 5: A pro.

1287
01:12:37,960 --> 01:12:41,439
Speaker 1: Huge thing, because yeah, I am guilty of that. I'm

1288
01:12:41,479 --> 01:12:44,960
Speaker 1: real interested in what states I got a turkey in Yeah, okay.

1289
01:12:46,240 --> 01:12:46,559
Speaker 2: It is.

1290
01:12:46,600 --> 01:12:48,080
Speaker 1: I thought it was dumb. Get smart.

1291
01:12:48,280 --> 01:12:51,040
Speaker 5: But what ends up what ends up being pretty cool here, though,

1292
01:12:51,160 --> 01:12:53,320
Speaker 5: is when you have all those people that are going

1293
01:12:53,360 --> 01:12:56,640
Speaker 5: to see that that stellar seagull at some place, what

1294
01:12:56,800 --> 01:13:00,840
Speaker 5: happens is we can start to understand something about the

1295
01:13:01,000 --> 01:13:03,799
Speaker 5: sensor network, So the group of people that are going

1296
01:13:03,840 --> 01:13:07,880
Speaker 5: there and how their lists are different between them. So

1297
01:13:07,880 --> 01:13:10,639
Speaker 5: they're all there, they all are focused on stellar siegel,

1298
01:13:10,920 --> 01:13:13,120
Speaker 5: but we're asking them to give us a complete list

1299
01:13:13,160 --> 01:13:15,280
Speaker 5: of all the birds that they see, so we can

1300
01:13:15,320 --> 01:13:18,720
Speaker 5: start to tease about those differences that are between the

1301
01:13:18,800 --> 01:13:25,719
Speaker 5: observers to understand the actual natural biological signal by having

1302
01:13:25,760 --> 01:13:27,479
Speaker 5: people go and see those. So what we try to

1303
01:13:27,520 --> 01:13:29,800
Speaker 5: do is think strategically, like how do we use some

1304
01:13:29,920 --> 01:13:34,120
Speaker 5: of these unusual circumstances to our advantage to be able

1305
01:13:34,120 --> 01:13:38,080
Speaker 5: to tease apart the differences between biology and human behavior.

1306
01:13:47,720 --> 01:13:50,439
Speaker 1: So have you I feel that that would be a

1307
01:13:50,479 --> 01:13:52,720
Speaker 1: great example of somewhere where you could really spend a

1308
01:13:52,720 --> 01:14:02,840
Speaker 1: lot of time on demographics. Human demographics. Okay, so let's

1309
01:14:02,840 --> 01:14:05,800
Speaker 1: say you didn't follow the news all right, you don't

1310
01:14:05,800 --> 01:14:09,719
Speaker 1: follow the news at all, and all of a sudden.

1311
01:14:11,040 --> 01:14:16,919
Speaker 1: It's just that there's this huge sudden population of stellar

1312
01:14:16,960 --> 01:14:22,439
Speaker 1: sea eagles in Maine. Okay, would you look at that

1313
01:14:22,720 --> 01:14:26,040
Speaker 1: and uh, it's kind of two different questions, But would

1314
01:14:26,080 --> 01:14:29,200
Speaker 1: you look at that and immediately think that's got to

1315
01:14:29,240 --> 01:14:31,799
Speaker 1: be a single bird? You just would?

1316
01:14:31,880 --> 01:14:38,840
Speaker 5: You would, because we we understand basically with an eye bird,

1317
01:14:38,840 --> 01:14:40,759
Speaker 5: you know, we have all we have all this data

1318
01:14:40,840 --> 01:14:43,559
Speaker 5: now to tell us where birds are across time and space.

1319
01:14:44,520 --> 01:14:47,400
Speaker 5: We know what the maximum count ends up being in

1320
01:14:47,520 --> 01:14:49,960
Speaker 5: so when we look at something like that, we can

1321
01:14:50,080 --> 01:14:53,000
Speaker 5: we can see the difference between when something might be

1322
01:14:53,160 --> 01:14:56,360
Speaker 5: a phenomenon caused by weather or something like that. So

1323
01:14:56,439 --> 01:15:00,000
Speaker 5: there's there's been other cases where there's birds like shore birds.

1324
01:15:00,080 --> 01:15:04,080
Speaker 5: Let's say, where there have been several different individuals all

1325
01:15:04,120 --> 01:15:06,120
Speaker 5: of a bird that's usually pretty rare. In this case,

1326
01:15:06,120 --> 01:15:09,320
Speaker 5: we could say something like northern lapwing. It's a European

1327
01:15:09,400 --> 01:15:13,479
Speaker 5: species that's usually it's migratory there, very very rare that

1328
01:15:13,520 --> 01:15:15,479
Speaker 5: it makes it over to the US. But sometimes you

1329
01:15:15,520 --> 01:15:19,000
Speaker 5: can have these storm systems that can blow those birds

1330
01:15:19,080 --> 01:15:21,800
Speaker 5: over here. In that case, what we can see is

1331
01:15:21,840 --> 01:15:24,479
Speaker 5: that there might be counts of three birds at one place,

1332
01:15:24,560 --> 01:15:27,479
Speaker 5: five birds at another place, and we can understand sort

1333
01:15:27,520 --> 01:15:30,240
Speaker 5: of how those birds are moving and so if and

1334
01:15:30,720 --> 01:15:34,479
Speaker 5: because they're rare and unusual events, people want to take

1335
01:15:34,520 --> 01:15:36,840
Speaker 5: pictures of them because they're noteworthy, And so then we

1336
01:15:36,880 --> 01:15:39,759
Speaker 5: can actually go in and really dive in and say, okay,

1337
01:15:39,840 --> 01:15:43,759
Speaker 5: like let's look at the molt pattern on this bird

1338
01:15:43,800 --> 01:15:46,680
Speaker 5: and look at the exact where on the ttertules. And

1339
01:15:46,720 --> 01:15:48,240
Speaker 5: so we've been able to look at you know, in

1340
01:15:48,280 --> 01:15:50,880
Speaker 5: the case of that sea eagle, as people were seeing

1341
01:15:50,920 --> 01:15:53,760
Speaker 5: at different places. You know, we didn't just sort of

1342
01:15:53,800 --> 01:15:56,000
Speaker 5: make the assumption that it was the same bird. People

1343
01:15:56,080 --> 01:16:00,360
Speaker 5: looked at the exact featherwhere to understand like, okay, it

1344
01:16:00,360 --> 01:16:02,639
Speaker 5: looks like this is the same bird because look at

1345
01:16:02,680 --> 01:16:06,000
Speaker 5: this scapular feather and this weird pattern that it has.

1346
01:16:06,040 --> 01:16:08,200
Speaker 5: It's very unusual. Just in the same way that you know,

1347
01:16:08,240 --> 01:16:11,200
Speaker 5: we we can do with people. You know, it's a

1348
01:16:11,240 --> 01:16:13,040
Speaker 5: lot harder to do with birds. But when you're just

1349
01:16:13,080 --> 01:16:15,800
Speaker 5: looking at sort of one or two individuals all of

1350
01:16:15,840 --> 01:16:18,720
Speaker 5: a sudden, you can really you know, given the photographic

1351
01:16:18,720 --> 01:16:21,559
Speaker 5: equipment that people have available, we can really understand that

1352
01:16:21,600 --> 01:16:23,719
Speaker 5: with a lot of detail. Now, which is pretty cool.

1353
01:16:24,600 --> 01:16:30,080
Speaker 1: How many have you ever looked how many individuals logged

1354
01:16:30,240 --> 01:16:30,879
Speaker 1: that bird?

1355
01:16:32,439 --> 01:16:33,880
Speaker 5: You know, I haven't. I haven't looked.

1356
01:16:35,000 --> 01:16:38,880
Speaker 1: Do you feel that it'd be hundreds of thousands us? Yeah,

1357
01:16:39,080 --> 01:16:42,000
Speaker 1: got to hit off that bird. Yeah. Had you guys

1358
01:16:42,040 --> 01:16:44,400
Speaker 1: ever seen that? You didn't go, look, what is that

1359
01:16:44,439 --> 01:16:45,559
Speaker 1: bird on your bird list?

1360
01:16:46,000 --> 01:16:48,720
Speaker 5: No, it's not, which is even more that makes it more.

1361
01:16:48,760 --> 01:16:51,080
Speaker 5: I mean, this is not this would be like it's

1362
01:16:51,160 --> 01:16:57,320
Speaker 5: you know, we I have seen snow cock. Yeah, they mountains. Yeah,

1363
01:16:57,360 --> 01:16:59,360
Speaker 5: because it's a you know, part of it is tell

1364
01:16:59,400 --> 01:17:00,120
Speaker 5: me where later on?

1365
01:17:00,520 --> 01:17:04,639
Speaker 1: Well it was it ended up getting close?

1366
01:17:05,200 --> 01:17:08,400
Speaker 5: Yeah? And what's I mean part of it is, you know,

1367
01:17:08,439 --> 01:17:11,439
Speaker 5: whether it's birding or hunting, like it's an excuse to

1368
01:17:11,520 --> 01:17:14,320
Speaker 5: go and and spend some time and in kind of

1369
01:17:14,360 --> 01:17:17,600
Speaker 5: a magical place. And you know, the focus might be

1370
01:17:17,680 --> 01:17:21,280
Speaker 5: there on some specific thing, but sometimes what you remember

1371
01:17:21,360 --> 01:17:25,040
Speaker 5: the most from that may not even be related to that.

1372
01:17:25,160 --> 01:17:27,760
Speaker 5: So you know, going up to that, you know, glacial Lake,

1373
01:17:28,280 --> 01:17:31,439
Speaker 5: seeing rosy finches and pine grossbeaks, and I mean it's

1374
01:17:31,479 --> 01:17:34,280
Speaker 5: just a it's a pretty cool, magical place. And you know,

1375
01:17:34,320 --> 01:17:37,080
Speaker 5: whether it's hunting or fishing or birds like you it's

1376
01:17:37,120 --> 01:17:39,479
Speaker 5: just this excuse to go and spend some time in

1377
01:17:39,520 --> 01:17:42,680
Speaker 5: a natural system and smell the crisp air in the

1378
01:17:42,720 --> 01:17:45,920
Speaker 5: morning and and you know, listen to the natural world

1379
01:17:45,960 --> 01:17:49,559
Speaker 5: and sort of detach from the the day to day,

1380
01:17:49,640 --> 01:17:53,599
Speaker 5: which you know sometimes in great Uh.

1381
01:17:53,720 --> 01:17:58,320
Speaker 1: My dad had he ranked all birds into two rough categories.

1382
01:17:58,800 --> 01:18:06,000
Speaker 1: Uh those games birds. There's tweety birds and just the uh.

1383
01:18:06,760 --> 01:18:09,800
Speaker 1: And that's the thing like we growing up. Man, you

1384
01:18:09,880 --> 01:18:13,240
Speaker 1: knew some birds, but it was just there was Yeah,

1385
01:18:13,280 --> 01:18:16,360
Speaker 1: there was the ones that you hunt for, you know,

1386
01:18:16,920 --> 01:18:21,200
Speaker 1: and then the ones that you didn't. And man, I

1387
01:18:21,200 --> 01:18:23,559
Speaker 1: I missed so many good opportunities to learn birds. And

1388
01:18:23,600 --> 01:18:29,560
Speaker 1: I still struggle with like identifying all the hawks up high,

1389
01:18:29,880 --> 01:18:31,679
Speaker 1: you know, and people that can do that, I'm jealous

1390
01:18:31,680 --> 01:18:35,720
Speaker 1: of that shit bad. You know. Do you guys allow uh?

1391
01:18:35,800 --> 01:18:42,599
Speaker 1: Do you allow yourselves as professional ornithologists to have favorite birds? Yeah?

1392
01:18:42,640 --> 01:18:44,360
Speaker 1: Of course, Yeah, what's your favorite?

1393
01:18:44,400 --> 01:18:48,040
Speaker 3: My favorite is a long tailed duck, So yeah, I

1394
01:18:48,080 --> 01:18:50,559
Speaker 3: mean they you know, I grew up on the shore

1395
01:18:50,560 --> 01:18:52,840
Speaker 3: of Lake Ontario and there were just hundreds of them

1396
01:18:52,840 --> 01:18:54,960
Speaker 3: out there all winter long. Because I just love that

1397
01:18:55,080 --> 01:18:58,000
Speaker 3: experience watching them throughout the winter and then knowing that

1398
01:18:58,040 --> 01:19:00,439
Speaker 3: they were traveling all the way to the Arctic completely

1399
01:19:00,520 --> 01:19:03,719
Speaker 3: changing their plumage over getting those all black heads and everything.

1400
01:19:03,760 --> 01:19:05,600
Speaker 3: Just you know, even as a ten year old, I

1401
01:19:05,640 --> 01:19:07,880
Speaker 3: was like thinking, man, wouldn't it be cool to go

1402
01:19:07,960 --> 01:19:11,000
Speaker 3: all the way to their breeding grounds and imagining the

1403
01:19:11,040 --> 01:19:14,080
Speaker 3: migrations that they do. And so that's that's my favorite bird.

1404
01:19:14,160 --> 01:19:16,519
Speaker 1: Did you grow up knowing that bird by a different name?

1405
01:19:16,800 --> 01:19:20,080
Speaker 3: Oh yeah, yeah, my family still it's like old squawk

1406
01:19:20,120 --> 01:19:20,559
Speaker 3: come over.

1407
01:19:21,240 --> 01:19:25,479
Speaker 1: Yeah, for sure. It's a that we know. We had

1408
01:19:25,560 --> 01:19:27,599
Speaker 1: quite a few of those in some areas and mission

1409
01:19:27,640 --> 01:19:30,240
Speaker 1: you have quite a few of them, and adoption of

1410
01:19:30,280 --> 01:19:31,479
Speaker 1: the new name is not wise.

1411
01:19:31,600 --> 01:19:34,320
Speaker 3: Yeah, it's mixed, a very mixed rate of adoption.

1412
01:19:35,240 --> 01:19:39,000
Speaker 1: So that's when you like, when you like favorite.

1413
01:19:38,680 --> 01:19:41,040
Speaker 5: It's tough for me. I mean, I don't I tend

1414
01:19:41,120 --> 01:19:45,240
Speaker 5: to be you know, whatever I'm looking at is is special.

1415
01:19:45,320 --> 01:19:48,240
Speaker 5: It's like you know, who's your favorite kid? Right, Like

1416
01:19:48,280 --> 01:19:50,160
Speaker 5: there's oh, there's something.

1417
01:19:53,680 --> 01:19:55,040
Speaker 1: Depends what what I'm talking about.

1418
01:19:55,280 --> 01:20:00,960
Speaker 5: They always ask what, No, I mean if today there's

1419
01:20:01,000 --> 01:20:03,760
Speaker 5: a there's a big owl, great gray owl.

1420
01:20:04,200 --> 01:20:04,800
Speaker 1: I know that that.

1421
01:20:05,000 --> 01:20:07,639
Speaker 5: Yeah, it's it's just you know, it's a it's an

1422
01:20:07,680 --> 01:20:11,360
Speaker 5: indicator of some of the you know, really nice boreal forests.

1423
01:20:12,439 --> 01:20:15,679
Speaker 5: Really really cool bird. You don't see them very often.

1424
01:20:16,120 --> 01:20:20,520
Speaker 5: Sometimes they stage these eruptions, you know, into Michigan or Minnesota, Wisconsin.

1425
01:20:21,840 --> 01:20:23,559
Speaker 5: I was fortunate one year to go up there during

1426
01:20:23,600 --> 01:20:25,719
Speaker 5: one of these big eruptions and so over two hundred

1427
01:20:25,720 --> 01:20:26,200
Speaker 5: in a day.

1428
01:20:27,080 --> 01:20:27,920
Speaker 1: And and you.

1429
01:20:27,840 --> 01:20:32,400
Speaker 5: Know, just just birds. And you know, anytime you see

1430
01:20:32,400 --> 01:20:35,439
Speaker 5: things that it has your answer asked these questions, like

1431
01:20:35,479 --> 01:20:38,160
Speaker 5: you know, what's what's driving that? Are they mostly young birds?

1432
01:20:38,160 --> 01:20:40,640
Speaker 5: Are they one year old birds? And you know, you

1433
01:20:40,640 --> 01:20:42,880
Speaker 5: you just start looking at things and thinking about things

1434
01:20:42,880 --> 01:20:44,640
Speaker 5: in a different, you know, different.

1435
01:20:44,280 --> 01:20:49,479
Speaker 1: Way mentioned that. I remember one time I can even

1436
01:20:49,520 --> 01:20:51,360
Speaker 1: tell you the owner of the field and the there

1437
01:20:51,400 --> 01:20:54,679
Speaker 1: was a there was a farm family, the zelden Rust family,

1438
01:20:55,560 --> 01:20:59,840
Speaker 1: and one day there was a snowy owl. So this

1439
01:20:59,920 --> 01:21:03,519
Speaker 1: is Michigan, you know, a snowy owl sitting in their field,

1440
01:21:03,600 --> 01:21:05,400
Speaker 1: and like that was the thing people went to watch,

1441
01:21:05,439 --> 01:21:10,479
Speaker 1: you know. Oh yeah. And then I remember my brother

1442
01:21:10,560 --> 01:21:14,720
Speaker 1: Danny hitting a great horn. He hit it, hit him

1443
01:21:15,280 --> 01:21:18,600
Speaker 1: and did real damage out the car, like broke the

1444
01:21:18,640 --> 01:21:21,360
Speaker 1: grill out of the car and stuff, you know, hitting

1445
01:21:21,400 --> 01:21:23,880
Speaker 1: that thing. And like you it's funny the way you

1446
01:21:23,920 --> 01:21:27,680
Speaker 1: get certain like bird interactions burned into your head. I

1447
01:21:27,720 --> 01:21:30,720
Speaker 1: remember being a little kid one day and even though

1448
01:21:30,760 --> 01:21:32,639
Speaker 1: we had a lot of rough grouse, like just seeing

1449
01:21:32,640 --> 01:21:36,200
Speaker 1: a rough grouse in the yard, which is the least

1450
01:21:36,320 --> 01:21:38,840
Speaker 1: rough grouse he looking, you know, like on a like

1451
01:21:39,000 --> 01:21:42,559
Speaker 1: on a mode lawn, right, and it burns in your

1452
01:21:42,600 --> 01:21:43,400
Speaker 1: head in a weird way.

1453
01:21:43,560 --> 01:21:47,679
Speaker 5: Totally. We had this right when COVID started. I remember

1454
01:21:47,720 --> 01:21:51,559
Speaker 5: the lab basically had just closed. Everybody brought their stuff home.

1455
01:21:52,320 --> 01:21:54,759
Speaker 5: We knew we were entering this sort of new space,

1456
01:21:54,800 --> 01:21:56,599
Speaker 5: and I was like, well, you know, at least we've

1457
01:21:56,600 --> 01:21:59,920
Speaker 5: got this rough grouse right outside our house that's drum

1458
01:22:00,320 --> 01:22:03,120
Speaker 5: and it was just like, you know, the world is

1459
01:22:03,600 --> 01:22:07,240
Speaker 5: a bunch of terrible stuff that's happening right now, but

1460
01:22:07,320 --> 01:22:11,280
Speaker 5: we have this grouse. And then the next day it's morning,

1461
01:22:11,320 --> 01:22:15,280
Speaker 5: we're drinking coffee, you know, and and we hear this

1462
01:22:17,720 --> 01:22:21,040
Speaker 5: and we on the second story. We go up there

1463
01:22:21,400 --> 01:22:24,759
Speaker 5: and there's this roughed grouse that's sitting there. Well, of course, Jesse,

1464
01:22:25,439 --> 01:22:27,040
Speaker 5: she's she's pretty.

1465
01:22:27,320 --> 01:22:27,800
Speaker 1: It's dead.

1466
01:22:28,600 --> 01:22:33,599
Speaker 5: It is dead on the roof. Well, it hits there's

1467
01:22:33,640 --> 01:22:36,000
Speaker 5: sort of a you know, there's the bottom level of

1468
01:22:35,720 --> 01:22:37,800
Speaker 5: the roof and then an upper roof and so it

1469
01:22:37,880 --> 01:22:41,840
Speaker 5: hit the window on the second store it fell, and

1470
01:22:43,240 --> 01:22:46,160
Speaker 5: so Jesse was also you know, we were traumatized, but

1471
01:22:46,200 --> 01:22:48,800
Speaker 5: she was also pretty excited that we would be able

1472
01:22:48,800 --> 01:22:51,879
Speaker 5: to have the rough grouse for the rough grouse for dinner.

1473
01:22:51,960 --> 01:22:56,040
Speaker 1: Time, getting the hard society society exactly.

1474
01:22:56,080 --> 01:22:58,240
Speaker 3: That was the week you couldn't find onions in the store.

1475
01:23:00,560 --> 01:23:02,280
Speaker 5: But it was, you know, it was this thing that

1476
01:23:02,400 --> 01:23:03,240
Speaker 5: was just like crushing.

1477
01:23:03,280 --> 01:23:05,320
Speaker 1: I was like, the world really is you know.

1478
01:23:05,560 --> 01:23:07,600
Speaker 5: And and then the next day, luckily there was a

1479
01:23:08,360 --> 01:23:11,639
Speaker 5: it wasn't our roughed grause that it was another bird

1480
01:23:11,720 --> 01:23:14,439
Speaker 5: that through there. So then I was like, okay, Jesse,

1481
01:23:14,560 --> 01:23:17,280
Speaker 5: we can I'm okay eating that eating the grasse tonight.

1482
01:23:17,960 --> 01:23:19,240
Speaker 5: What's your favorite bird?

1483
01:23:19,800 --> 01:23:23,839
Speaker 2: You know, I I love waterfowl, but I would say

1484
01:23:24,240 --> 01:23:27,479
Speaker 2: my my favorite bird is probably reddish egret. Really growing

1485
01:23:27,560 --> 01:23:30,200
Speaker 2: up on the on the Gulf coast, uh, seeing them

1486
01:23:30,320 --> 01:23:34,000
Speaker 2: was was special because they're they're I'm not gonna say

1487
01:23:34,000 --> 01:23:37,440
Speaker 2: they're rare where I where I was, uh in Alabama

1488
01:23:37,520 --> 01:23:40,080
Speaker 2: on the Gulf coast, but you're not going to see

1489
01:23:40,120 --> 01:23:43,240
Speaker 2: a ton of them when you're out and they uh,

1490
01:23:43,840 --> 01:23:47,080
Speaker 2: they they'll run like wind sprints to stir up the water,

1491
01:23:47,520 --> 01:23:50,000
Speaker 2: confuse fish, and then they'll shade the water with their

1492
01:23:50,040 --> 01:23:52,639
Speaker 2: wings and do this weird little dance and then pick

1493
01:23:52,720 --> 01:23:55,360
Speaker 2: fish out. It's just there, you know, a blast to watch.

1494
01:23:55,880 --> 01:23:58,840
Speaker 1: We used to have when we were kids. My dad

1495
01:23:58,800 --> 01:24:02,880
Speaker 1: would use a for a live well. He'd use the

1496
01:24:03,040 --> 01:24:07,000
Speaker 1: washing machine agitator like the like. So it's a those

1497
01:24:07,040 --> 01:24:10,200
Speaker 1: old steel perforated tanks. It was inside old washing machines

1498
01:24:10,240 --> 01:24:13,040
Speaker 1: and had that center at that center piece in it.

1499
01:24:13,760 --> 01:24:15,320
Speaker 1: But he'd just take those out and set them in

1500
01:24:15,360 --> 01:24:18,080
Speaker 1: the water because they're perforated and steal and that would

1501
01:24:18,080 --> 01:24:22,800
Speaker 1: be the live well. So when you caught bluegills, we'd

1502
01:24:22,800 --> 01:24:26,280
Speaker 1: throw our bluegills in this perforated tank. They just sat

1503
01:24:26,320 --> 01:24:28,760
Speaker 1: in the water, and then when there got to be

1504
01:24:28,880 --> 01:24:31,120
Speaker 1: enough of them in there, he'd go clean all the bluegills.

1505
01:24:31,240 --> 01:24:38,040
Speaker 1: And we used to watch blue herons and they would

1506
01:24:38,080 --> 01:24:41,360
Speaker 1: never figure out that those things couldn't get away, and

1507
01:24:41,400 --> 01:24:45,719
Speaker 1: they would stalk that tank, you know, from fifty yards away.

1508
01:24:45,760 --> 01:24:48,320
Speaker 1: Like every morning, he'd lay in there and stalk that

1509
01:24:48,479 --> 01:24:51,200
Speaker 1: tank and look up over the edge and so careful,

1510
01:24:51,200 --> 01:24:53,040
Speaker 1: and you might be like he's just sitting there and

1511
01:24:53,080 --> 01:24:56,000
Speaker 1: just grab it, you know, but he'd make his strike

1512
01:24:56,080 --> 01:24:58,360
Speaker 1: and grab it. You know. But my favorite bird man

1513
01:24:58,439 --> 01:25:01,840
Speaker 1: is I think it transcends burden. But it's a turkey. Yeah,

1514
01:25:02,040 --> 01:25:02,719
Speaker 1: wild turkey.

1515
01:25:03,439 --> 01:25:05,040
Speaker 5: Yeah, almost the national symbol.

1516
01:25:05,520 --> 01:25:08,759
Speaker 1: Well you know, it's funny. We just talked about this today. Yeah,

1517
01:25:08,920 --> 01:25:11,320
Speaker 1: and it wasn't No, it wasn't really, but you know,

1518
01:25:12,280 --> 01:25:14,439
Speaker 1: it's a good it's a good story. It's a good story.

1519
01:25:14,479 --> 01:25:16,840
Speaker 1: It's a good story. So you know that whole deal

1520
01:25:16,880 --> 01:25:18,600
Speaker 1: that he was being like a little bit, he was

1521
01:25:18,640 --> 01:25:25,800
Speaker 1: being cute. Yeah, yeah, so e birds free, participations free,

1522
01:25:26,920 --> 01:25:32,400
Speaker 1: Merlin's free. We're with, Uh, we're with I told you this,

1523
01:25:32,439 --> 01:25:36,320
Speaker 1: but we're with a famous documentarian the other day and

1524
01:25:36,360 --> 01:25:38,680
Speaker 1: I was talking about how much I liked Merlin and

1525
01:25:38,720 --> 01:25:40,760
Speaker 1: he asked how much it was and I told him

1526
01:25:40,800 --> 01:25:42,080
Speaker 1: it was free, and he said, well, I'm going to

1527
01:25:42,160 --> 01:25:45,200
Speaker 1: get it because I don't buy anything on the internet,

1528
01:25:48,240 --> 01:25:50,920
Speaker 1: so it's free. And you don't even spy on people.

1529
01:25:51,160 --> 01:25:53,240
Speaker 3: No, No, we just want people to go out and

1530
01:25:53,479 --> 01:25:55,880
Speaker 3: look at birds and enjoy the natural world and care

1531
01:25:55,960 --> 01:25:56,799
Speaker 3: more about this stuff.

1532
01:25:56,880 --> 01:25:57,120
Speaker 5: Yeah.

1533
01:25:57,360 --> 01:26:03,120
Speaker 1: It's effective, man, I have it is. Uh. Yeah, I've

1534
01:26:03,200 --> 01:26:06,320
Speaker 1: learned a ton about birds man from from doing it.

1535
01:26:06,360 --> 01:26:08,280
Speaker 1: I need to start remembering them better. But I've learned,

1536
01:26:08,320 --> 01:26:09,639
Speaker 1: I've learned a ton about birds.

1537
01:26:09,800 --> 01:26:11,920
Speaker 3: Yeah, it's easy to kind of get those answers from

1538
01:26:11,920 --> 01:26:14,000
Speaker 3: Merlin and then not really think about it again. So

1539
01:26:14,840 --> 01:26:16,840
Speaker 3: we're thinking about features. It would be fun to, like,

1540
01:26:16,960 --> 01:26:18,360
Speaker 3: you know, test those skills over time.

1541
01:26:20,880 --> 01:26:25,200
Speaker 1: I'll tell you what though, It is so satisfying even

1542
01:26:25,200 --> 01:26:27,600
Speaker 1: though you don't recall the name it is. So it

1543
01:26:27,720 --> 01:26:31,040
Speaker 1: scratches the biggest itch to hear something off from the

1544
01:26:31,080 --> 01:26:32,720
Speaker 1: trees and then finally be able to put a name

1545
01:26:32,760 --> 01:26:34,639
Speaker 1: to it. And a lot of times it's some bird

1546
01:26:34,680 --> 01:26:37,400
Speaker 1: you heard about, you know, but you just you just

1547
01:26:37,439 --> 01:26:40,960
Speaker 1: didn't know. I didn't know what it was. So it's

1548
01:26:41,000 --> 01:26:45,479
Speaker 1: great and I love you. Guys put out so much

1549
01:26:45,479 --> 01:26:50,479
Speaker 1: great stuffs at the ornithology Lab. Yeah, it's impressive. So

1550
01:26:50,560 --> 01:26:52,640
Speaker 1: I encourage people to go and get in and participate

1551
01:26:52,640 --> 01:26:59,160
Speaker 1: in ibird Ebert not ibird participating eBird and overlog stuff

1552
01:26:59,160 --> 01:27:05,599
Speaker 1: you like to hunt. I'm joking, don't overlog something science

1553
01:27:09,400 --> 01:27:13,920
Speaker 1: and for you and for you uh Nevada Ruby Mountain hunters,

1554
01:27:14,000 --> 01:27:16,040
Speaker 1: keep keep logging them in because someday I'm gonna come

1555
01:27:16,080 --> 01:27:17,920
Speaker 1: look for one. Did you log yours? Yeah?

1556
01:27:17,920 --> 01:27:18,400
Speaker 5: They're in there.

1557
01:27:18,520 --> 01:27:19,000
Speaker 1: It's in there.

1558
01:27:19,080 --> 01:27:22,480
Speaker 5: Yeah, I think it might be a photode.

1559
01:27:22,479 --> 01:27:24,280
Speaker 1: You might regret that, man, because people want to know

1560
01:27:24,280 --> 01:27:27,960
Speaker 1: where those birds are. They're not native though not native.

1561
01:27:29,720 --> 01:27:31,280
Speaker 1: Any guys want to throw in that. I failed to

1562
01:27:31,280 --> 01:27:34,120
Speaker 1: ask about that. You think I should have asked about You.

1563
01:27:34,080 --> 01:27:36,559
Speaker 3: Haven't been playing as much trivia lately, so kartin then, though,

1564
01:27:36,560 --> 01:27:40,200
Speaker 3: would be fun of like just tossing some extra We're

1565
01:27:40,200 --> 01:27:43,559
Speaker 3: gonna throw you some bird sounds and play along. Yeah,

1566
01:27:43,640 --> 01:27:45,600
Speaker 3: play along. There's a couple of warm ups for you

1567
01:27:45,760 --> 01:27:46,280
Speaker 3: that you.

1568
01:27:46,200 --> 01:27:48,799
Speaker 1: Know, like easy one, Yeah, easy ones, like a mallard.

1569
01:27:48,880 --> 01:27:49,639
Speaker 1: Hit me on the mallard.

1570
01:27:53,160 --> 01:27:55,120
Speaker 5: That's actually really tough because they sound a lot like

1571
01:27:55,120 --> 01:27:57,599
Speaker 5: a black duck. She would have played like black duck.

1572
01:27:57,680 --> 01:27:59,679
Speaker 1: Yeah, don't do don't do any kind of trick stuff.

1573
01:28:06,760 --> 01:28:14,080
Speaker 1: I do this one.

1574
01:28:15,040 --> 01:28:16,480
Speaker 3: You totally know that one?

1575
01:28:16,400 --> 01:28:16,479
Speaker 5: Ye?

1576
01:28:16,600 --> 01:28:17,719
Speaker 1: Why do I know that? One?

1577
01:28:23,000 --> 01:28:26,719
Speaker 3: Most frequently heard in June Northern region birds?

1578
01:28:28,720 --> 01:28:30,040
Speaker 1: I mean, I know it, but I can't tell you

1579
01:28:30,080 --> 01:28:30,880
Speaker 1: what it is. I don't know.

1580
01:28:31,120 --> 01:28:34,559
Speaker 5: Think about caribou. Yeah, you're on a caribou hunt.

1581
01:28:34,800 --> 01:28:39,880
Speaker 1: Yeah that's right, that's right. Yeah. Yeah, Because we used

1582
01:28:39,880 --> 01:28:43,760
Speaker 1: to remember, like remember the three Stooges and Curly to

1583
01:28:43,840 --> 01:28:48,320
Speaker 1: have that. Yeah, yeah, we used to talk about. Yeah, yeah,

1584
01:28:48,320 --> 01:28:49,800
Speaker 1: I forgot about that one. I told you I wasn't

1585
01:28:49,800 --> 01:29:00,800
Speaker 1: gonna do. Yeah, Okay, I know that bird. I know

1586
01:29:00,920 --> 01:29:02,880
Speaker 1: that one. That's the one up high in the tree tops.

1587
01:29:02,920 --> 01:29:03,960
Speaker 3: Yep, that's the one.

1588
01:29:04,640 --> 01:29:07,800
Speaker 1: It's a yeah, he lives up in the canopy in

1589
01:29:07,840 --> 01:29:12,920
Speaker 1: the old Girl's forest, the Viriowainson's rush, Swainson's throw Okay,

1590
01:29:13,000 --> 01:29:14,040
Speaker 1: Slainson's thrush.

1591
01:29:14,160 --> 01:29:16,679
Speaker 3: Yeah. So Swainson's rush is in the old growth and

1592
01:29:16,840 --> 01:29:19,439
Speaker 3: you know, across the North America and the breeding grounds

1593
01:29:19,479 --> 01:29:21,799
Speaker 3: and then they go all the way to South.

1594
01:29:21,600 --> 01:29:22,960
Speaker 1: America for the winters they do.

1595
01:29:23,120 --> 01:29:24,759
Speaker 3: Yeah, so they're gonna be So that.

1596
01:29:24,680 --> 01:29:26,559
Speaker 1: One is the one that when you're bear hunting in

1597
01:29:26,560 --> 01:29:29,880
Speaker 1: Southeast Alaska, it's always doing that. And that's one of

1598
01:29:29,920 --> 01:29:32,800
Speaker 1: the ones I wanted to zapp with Merlin, right because

1599
01:29:32,800 --> 01:29:34,680
Speaker 1: I wanted to zappa with Maryland to find out what

1600
01:29:34,760 --> 01:29:36,799
Speaker 1: it was. And that was totally satisfied. But then forgot

1601
01:29:36,800 --> 01:29:37,800
Speaker 1: what his fame was.

1602
01:29:39,760 --> 01:29:42,479
Speaker 3: But yeah, these guys are going into the Andes for

1603
01:29:42,520 --> 01:29:46,440
Speaker 3: the winter. So you imagine them flying at night almost NonStop,

1604
01:29:47,400 --> 01:29:50,840
Speaker 3: all the way uh down to the spots. So you know,

1605
01:29:50,880 --> 01:29:53,519
Speaker 3: when we we're thinking about protecting ham Dad is not

1606
01:29:53,640 --> 01:29:55,679
Speaker 3: just you know, where they're breeding up in North America,

1607
01:29:55,760 --> 01:29:58,400
Speaker 3: but also working with groups in Columbia and Ecuador and

1608
01:29:58,439 --> 01:30:01,599
Speaker 3: Peru to make sure that the wintering grounds are protected

1609
01:30:01,800 --> 01:30:04,799
Speaker 3: for these species and the stops that they have in between.

1610
01:30:04,920 --> 01:30:05,960
Speaker 3: So a lot.

1611
01:30:06,080 --> 01:30:08,639
Speaker 1: So those birds you're listening to in southeast Alaska going

1612
01:30:08,680 --> 01:30:09,519
Speaker 1: to South America.

1613
01:30:09,800 --> 01:30:10,000
Speaker 5: Yeah.

1614
01:30:10,760 --> 01:30:10,960
Speaker 1: Yeah.

1615
01:30:11,240 --> 01:30:13,640
Speaker 2: One of the things to notice that that Jesse was

1616
01:30:13,680 --> 01:30:18,160
Speaker 2: talking about is you can see how small the wintering

1617
01:30:18,240 --> 01:30:23,160
Speaker 2: range is, and when they come back to breed, you

1618
01:30:23,200 --> 01:30:26,479
Speaker 2: see how huge it is they disperse.

1619
01:30:26,840 --> 01:30:27,080
Speaker 1: Yeah.

1620
01:30:27,200 --> 01:30:29,920
Speaker 2: So so that's that's why it's important to get this

1621
01:30:29,920 --> 01:30:33,160
Speaker 2: this full annual cycle picture, so we can you know,

1622
01:30:33,280 --> 01:30:38,120
Speaker 2: really pinpoint where they are down here to help you

1623
01:30:38,320 --> 01:30:40,599
Speaker 2: conserve habitat and whatnot.

1624
01:30:40,760 --> 01:30:42,799
Speaker 3: Steve, for listeners at home, what are you seeing?

1625
01:30:43,120 --> 01:30:47,200
Speaker 1: Oh? So he it's uh what it goes along the

1626
01:30:47,600 --> 01:30:52,720
Speaker 1: face of the Andes. So it's all across the you know,

1627
01:30:54,080 --> 01:30:56,600
Speaker 1: all across the north a little bit north of the

1628
01:30:56,640 --> 01:31:01,839
Speaker 1: Boreal forest, right, but like from coastal Alaska, all across Canada.

1629
01:31:02,720 --> 01:31:05,559
Speaker 1: But then when they migrate, they consolidate around the Andes

1630
01:31:05,600 --> 01:31:09,280
Speaker 1: in South America. Is that down the spine of the

1631
01:31:09,320 --> 01:31:11,960
Speaker 1: Andes or the or the East Face. It's the spine

1632
01:31:11,960 --> 01:31:15,240
Speaker 1: of the Andes and some like, you know, some more north,

1633
01:31:15,280 --> 01:31:18,920
Speaker 1: but they really concentrate down there, and then they're and

1634
01:31:18,960 --> 01:31:22,040
Speaker 1: then they're Pacific to Atlantic, across the Arctic or across

1635
01:31:22,080 --> 01:31:27,000
Speaker 1: the North. God man, they like they're like a global bird,

1636
01:31:27,080 --> 01:31:29,759
Speaker 1: not global, but just using huge amounts of territory.

1637
01:31:29,880 --> 01:31:31,360
Speaker 3: Yeah, that's really impressive.

1638
01:31:31,479 --> 01:31:34,840
Speaker 1: There's a lot of opportunities for vulnerability exactly. Yep.

1639
01:31:34,880 --> 01:31:38,600
Speaker 3: And you think about, you know, a bird like that migrating,

1640
01:31:38,680 --> 01:31:41,760
Speaker 3: they've got all those challenges along the way, whether it's

1641
01:31:41,920 --> 01:31:45,720
Speaker 3: you know, window strikes or big storms or you know,

1642
01:31:45,760 --> 01:31:47,920
Speaker 3: not having enough food before they take off on a

1643
01:31:47,960 --> 01:31:50,160
Speaker 3: long flight. There's lots of things that are contributing to

1644
01:31:50,760 --> 01:31:51,519
Speaker 3: major challenges.

1645
01:31:51,520 --> 01:31:54,639
Speaker 2: For a lot has to go right to get from

1646
01:31:54,680 --> 01:31:56,200
Speaker 2: the southern end of that range to the north.

1647
01:31:57,560 --> 01:31:58,559
Speaker 1: Kind'm gonna memorize that.

1648
01:31:58,560 --> 01:32:00,639
Speaker 3: Bird on a Swains and swain st Okay, So this

1649
01:32:00,680 --> 01:32:05,720
Speaker 3: one just guess like what state it was recorded, Just

1650
01:32:05,760 --> 01:32:06,600
Speaker 3: pick state.

1651
01:32:15,400 --> 01:32:17,759
Speaker 1: Nevada.

1652
01:32:18,439 --> 01:32:22,320
Speaker 3: So this one is Hawaii and it's a pretty special song.

1653
01:32:24,280 --> 01:32:28,160
Speaker 3: So you can hear it's kind of jazzy, bubbly. There's

1654
01:32:28,160 --> 01:32:32,640
Speaker 3: some pauses in there that's kind of interesting. And so

1655
01:32:32,920 --> 01:32:38,960
Speaker 3: this is the recording of a kawaioh and oos were

1656
01:32:39,000 --> 01:32:41,040
Speaker 3: her family and birds that lived on the Hawaiian islands.

1657
01:32:41,120 --> 01:32:45,360
Speaker 3: But they were hunted by the Polynesian emperors because they

1658
01:32:45,479 --> 01:32:47,800
Speaker 3: liked their feathers for the robes. They lost their habitat

1659
01:32:47,840 --> 01:32:50,840
Speaker 3: to cattle ranching, and pretty soon there was only one

1660
01:32:50,880 --> 01:32:54,439
Speaker 3: species of left, the kaio. And that bird that we

1661
01:32:54,479 --> 01:32:58,639
Speaker 3: were listening to was the last quaio is the last

1662
01:32:58,680 --> 01:33:00,800
Speaker 3: male to ever lived.

1663
01:33:01,680 --> 01:33:03,240
Speaker 5: And so he would play them again.

1664
01:33:03,479 --> 01:33:07,960
Speaker 1: Yeah, that's the last of its kind. Who recorded that

1665
01:33:08,280 --> 01:33:08,720
Speaker 1: This was.

1666
01:33:08,680 --> 01:33:18,680
Speaker 3: A fine prat nineteen eighty eight, And when you hear

1667
01:33:18,720 --> 01:33:20,600
Speaker 3: it again, it takes on this whole new meaning. Right,

1668
01:33:20,640 --> 01:33:23,960
Speaker 3: it's kind of melancholiny. And you hear those spaces in

1669
01:33:23,960 --> 01:33:27,479
Speaker 3: between the notes, and that's where the female would have

1670
01:33:27,800 --> 01:33:30,639
Speaker 3: completed the duet. So o o's were a dueting species,

1671
01:33:31,200 --> 01:33:34,320
Speaker 3: and we're only hearing half the song. We don't really

1672
01:33:34,360 --> 01:33:36,240
Speaker 3: know what the full song looks like.

1673
01:33:36,479 --> 01:33:41,040
Speaker 1: And eighty eight eighty eight, Yeah, nineteen eighty eight, I

1674
01:33:41,080 --> 01:33:43,680
Speaker 1: don't know. I didn't know a bird went extinct.

1675
01:33:43,280 --> 01:33:47,040
Speaker 3: In eighty eight yeah, it's this isn't you know a

1676
01:33:47,040 --> 01:33:49,200
Speaker 3: bird that's well known. There's still a lot of stories

1677
01:33:49,200 --> 01:33:52,040
Speaker 3: out there like that, are you know the actually Wi

1678
01:33:52,120 --> 01:33:56,320
Speaker 3: curlew went extinct in you know, probably the mid nineteen hundreds.

1679
01:33:56,360 --> 01:33:59,759
Speaker 3: But yeah, these are all sad stories that we're losing

1680
01:33:59,760 --> 01:34:02,519
Speaker 3: these species, but they're really meant to be inspiring that

1681
01:34:02,560 --> 01:34:05,080
Speaker 3: there's still an opportunity to save some of the species

1682
01:34:05,080 --> 01:34:06,759
Speaker 3: that are declining now.

1683
01:34:08,320 --> 01:34:11,760
Speaker 5: Well, and that and that Kawai oh you know, as

1684
01:34:11,800 --> 01:34:15,600
Speaker 5: people figured this out and this bird is kind of

1685
01:34:15,640 --> 01:34:20,679
Speaker 5: rediscovered in this area, it really did start to focus

1686
01:34:20,760 --> 01:34:24,040
Speaker 5: on conservation of the of of both in Kawai and

1687
01:34:24,080 --> 01:34:27,040
Speaker 5: other Hawaiian islands and trying to figure out how you

1688
01:34:27,080 --> 01:34:30,520
Speaker 5: pull together these partnerships of groups like the nature conservancies

1689
01:34:31,040 --> 01:34:36,120
Speaker 5: states to think about and you know, how you how

1690
01:34:36,160 --> 01:34:40,000
Speaker 5: you can set aside habitat, create connectivity to move for

1691
01:34:40,120 --> 01:34:45,280
Speaker 5: birds to be able to move and oftentimes, you know,

1692
01:34:45,320 --> 01:34:49,479
Speaker 5: it's it's birds that are the things that first highlight

1693
01:34:49,520 --> 01:34:51,679
Speaker 5: that there's something that could be going wrong in a system.

1694
01:34:51,720 --> 01:34:55,559
Speaker 5: And so why while Kawaii oh oh is you know,

1695
01:34:55,640 --> 01:34:59,880
Speaker 5: it's the first the first family of birds that we've lost,

1696
01:35:01,320 --> 01:35:04,680
Speaker 5: you know, really in modern times where it's it's not

1697
01:35:04,760 --> 01:35:07,320
Speaker 5: just that individual species, but as you go up and

1698
01:35:07,360 --> 01:35:10,599
Speaker 5: it's not just you know, the genus that those birds

1699
01:35:10,600 --> 01:35:14,559
Speaker 5: were in, but a fairly long lineage of birds that

1700
01:35:15,360 --> 01:35:21,160
Speaker 5: you know, all of the representatives of them are now gone.

1701
01:35:21,840 --> 01:35:25,040
Speaker 5: That that power that birds have, you know now when

1702
01:35:25,080 --> 01:35:27,599
Speaker 5: you hear that sound and that bit of knowledge that

1703
01:35:27,640 --> 01:35:31,439
Speaker 5: you have that it's human actions that have caused that

1704
01:35:31,680 --> 01:35:35,240
Speaker 5: to take place. You know, really has this change you

1705
01:35:35,240 --> 01:35:37,639
Speaker 5: know that I think when you hear that sound now

1706
01:35:39,120 --> 01:35:42,280
Speaker 5: is the power of birds and the ability to say like,

1707
01:35:42,400 --> 01:35:45,360
Speaker 5: oh my gosh, this is this is not a sustainable

1708
01:35:45,400 --> 01:35:48,240
Speaker 5: way that we're living. This isn't going to be It's

1709
01:35:48,240 --> 01:35:52,200
Speaker 5: certainly not sustainable for them, they're gone. It's probably not

1710
01:35:52,320 --> 01:35:55,639
Speaker 5: sustainable for us. We really need to change our behavior.

1711
01:35:55,640 --> 01:35:58,160
Speaker 5: And then you can look at things like bald eagle.

1712
01:35:58,560 --> 01:36:01,240
Speaker 5: You know that the same thing was happening to populations

1713
01:36:01,240 --> 01:36:04,160
Speaker 5: of bald eagle or you know, you may find this

1714
01:36:05,000 --> 01:36:07,160
Speaker 5: hard to believe, but Canada Goose. You know, you look

1715
01:36:07,200 --> 01:36:11,280
Speaker 5: at maximum Canada Goose, the success or maybe even over

1716
01:36:11,439 --> 01:36:15,000
Speaker 5: success of the effect that human action can have when

1717
01:36:15,040 --> 01:36:18,920
Speaker 5: we understand sort of what's happening. You know, people are

1718
01:36:19,040 --> 01:36:22,040
Speaker 5: people are pretty amazing. We can shrew things up real well,

1719
01:36:22,240 --> 01:36:25,000
Speaker 5: but we also have the power to change our actions,

1720
01:36:26,160 --> 01:36:30,080
Speaker 5: you know, protect natural places, restore the places that we've lost,

1721
01:36:30,120 --> 01:36:33,880
Speaker 5: and then change the way that we're living with natural systems.

1722
01:36:34,240 --> 01:36:37,840
Speaker 1: Yeah. Uh. You know, my dad was a hunter in

1723
01:36:37,880 --> 01:36:41,600
Speaker 1: the late forties early fifties, and their ideas and attitudes

1724
01:36:41,600 --> 01:36:44,320
Speaker 1: about geese were so different. And for a long time

1725
01:36:44,360 --> 01:36:47,600
Speaker 1: there was a goose on the Sand County Almanac. Yeah,

1726
01:36:47,640 --> 01:36:49,519
Speaker 1: I mean it was like a special thing. Yeah, and

1727
01:36:49,520 --> 01:36:51,760
Speaker 1: he wrote all about like the call the goose, you know,

1728
01:36:51,920 --> 01:36:53,840
Speaker 1: and now it people like just go golfing, dude. It's like,

1729
01:36:53,920 --> 01:36:56,760
Speaker 1: you right, exactly is there more?

1730
01:36:56,960 --> 01:36:58,400
Speaker 3: Yeah, we got here's one.

1731
01:36:58,560 --> 01:36:59,680
Speaker 1: I haven't got any right yet.

1732
01:37:00,080 --> 01:37:02,400
Speaker 3: Inching out So.

1733
01:37:12,080 --> 01:37:14,320
Speaker 5: So you can hear the Swainson's thrush in there. There's

1734
01:37:14,360 --> 01:37:15,080
Speaker 5: lots of stuff.

1735
01:37:16,120 --> 01:37:16,679
Speaker 2: Give me a hint.

1736
01:37:16,720 --> 01:37:17,559
Speaker 5: It's not a bird.

1737
01:37:17,920 --> 01:37:22,200
Speaker 1: Ess is trying to trying. She's trying to trick you,

1738
01:37:22,439 --> 01:37:24,320
Speaker 1: like I don't know, like a porcupine, I don't know.

1739
01:37:24,800 --> 01:37:25,200
Speaker 1: A beaver.

1740
01:37:26,280 --> 01:37:28,600
Speaker 3: Yeah, so I dropped that microphone on the top of

1741
01:37:28,600 --> 01:37:29,360
Speaker 3: a beaver lodge.

1742
01:37:29,600 --> 01:37:30,400
Speaker 1: You can hear them in there.

1743
01:37:30,439 --> 01:37:32,080
Speaker 3: And then you're hearing the kits in the lodge.

1744
01:37:32,280 --> 01:37:34,560
Speaker 1: Yea, my wife was telling me about the Yeah, my

1745
01:37:34,640 --> 01:37:36,680
Speaker 1: wife was listening to recordings of what they sound like.

1746
01:37:36,720 --> 01:37:38,280
Speaker 1: I was like, that's not true. She's like, no, man,

1747
01:37:38,280 --> 01:37:39,240
Speaker 1: they're vocal, you know.

1748
01:37:40,000 --> 01:37:42,439
Speaker 3: Yeah, like for the whole hour they just kept doing that.

1749
01:37:42,520 --> 01:37:44,679
Speaker 1: But let me hear that again. My wife was playing

1750
01:37:44,720 --> 01:37:57,880
Speaker 1: it's playing that for me. Where is that?

1751
01:37:57,880 --> 01:38:01,160
Speaker 3: That's about runner miles north of Toronto.

1752
01:38:04,120 --> 01:38:06,240
Speaker 1: Yeah, my wife was talking about how much noise beavers

1753
01:38:06,240 --> 01:38:07,240
Speaker 1: make and I was like, no, they don't.

1754
01:38:08,800 --> 01:38:10,600
Speaker 3: I didn't know until I dropped the microphone on the

1755
01:38:10,600 --> 01:38:14,479
Speaker 3: top of the lodge. But then like one more kind

1756
01:38:14,479 --> 01:38:15,000
Speaker 3: of out there.

1757
01:38:22,680 --> 01:38:23,679
Speaker 1: That's a bomb falling.

1758
01:38:25,680 --> 01:38:26,760
Speaker 5: Not what I thought.

1759
01:38:26,800 --> 01:38:27,800
Speaker 1: It was a space ship.

1760
01:38:30,520 --> 01:38:31,559
Speaker 3: The UFOs didn't.

1761
01:38:37,800 --> 01:38:39,639
Speaker 1: Oh, man, I have no idea. That's crazy.

1762
01:38:39,720 --> 01:38:41,000
Speaker 3: So this is bearded seal.

1763
01:38:41,720 --> 01:38:45,120
Speaker 1: Oh that's a bearded seal microphone underwater.

1764
01:38:51,320 --> 01:38:52,080
Speaker 2: That's crazy.

1765
01:38:53,400 --> 01:38:56,120
Speaker 3: It turns out there starting to sing louder now that

1766
01:38:56,160 --> 01:39:00,679
Speaker 3: there's more industrial noise under water this this are increasing

1767
01:39:00,680 --> 01:39:02,000
Speaker 3: their volume to try to compensate.

1768
01:39:05,160 --> 01:39:11,400
Speaker 1: That's great noise, loud and proud what I get zero?

1769
01:39:15,200 --> 01:39:16,559
Speaker 5: But are you glad you played?

1770
01:39:16,800 --> 01:39:18,519
Speaker 1: I'm glad I played. I got a zero and I

1771
01:39:18,520 --> 01:39:22,280
Speaker 1: think that there's a problem in my brain with like,

1772
01:39:22,320 --> 01:39:25,559
Speaker 1: there's the problem my brain with remembering, uh, you know,

1773
01:39:26,280 --> 01:39:28,120
Speaker 1: like as signing noise.

1774
01:39:28,439 --> 01:39:30,400
Speaker 3: Well, you know, it might help you to look at

1775
01:39:30,439 --> 01:39:33,280
Speaker 3: the spectrograms and the Merlin app because if you're more visual,

1776
01:39:33,600 --> 01:39:35,280
Speaker 3: you can kind of start to see the pattern of

1777
01:39:35,320 --> 01:39:38,759
Speaker 3: notes like in a picture, which really helps me remember.

1778
01:39:39,040 --> 01:39:43,320
Speaker 1: So, yeah, but I can do. I can listen to

1779
01:39:43,400 --> 01:39:45,040
Speaker 1: f M radio and tell you what song it is,

1780
01:39:45,080 --> 01:39:46,400
Speaker 1: three three beats in.

1781
01:39:50,439 --> 01:39:52,680
Speaker 2: It's just practice familiarity.

1782
01:39:53,680 --> 01:39:56,880
Speaker 5: Just swab out your like, yeah, you're.

1783
01:40:00,080 --> 01:40:02,479
Speaker 3: Driving around and just put on bird. So that's what

1784
01:40:02,520 --> 01:40:02,800
Speaker 3: I did.

1785
01:40:03,840 --> 01:40:06,439
Speaker 1: That's great. Thanks, thanks for doing that. Well, thank you

1786
01:40:06,520 --> 01:40:09,320
Speaker 1: very much for coming on. I hope people keep. I

1787
01:40:09,320 --> 01:40:11,280
Speaker 1: hope people go and get the tools and start logging

1788
01:40:11,280 --> 01:40:15,960
Speaker 1: their bird stuff and be like a citizen scientist.

1789
01:40:16,000 --> 01:40:19,639
Speaker 5: Man, Well, we'll see you on there, will Yah.

1790
01:40:19,840 --> 01:40:21,320
Speaker 1: We'll be able to look up my stuff and check

1791
01:40:21,360 --> 01:40:27,760
Speaker 1: my work. Yeah, give you some feedback. You're doing it wrong.

1792
01:40:27,840 --> 01:40:29,519
Speaker 5: We gotta we got a team, and we got a

1793
01:40:29,520 --> 01:40:33,639
Speaker 5: team in Montana that'll probably be in touch if you report.

1794
01:40:33,840 --> 01:40:37,360
Speaker 5: If you report that cock of the rock, they will

1795
01:40:37,400 --> 01:40:39,120
Speaker 5: definitely flag you.

1796
01:40:39,400 --> 01:40:43,479
Speaker 1: Oh really, this they monitor it. We we keep We

1797
01:40:43,560 --> 01:40:47,799
Speaker 1: do keep a list of we do keep a list

1798
01:40:47,840 --> 01:40:51,280
Speaker 1: of the birds we see in certain areas, and we

1799
01:40:51,360 --> 01:40:52,800
Speaker 1: keep a good list of the birds we see in

1800
01:40:52,840 --> 01:40:55,840
Speaker 1: our yard. And it was fun, is ye today I heard

1801
01:40:56,520 --> 01:40:59,840
Speaker 1: I was on the phone with someone I was gonna

1802
01:40:59,840 --> 01:41:01,920
Speaker 1: work call, and I was hearing this bird. I was like, man,

1803
01:41:01,920 --> 01:41:03,679
Speaker 1: I don't think that's like. It was like a hawk

1804
01:41:03,760 --> 01:41:06,160
Speaker 1: and a tree but didn't quite sound like a redtail,

1805
01:41:06,640 --> 01:41:10,679
Speaker 1: but doing that like again and again, and I actually

1806
01:41:10,680 --> 01:41:12,400
Speaker 1: at one point said I have to go from it.

1807
01:41:12,479 --> 01:41:16,559
Speaker 1: I'll call you right back. Hung up. The call opened up, Merlin,

1808
01:41:16,680 --> 01:41:18,479
Speaker 1: because I couldn't. Weirdly, I couldn't open it up while

1809
01:41:18,520 --> 01:41:19,200
Speaker 1: I was on the phone.

1810
01:41:19,720 --> 01:41:21,840
Speaker 3: Oh yeah, that was your the audio.

1811
01:41:23,880 --> 01:41:28,000
Speaker 1: Yeah, that was pissed about that. So I hung up

1812
01:41:28,040 --> 01:41:31,639
Speaker 1: my call, turned it on. That bird quit doing that noise.

1813
01:41:33,280 --> 01:41:35,280
Speaker 1: Called the person back and minute I got him back

1814
01:41:35,280 --> 01:41:37,639
Speaker 1: on the phone and started up again, hung up, turned

1815
01:41:37,680 --> 01:41:41,439
Speaker 1: it back on. The bird quit. After the call I took,

1816
01:41:41,600 --> 01:41:43,439
Speaker 1: I told my kid to take that phone, and I said,

1817
01:41:43,439 --> 01:41:44,800
Speaker 1: I want you to go over in the neighbor's yard

1818
01:41:44,800 --> 01:41:46,360
Speaker 1: and find out that bird. And he came back a

1819
01:41:46,360 --> 01:41:49,000
Speaker 1: while later said he never did it again. So to

1820
01:41:49,040 --> 01:41:53,920
Speaker 1: this day, it's still a mystery. But I was going

1821
01:41:54,000 --> 01:41:56,320
Speaker 1: to add it to the Ronela family list, but wasn't

1822
01:41:56,360 --> 01:41:58,920
Speaker 1: able to do it. All right, But thank you guys

1823
01:41:59,000 --> 01:42:02,880
Speaker 1: very much. I appreciate it. Man. Yeah, I hope that

1824
01:42:02,920 --> 01:42:07,439
Speaker 1: you're able to solve problems and and help us make

1825
01:42:07,479 --> 01:42:08,720
Speaker 1: good habitat decisions.

1826
01:42:09,920 --> 01:42:10,120
Speaker 5: Yeah.

1827
01:42:10,280 --> 01:42:12,519
Speaker 1: Well, it takes a team, yeah, to save to save

1828
01:42:12,560 --> 01:42:16,520
Speaker 1: America's birds, So thank you very much. O.

1829
01:42:19,920 --> 01:42:31,680
Speaker 6: Ride on on the seal Gray, shine like silver in

1830
01:42:31,840 --> 01:42:43,800
Speaker 6: the sun. Ride ride on alone, sweetheart.

1831
01:42:44,680 --> 01:42:51,400
Speaker 4: We're done beat this damn horse today, taking her new one. Ride.

1832
01:42:54,920 --> 01:43:00,599
Speaker 4: We're done beat this damn horse today, So take your

1833
01:43:00,640 --> 01:43:02,720
Speaker 4: new one and ride on.

1834
01:43:11,560 --> 01:43:11,920
Speaker 6: Mhm.