WEBVTT
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And we're live.
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I'm sure that'll be in the podcast now.
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Definitely.
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Welcome, Hive Mind.
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We are buzzing with excitement about today's guest.
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We loved her so much the first time around.
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We had to have a repeat guest in Dr.
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Erica Feuerbacher.
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And joining her today is her colleague, KPA dog trainer and PhD applied animal behaviorist, Dr.
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Sally Dickinson.
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These wonderful doctors are joining us to talk about conservation detection dogs and using citizen science in their research.
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This is the Behavior Buzz, a veterinary behavior podcast that aims to bring cutting-edge scientific information and education to pet parents, behavior professionals, and the veterinary community.
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I'm your co-host, Dr.
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Amy Pike, a board-certified veterinary behaviorist.
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And I'm your other co-host, Dr.
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Amy Lern, also a board-certified veterinary behaviorist.
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Hello, Doctors, Erica and Dr.
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Sally.
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We are thrilled to have you join us for a morning of fun and ridiculosity.
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So we just watched, well no, it's not.
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But we last night, my family and I just watched Wicked for Good.
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And that it reminded me of like the first Wicked, right?
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Like they make all these crazy words up.
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And I was like, wouldn't it be just fun if we made up some words?
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I love it.
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I love it.
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So make up a Z word.
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I know.
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I couldn't find any.
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Oh my goodness.
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Um, so we are coming to you live from the PRN PharmaCow Studios.
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You guys know what PRN stands for, right?
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My stomach is growling, so I'm gonna say probably requires nachos.
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Nachos.
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Nachos that go great with the drink I'm drinking.
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Yeah, exactly.
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Does not mean nachos.
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I wish that it meant nachos.
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Um, it does mean, however, pro reinata, a Latin phrase meaning as the circumstance arises, and do you know how long the veterinary community has been trusting PRN Pharmacal to be here when needed with industry-leading research and innovative products to improve animal health and quality of life?
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Yes, since 1978, which is also back when the USDA was ramping up biological control and invasive pest monitoring to tackle the increasing problem of invasive agricultural pests, which is kind of what we're talking about today.
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How are you?
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You are a smarty baby.
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I am super smart with my AI search.
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The computer did it for you.
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The computer did it for me.
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Yep.
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Uh PRN PharmaCal is a proud sponsor of the Behavior Buzz podcast and is committed to the physical and emotional health of animals everywhere.
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Which makes sense why their slogan is PRN PharmaCal, here when you need us.
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Alright, well, let's get started here because I need to know what's getting us all buzzed today.
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I went on Bug Theme and I'm drinking a June bug cocktail, which is a sweet tropical drink made with Midori, coconut rum, banana liqueur, pineapple juice, and citrus juice.
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Oh, that sounds very tropical.
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Summery.
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I love it.
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I also went with a bug theme today.
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I'm drinking a butterfly martini, which has butterfly pea flower gin.
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Uh specifically, I'm using Empress 1908, Elderflower liqueur, and lemon juice.
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The cool thing about this drink is that it changes from blue to purple when you add the lemon juice.
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So it's super fun.
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That is fun.
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I love pea powder butterfly pea powder, pea powder.
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Whatever.
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Whatever.
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That's the stuff that changes.
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Yeah, yeah, yeah.
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What about you guys, Dr.
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Erica and Dr.
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Sally?
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What's getting y'all buzzed today?
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I picked a bug-themed one as well and have the bee's knees, which is also a gin drink.
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And I'm with you.
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I love the uh the butterfly pea in the gin as well.
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That's awesome.
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Yeah, yeah.
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Changing colors.
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Sally, what about you?
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Sticking with the gin, I am doing a traditional uh British drink, just a simple gin and tonic, but always remembering those floral notes from the gin that come from our bugs.
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So it's in there, just slightly more reserved.
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Didn't our last British guest also have the guest also had a gin and tonic?
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So fun.
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I love it.
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I love it.
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Sounds like it's on brand.
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I do love a good G and T.
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Me too.
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That is my go-to drink, really.
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Well, remember, you can find all of our signature cocktail and mocktail recipes on our website, behaviorbuzz.com.
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That's Behavior Buzz with six Zs because we are seriously excited.
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Seriously.
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I was trying to think of something to go with.
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So much for your made-up work.
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Seriously.
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I love it.
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So let's dive in.
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Let's get this whole show in the road.
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The research we are talking about today is about teaching dogs to detect spotted lanternflies, which we did talk about in a previous episode with Melissa McHugh McGrath, who was one of the participants in this research.
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So, how did you both, as the researchers, come up with this topic to study?
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So, you know, we have a colleague, Dr.
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Nathan Hall at Texas Tech, who does a lot of olfactory work.
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Um, and we are in an ag department here at Virginia Tech.
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Uh so trying to meld our uh foci of dog behavior and agricultural outcomes seemed really useful.
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And when I was in Montana, I'd go hiking with my dog every day.
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And we had some partnerships with uh working dogs for conservation out there with our dog training program.
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And I always just thought their work was fascinating and amazing.
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And every time I was hiking with my dog, I kept thinking, wouldn't it be fun if I were out here hiking and we were doing something useful for the community?
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And that really brought up the idea of this community-based uh sort of solution to environmental and agricultural issues.
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So now when we're out here at Virginia Tech, uh we had an opportunity to partner with Texas Tech on this program.
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Uh, and we brought in one of our sort of plant pathology specialists, Dr.
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Mizuho Nita, who kind of helped guide us on what would be some useful targets.
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And we landed on the spotted lantern fly for a number of reasons.
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One, that they are uh increasingly problematic in our area and spreading west, um, that they have cryptic uh egg masses, so they are hard to detect, and that's something where the dogs might outperform humans.
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Um, so it was kind of a uh from an initial idea of we'd love to do something community science-based, and then let's find a find a target.
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That's awesome.
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I love that.
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Yeah, are there um similar studies for like other invasive species out there?
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Yeah, there are a bunch of other aspects that uh conservation detection dogs use, everything from a endangered species, like the studies out of the University of Washington and conservation uh detection dogs out there looking for killer whale scat so that they can analyze sort of population densities, pregnancy, diet, those kind of questions.
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To invasive species, we can use dogs to detect different plants like the yellow uh star thistle out in the Midwest, which is choking out other native species.
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So, you know, uh allowing a dog to sort of detect those areas so they can be eradicated without using sort of broad spectrum herbicides.
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And then sometimes we just want to look at uh um what is in else is going on in the environment.
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So uh, for example, the Hudson mink study, which looked at sort of the mink as the sentinel uh mammal in the environment, uh, meaning that it was uh, you know, living in the water, drinking the water, eating the fish, doing all of the things.
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So it's sort of at the top of the food chain, what may it be accumulating in its body and looking at its scat to analyze like heavy metals in the environment?
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So sort of a uh a secondary byproduct and being able to find either the actual evidence of the species, their maybe their egg sites or their nest sites or the evidence that they were there for a variety of different reasons.
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That's amazing.
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I wish that they could detect poison ivy and find all the poison ivy around my house, and then we could eradicate that.
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That sounds like a good project.
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I know some of them.
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I could just go out there.
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Like, yeah, I'll get it.
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Oh, it's here.
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I know where it is.
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I got some.
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It's over here somewhere.
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Yeah.
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Um, I also it it's fascinating that you're saying that they're finding the scat and detecting this scat so that you can kind of track some of those other parameters.
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But if you have multiple species that have similar diets, is it just a matter of, I mean, like I feel like digestive food is gonna smell similarly, but I guess each individual animal also lends their scent in as they're passing through the digestive tract.
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And is that especially the killer whale?
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That is crazy.
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Like that's so fun.
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For some of the species where they're they're very identical and they're uh experiencing the world in very similar ways and eating very similar things, there may not be a lot of differentiation.
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But it's unusual to find two examples of those living in the same context.
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So sometimes it's oh, you know, we have this species in this context and this species in this context.
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If we put those together, they might be very similar, but separate context, then we can actually differentiate them from any of the other background noise.
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There's the light bulb moment.
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That's why we have scientists on our show.
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Exactly.
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We learned so much.
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Can you I know, right?
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Every moment.
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Um, can you explain really what is meant by citizen science and how that was used in this particular research project?
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Yeah, so the citizen science, sometimes we call it community science, um, is really bringing sort of average citizens, average community members into helping collect data or uh implement an intervention.
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And in this case, we recruited about 180 dog handler teams across the United States that wanted to participate.
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We actually had way more than that in the thousands, which was really encouraging that so many people wanted to do something meaningful with their dog and go out and help their community while playing with their dog.
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So that was really, I think, a buoy to us of how excited people are about projects like this.
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But we brought in those average citizens that uh a lot of them had done some nosework with their dogs, but they were not purpose-bred dogs.
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They were not professional dog uh handlers or trainers necessarily.
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And then we did pair them up.
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We um, and this is a lot of what Dr.
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Sally did was uh put them into uh regional teams that were kind of supported by a local trainer, and that trainer is the person that we would send samples to, and they'd you know, help the folks train through, you know, this new odor.
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But really, we're looking for just average folks that have probably day jobs doing other things and want to participate with their dogs in solving local issues.
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That's so cool.
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So obviously, we know that dogs can smell a lot better than we do.
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So, what odor are the dogs actually learning to identify with the spotted lantern flies?
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So we're training them to detect the egg masses.
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There are a range of targets, right?
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You could do individuals, but the egg masses are uh where we're gonna get more bang for our training buck that uh we can eradicate, eradicate uh 30 to 50 at a time rather than one adult, one adult, one adult.
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So we can hopefully have a bigger impact by going after those egg masses.
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And that's likely where the dogs are gonna be really useful anyway, because they are cryptic and really hard to see, and that's where humans are gonna struggle to identify them.
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Yeah, how and how difficult is it to teach a dog to discriminate like such a specific scent of that egg mass?
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I'll let Sally take this one.
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So it's it's kind of a big question.
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There's a lot to unpack there.
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But when we think about dogs, they've evolved to live their world in an olfactory sort of metric, right?
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We're very visual, we see things, we think in pictures, we can recall in our mind's eye, quote unquote, uh, sort of images.
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And dogs do that through sense.
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So, you know, whether it's from the structures and the sensory sort of organs that they have that, you know, are a lot more prevalent than than we have in humans, or it's their physiology that allows them to smell in stereo and understand uh gradients of odors much more clearly than we can, that we do the equivalent of in vision.
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Uh, they've evolved to understand the world in terms of scent.
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And that's relevant for their evolution, so that they could understand the difference between potentially a threat that was coming to aggress them and their next meal or uh a potential mating partner, that they could understand those just through through the scent.
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And and how to sort of explain that to the dog is part of our job as humans, right?
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They're scenting all of the time and they're they're experiencing this world in this three-dimensional, multifaceted sort of uh array of odas.
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And it's up to us as the humans to sort of appreciate that and then add the emphasis on the places that that we're interested in.
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And because they're so biddable and uh affiliative through our you know uh joint sort of evolution together, we're able to do that.
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But oftentimes it's the humans that adds the confusion in there, the dogs, the dogs are senting things anyway.
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I feel like I heard now.
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I'm ashamed that I don't know this answer because I feel like I should, and I feel like I read it or saw it in some research somewhere about each nostril.
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Like if they sniff towards it one nostril, it's a new scent or something like that.
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And the other nostril, I don't know.
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Do you does that sound familiar to either one of you?
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Do you know what I'm talking about?
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I just make that up.
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She's just making it up.
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You're close, you're really close.
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So they can smell in stereo, so where um they can perceive sort of directionality, right?
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Where we do with our hearing, um even my hearing that's you know been abused by a fire truck for so long, I can still hear in stereo.
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Dogs do the same with their smelling.
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So just standing still or moving through the environment, they can appreciate this has more parts per million on the right side than this on the left side.
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Um, but then other processes are involved.
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Uh when they breathe in, part of that inhale goes into their lungs for biological respiration for all of the things needed for life.
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But part of that is funneled up into the uh the uh sensory areas that can appreciate those different odors that are in the environment.
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All of that is combined with taste.
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So it's basically the all the same sort of functions as taste and smell.
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It's all combined into one.
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And then that can be exhaled at a different rate than their breathing for respirations for biological processes.
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And then you add in uh just even the physiology of the slits on the side of their nose.
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When they exhale, the exhalation part goes out to the side where they can bring in sort of new fresh samples so they get that clean appreciation instead of repeat analysis of the same air that they've just exhaled.
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So so, really, when we think about it, they have evolved and are are just physiologically set up to be experts at this, and and we're simply tapping into that.
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That's amazing.
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So tell us a little bit more about what you found out from your first study.
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Did they do a good job?
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Can any dog do this?
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They did an amazing job.
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And coming from a professional detection dog background, um they they outperformed any sort of uh uh preconceived notions that I at least I had going into it.
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I know dogs can smell things, I knew they were gonna be good, but we always have this idea that you know certain dogs can do this work.
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Uh going into what we call the odor recognition test, which is sort of a standardized 10 trials, six boxes that all look identical.
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You have a minute and a half to examine the boxes by scent alone, not picking up and looking at them, but sent alone, a minute and a half to make a decision of which one has the target in, or to say that there is no target in that array, time after time after time again.
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So a pretty high pressure test.
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Um, they were able to do that at 82% sensitivity.
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And that means if the target was there, 82% of the time they were saying, yep, it's this one, we're getting it right.
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Their sensitivity was a little bit less, which looks at if the target's not there, how well can the dog say, nope, it's all clear, which is understandable.
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You sort of get this inverse relationship between sensitivity and specificity.
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And for an invasive species, we care more about um finding all of the targets that are potentially there versus saying, nope, there's no targets, right?
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If we if we have a little hiccup on the no target one, so be it.
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Um, but we really want to find those individual targets.
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So that that was amazing metrics.
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And then uh as they moved out into the field trials, their sensitivity dropped a little bit, but they're looking for half a gram of material concealed in a variety, uh, an absolute technical or array of of odors that are out there.
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And some of that ability, I think, is is the handless understanding how to work the dog in that environment and and sort of move through that space effectively.
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And what is the distance?
00:18:08.720 --> 00:18:18.319
Like, I know the dogs can smell for miles, but what is the average, like either when you're working with the boxes or if you're out in nature, you know, do they have to be right on top of it?
00:18:18.559 --> 00:18:20.799
Is there an air scent associated?
00:18:20.960 --> 00:18:26.000
Like, how far away to they kind of really collimate in on what they're looking for?
00:18:26.480 --> 00:18:31.519
If we had the answer to that question, we would be ruling all detection things.
00:18:31.680 --> 00:18:34.799
That's the million-dollar question of all detection work.
00:18:35.039 --> 00:18:35.519
Yeah.
00:18:35.680 --> 00:18:42.400
Um, and it actually we touched it a little bit in our second part of our study, which was our range of detection data.
00:18:42.559 --> 00:18:47.039
And what we found there was it was more sort of asking that exact question.
00:18:47.279 --> 00:18:52.720
How closely uh will they miss a target and how far away will they be able to detect a target?
00:18:52.799 --> 00:19:02.240
And what we found there was between zero and five meters, so up to about 15 feet, um, they were able to detect the targets that we placed in those sort of environments.
00:19:02.400 --> 00:19:13.200
So that took out that handler influence of how I'm going to present the search area to my dog and just asked if my dog is following the straight line, um, you know, how close, how far.
00:19:13.440 --> 00:19:30.160
Um, so again, that's half a gram of a very subtle odor that blends very closely with background odors of grass and bark and other insects that are in the space, and they were still able to detect it at that like 15 feet away range.
00:19:30.319 --> 00:19:32.559
So, again, pretty incredible.
00:19:33.359 --> 00:19:33.839
I believe it.
00:19:34.000 --> 00:19:41.119
My dog is um scenting all my backyard and looking for the roots of one specific tree, and then he'll dig a hole and find the root.
00:19:41.200 --> 00:19:49.039
And I'm like, yeah, now I've holes all over where all the roots of this one tree are, and we're gonna like create a pathway to the tree.
00:19:49.680 --> 00:19:51.920
Oh, he's a he's a monster.
00:19:53.920 --> 00:20:00.880
One of the one of the things that I just absolutely love about this project is it does empower kind of your everyday dog owner, right?
00:20:00.960 --> 00:20:06.160
Like you had trainers involved, obviously, as you're like your kind of cohort lead or whatever.
00:20:06.319 --> 00:20:09.680
But so why is involving the public so valuable?
00:20:09.839 --> 00:20:15.279
And and how does citizen science actually expand what researchers are able to accomplish?
00:20:16.240 --> 00:20:27.279
Yeah, that's part of what I think we're most excited about is not just showing that dogs can detect that, but actually providing, you know, a mechanism where a lot of folks could go out and kind of help solve this problem.
00:20:27.359 --> 00:20:31.119
And that's where bringing in the community members is really powerful.
00:20:31.279 --> 00:20:36.960
Um, one of the things, you know, when we propose this project of why we thought dogs would be useful is that they are ubiquitous, right?
00:20:37.039 --> 00:20:38.160
We find them all over.
00:20:38.319 --> 00:20:45.359
And certainly in the United States, we have we have lots of wonderful dogs, and my dogs included because always use more enrichment.
00:20:45.440 --> 00:20:51.440
And so this is really a fun enrichment opportunity for owners to get their dogs involved in, but also then they're helping their communities.
00:20:51.599 --> 00:20:58.640
And I think the number of folks we saw interested in participating really spoke to how many people want to give back and want to help their communities.
00:20:59.359 --> 00:21:03.920
And I, you know, I I get uh down about the environmental issues you see every day.
00:21:04.000 --> 00:21:08.000
And I think this is one place where you can start to feel a little powerful, right?
00:21:08.079 --> 00:21:12.559
Sometimes it seems overwhelming, and what could I do as a single citizen?
00:21:12.799 --> 00:21:20.000
Um, but then you find out, hey, I could contribute in the small way, and there are all these other people contributing in a small way, and we could really make a difference.
00:21:20.240 --> 00:21:23.279
I think about I'm gonna put a plug in for the pollinators.
00:21:23.440 --> 00:21:31.920
Um, I always think about this with the pollinator gardens too, when you see like all this environmental destruction and you think, how is this gonna how can I do anything against these big companies?
00:21:32.160 --> 00:21:36.480
And then you read things about like, well, if you plant your own little pollinator garden and all your neighbors do this, right?
00:21:36.559 --> 00:21:41.359
It actually ends up being a really powerful sort of refuge for all of our lovely pollinators.
00:21:41.519 --> 00:21:47.920
And I think our our project is similar in that um together we can make big differences.
00:21:48.240 --> 00:21:49.519
People want to do this.
00:21:49.759 --> 00:21:56.480
And uh the other thing we liked about this sort of approach is that not every area is gonna have the same issue, right?
00:21:56.559 --> 00:21:59.680
So spotted lanternfly might be the issue where we are.
00:22:00.400 --> 00:22:03.039
But someone in uh Utah might have a different issue.
00:22:03.119 --> 00:22:19.200
And really what we're what we wanted to do with this was a sort of a proof of concept study showing that average owners and average dogs could detect subtle odors and be, you know, a sort of a flexible ubiquitous detection force.
00:22:19.359 --> 00:22:20.640
And so we're really excited about that.
00:22:20.720 --> 00:22:22.799
We still have teams that are continuing to volunteer.
00:22:22.880 --> 00:22:33.119
We have some teams in Ohio that are still working in their parks to help uh kind of clear them of spotted lantern fly egg masses, which is it is it's really exciting.
00:22:33.200 --> 00:22:36.799
And I think it's uh um really helps the citizens too, right?
00:22:36.880 --> 00:22:38.799
Where you're like, I can give back, I can make a difference.
00:22:39.359 --> 00:22:39.519
Yeah.
00:22:40.240 --> 00:22:47.440
Yeah, and we know obviously we've all done research and attrition um throughout a study can be very difficult to deal with.
00:22:47.519 --> 00:22:52.240
And and obviously everyone here is a volunteer, all your citizens are volunteers.
00:22:52.319 --> 00:22:59.680
So, like, what safeguards did you have to kind of put in place to ensure the the data that they collected is reliable?
00:23:00.480 --> 00:23:02.319
Yeah, that's a that's a really great question.
00:23:02.400 --> 00:23:06.480
And I think that's one of the challenges with this sort of approach for research, right?
00:23:06.559 --> 00:23:09.039
Is that you need to make sure you can trust your data.
00:23:09.200 --> 00:23:13.039
Um, and so we had put together some really detailed protocols.
00:23:13.279 --> 00:23:29.279
Sally went out to a number of our ORTs and our OTO-recognition trials and our field evaluations to make sure all things were running as as desired, but then also put together these really lovely protocols, gave a lot of coaching to folks where we weren't going to travel to them.
00:23:29.519 --> 00:23:33.519
And if they didn't follow their protocol, they did have to submit videos, their data sheets.
00:23:33.599 --> 00:23:36.160
And if they didn't follow our protocol, their data was not included.
00:23:36.400 --> 00:23:43.680
So we made sure that the data were from dogs who had participated in trials that were run the correct way.
00:23:43.759 --> 00:23:47.440
Otherwise, their data are really not comparable, and we really can't say much about it.
00:23:47.599 --> 00:23:49.759
So we had some safeguards there.
00:23:50.400 --> 00:23:54.319
And you're right, also about attrition, these are volunteers, and it can take a lot of time.
00:23:54.960 --> 00:23:59.759
And most of our attrition was due to that, that uh it's just hard to sometimes fit this in.
00:24:00.160 --> 00:24:11.920
So again, this this project might not be for everybody, um, but it seems like it's uh really interesting to a lot of folks that are probably gonna want to participate and folks that can't, you know, devote this much time.
00:24:12.000 --> 00:24:14.000
There's probably some other project they can get involved in.
00:24:15.039 --> 00:24:16.319
Yeah, that's so exciting.
00:24:16.559 --> 00:24:21.519
I'm trying to um convince my husband to turn in our yard into a like a wildflower garden, like you said, Paul.
00:24:21.680 --> 00:24:22.880
Yeah, like why do we have grass?
00:24:22.960 --> 00:24:26.640
I don't understand why everybody has lawns and they mow their lawn, like it's such a waste of my life.
00:24:26.799 --> 00:24:28.000
Yeah, he's not on board.
00:24:28.160 --> 00:24:29.680
I don't know how long it's gonna take for.
00:24:30.160 --> 00:24:30.640
We'll see.
00:24:30.799 --> 00:24:32.319
It would save a lot of effort, right?
00:24:32.400 --> 00:24:34.960
Like then he wouldn't have to do so much yard work, but whatever.
00:24:35.200 --> 00:24:35.599
Totally.
00:24:35.759 --> 00:24:36.079
Exactly.
00:24:36.240 --> 00:24:38.319
And it's prettier and it's so much prettier.
00:24:38.640 --> 00:24:39.359
So yeah.
00:24:40.240 --> 00:24:41.599
Someday that's my goal.
00:24:41.839 --> 00:24:44.400
So shame him on the podcast.
00:24:44.799 --> 00:24:51.039
So everybody start getting all these hate calls.
00:24:53.200 --> 00:24:54.240
The pollinators.
00:24:54.799 --> 00:24:57.119
Because a veterinarian is a good pollinator.
00:24:59.200 --> 00:25:00.480
Oh my goodness.
00:25:00.720 --> 00:25:02.640
So let's move on to the second study.
00:25:02.720 --> 00:25:17.200
And Sally, you already talked about this a little bit as far as um putting things into a more real-world context and kind of adding more difficulty and exploring how those dogs could participate or learn or um be successful in those new environments.
00:25:17.359 --> 00:25:19.359
So tell us a little bit more about that.
00:25:19.680 --> 00:25:25.440
Yeah, so the range of detection study was sort of uh one half of the second part of the project.
00:25:25.599 --> 00:25:30.079
The other half was kind of putting this into real-world context.
00:25:30.240 --> 00:25:48.640
And the way we did that was almost to pit the uh human visual searches against the dogs and say, okay, you know, we're you're saying that dogs are fine, but we can also visually search this without the hassle of having to have this dog and this other research and all that comes along with that.
00:25:48.880 --> 00:26:10.799
So uh asking people, even our own uh plant pathologist that was on our team to go out and search areas uh to find spotted Latinfly egg masses, uh, other extension personnel that have been trained and go out and do eradication processes uh to investigate half-acre uh sectors of the environment.
00:26:10.880 --> 00:26:16.960
And that varied between some partially managed woodland to sort of wasteland areas.
00:26:17.039 --> 00:26:30.319
Uh, we had one site in Rhode Island that was sort of tucked in behind a solar farm and a yard uh waste sort of dump site for the the city, and go out there and and log and GPS all of the sites.
00:26:30.400 --> 00:26:35.519
And each one of those human sort of searches did that independently, and then we sent the dogs out.
00:26:35.680 --> 00:26:50.640
And the dogs, I yeah, we were able to find the things that were obvious and within our field of vision, but the dogs were saying, no, no, we have we have Oda from you know this sort of more hidden site, this this cryptic location.
00:26:50.799 --> 00:27:03.839
And and some of those places took a long time for us to actually find where that odor was coming from, to include me uh climbing trees on occasion to look down into where like a branch had been broken off.
00:27:04.000 --> 00:27:05.839
So think about the spotted landfly.
00:27:05.920 --> 00:27:12.720
It's trying to uh maintain its offspring, but it doesn't have the ability to sort of protect the eggs once they're laid.
00:27:12.960 --> 00:27:20.000
So while they put that sort of what looks like a mud covering on top of the eggs, they also are laying them in mindful areas.
00:27:20.079 --> 00:27:33.920
They're putting them down into sort of fractured branches of trees, uh down in the grass areas on the underside of uh branches and and in vineyards, the cordons, which are the horizontal members of the vine, uh, that would lay on the underside of that.
00:27:34.079 --> 00:27:44.319
So it takes some sort of sleuthing on the part of the human to get down on your back and look up or climb a tree and look down into a sort of void area to be able to find them.
00:27:44.480 --> 00:27:49.759
And and that's where we found the egg masses that the humans just simply were not finding.
00:27:49.920 --> 00:28:01.680
Um, to to about a two to one ratio, you know, of uh those dogs were clearing far more in far less time than the humans were able to.
00:28:02.079 --> 00:28:02.640
That's great.
00:28:02.799 --> 00:28:03.440
I love that.
00:28:03.599 --> 00:28:09.519
How like I'm just looking at my backyard right now because my trees are very beautiful, but also really tall.
00:28:09.599 --> 00:28:13.519
Like, how how high up do these egg masses get laid?
00:28:13.839 --> 00:28:20.079
Um, well, uh at the tree of heaven is their really like high price tree in all of this.
00:28:20.160 --> 00:28:24.880
And those trees grow, you know, 40, 50 feet tall when left unchecked.
00:28:25.119 --> 00:28:27.839
I never climbed above like 20 feet off the ground.
00:28:30.400 --> 00:28:35.279
I was a paramedic on duty, so you know, if I felt everybody's down.
00:28:35.680 --> 00:28:36.480
Right, exactly.
00:28:36.640 --> 00:28:40.640
So uh yeah, I mean, but we were finding them that high up.
00:28:40.799 --> 00:28:43.279
I don't know that there's uh any solid data.
00:28:43.359 --> 00:28:50.880
I think that if they can access an area and it's a good sheltered spot and they they sort of like the feel of it, they're gonna lay their eggs there.
00:28:51.039 --> 00:28:57.359
Um the other sort of facet of this is uh we are at the big vector with the spotted lanternfly, right?
00:28:57.599 --> 00:29:00.240
We move this thing around the country.
00:29:00.319 --> 00:29:01.279
That's part of the problem.
00:29:01.359 --> 00:29:04.079
They're they're a hitchhiker uh type insect.
00:29:04.319 --> 00:29:09.279
So uh not only on trees that aren't going anywhere, but on bundles of lumber.
00:29:09.440 --> 00:29:17.440
Or in fact, this is how it got to the United States on a shipment of landscape uh stones that laid on the other side of the stones.
00:29:17.680 --> 00:29:27.119
Visual inspection, you know, didn't seem anything awry, but uh, as they sort of unpack that stone, then that exposed those egg masses and they were able to take a hold.
00:29:27.200 --> 00:29:32.960
And that was that one single shipment that has caused this entire sort of issue throughout the United States.
00:29:33.039 --> 00:29:34.480
So we become the vector.
00:29:34.720 --> 00:29:36.480
We transport this material around.
00:29:36.640 --> 00:29:41.599
So sometimes it's not how high, more how deep into something.
00:29:41.839 --> 00:29:48.720
And and a shipping company is not gonna be willing to take every piece of wood out of a bundle of lumber.
00:29:48.799 --> 00:29:50.640
There could be hundreds of examples there.
00:29:50.720 --> 00:30:00.720
But if we can run a dog through and say, yep, this bundle has a target in it, we're going to investigate this smaller bundle, take that apart, find that example, remove it, send it on its way.
00:30:01.279 --> 00:30:06.240
Is that happening now with like detection dogs like at shipping ports?
00:30:07.039 --> 00:30:18.000
So for the spotted landfly, there is uh one or two areas where sort of um USDA or other uh agency dogs are doing that sort of cargo inspection.
00:30:18.160 --> 00:30:21.680
But that model's really been set by our muscle detection dogs.
00:30:21.759 --> 00:30:30.960
Uh, so the zebra mussels uh being transported on um private boats from one lake area to another lake area and populating them.
00:30:31.119 --> 00:30:35.119
And and that's where this sort of model is is really prolific, right?
00:30:35.279 --> 00:30:54.880
The volunteers go out there to a boat launch and the dog runs around the boat and says, Yep, we need to flush the bilge on this boat and and remove all the examples of the zebra mussel so that when you go to the next uh lake and put in there, you're not then distributing this invasive, highly invasive species uh to that next area.
00:30:55.039 --> 00:31:02.559
So we're hoping that maybe we can set up some of those more sort of um inspection type concepts.
00:31:02.799 --> 00:31:08.799
Um right now it's being controlled by these in a very infested area.
00:31:08.960 --> 00:31:16.880
There's a limit on sort of the uh import and export from that area, and that we have to actually say, you know, it's been visually searched.
00:31:16.960 --> 00:31:19.599
But as we've shown, that is not 100%.
00:31:20.400 --> 00:31:22.160
Yeah, not even close.
00:31:22.480 --> 00:31:23.759
Yeah, that's amazing.
00:31:24.079 --> 00:31:32.880
So we've all done research and it is never really perfect exactly the way that you planned it to be on paper.
00:31:33.279 --> 00:31:39.279
What were some of the obstacles or challenges that you encountered on the way in this project?
00:31:39.920 --> 00:31:44.640
I think uh managing the people was uh a lot, right?
00:31:44.799 --> 00:31:47.279
Uh especially that many people.
00:31:47.359 --> 00:31:56.400
And of course, we all know being in the dog world and the animal world, everybody has big opinions about the way things should be and and whether or not something's important.
00:31:56.640 --> 00:31:58.319
And as uh Dr.
00:31:58.480 --> 00:32:08.319
Eriger already mentioned, you know, maybe a citizen scientist or a dog handler is not as interested in the the sort of um robustness of a data set.
00:32:08.400 --> 00:32:13.519
They're more interested in sort of getting getting to the end point or success or the ribbon, right?
00:32:13.599 --> 00:32:15.039
This is the world that they've come from.
00:32:15.119 --> 00:32:32.319
And so trying to bridge that gap and really uh translate what we're trying to achieve from a research study perspective into what is exciting and obtainable and something that is tangible for our citizen scientists.
00:32:32.480 --> 00:32:34.559
And I think that was one of our biggest challenges.
00:32:34.640 --> 00:32:37.519
And we we overcame that a lot with uh, like Dr.
00:32:37.680 --> 00:32:51.759
Erica was saying, you know, the the protocols that we developed and sort of the those safety rails that we put up, but also uh creating a sense of camaraderie within the uh study group, population group, uh community.
00:32:51.920 --> 00:33:07.759
So a lot of uh closed Facebook group to be able to share sort of experiences and and how to overcome sort of technical challenges that they faced uh was one of the big, big sort of uh uh things that we did to overcome some of those challenges.
00:33:09.039 --> 00:33:11.599
Yeah, I bet they uh really enjoyed all that.
00:33:11.759 --> 00:33:14.079
I could see like because of the dog world, right?
00:33:14.319 --> 00:33:17.920
The dog sport world, everyone goes for the social stuff too, right?
00:33:18.079 --> 00:33:22.079
So like the fact that you have all these uh folks um together.
00:33:22.160 --> 00:33:23.039
I think that's great.
00:33:24.160 --> 00:33:26.559
So I want to know can any dog do this?
00:33:26.640 --> 00:33:27.839
I have a kind of corso.
00:33:28.240 --> 00:33:30.000
Um, what if you have a bulldog or a pug?
00:33:30.079 --> 00:33:32.079
What if you have a dog that doesn't really have a great nose?
00:33:32.240 --> 00:33:33.759
Like, are there special breeds?
00:33:33.920 --> 00:33:38.160
We always think about certain breeds for search and rescue and drug detection and sniffer dogs.
00:33:38.400 --> 00:33:40.079
Who's who's the best dog?
00:33:40.480 --> 00:33:42.640
Yeah, all dogs are the best dog.
00:33:42.880 --> 00:33:46.160
They're absolutely every single one of them is the best dog ever.
00:33:46.480 --> 00:33:50.880
Uh, but our research actually followed up on what we know from other researchers.
00:33:51.039 --> 00:33:51.519
So, Dr.
00:33:51.599 --> 00:33:54.480
Nathan Hall down at Texas Tech, and then more recently, Dr.
00:33:54.640 --> 00:34:00.720
Salomon from Budapest found that there are very little differences between breeds.
00:34:00.799 --> 00:34:09.519
And in fact, the most recent study showed there was more variation um within breed than between breeds uh when set on a detection task.
00:34:09.760 --> 00:34:12.239
So, fun story from our project.
00:34:12.400 --> 00:34:18.960
One of our star sniffers was a little Boston terrier who was searching in a vineyard.
00:34:19.280 --> 00:34:22.239
So classic little brachiocephalic dog, right?
00:34:22.320 --> 00:34:23.679
Not your typical detection.
00:34:24.159 --> 00:34:25.039
Can barely breathe.
00:34:25.360 --> 00:34:26.719
Can barely breathe, right?
00:34:26.880 --> 00:34:28.320
And we have other considerations.
00:34:28.559 --> 00:34:29.599
Are they heat tolerant?
00:34:29.679 --> 00:34:36.639
All of that stuff, exercise tolerant, but but we're searching in a vineyard that was supposedly clear of spotted Lentenfly.
00:34:36.800 --> 00:34:44.000
There was no samples that we had placed in that area, and this dog kept saying, no, no, no, mom, it's this plant right here.
00:34:44.079 --> 00:34:45.519
It's this plant, it's this plant.
00:34:45.760 --> 00:34:49.519
And I assumed I hadn't seen the dog work very much before.
00:34:49.599 --> 00:34:54.559
And I assumed, well, maybe it's just a little confused, or the hen was prompting it, or something else is going on.
00:34:54.719 --> 00:34:56.079
Um it was a very hot day.
00:34:56.239 --> 00:34:59.920
And and I looked, I visually looked, and I couldn't see anything.
00:35:00.079 --> 00:35:04.400
And finally the hen was like, like, I really feel like my dog is not wrong.
00:35:04.559 --> 00:35:07.440
I'm really, I'm like, okay, well, I'll get down on the ground.
00:35:07.599 --> 00:35:15.199
So here I am laying on my back, looking up at the sky through the uh the vines of the vineyard.
00:35:15.360 --> 00:35:21.599
And sure enough, there on the underside of the cordon was a naturally laid spotted lanfly egg mass.
00:35:21.679 --> 00:35:25.039
And when I looked to my left and right, there were other examples.
00:35:25.199 --> 00:35:32.000
And so that that had passed the muster of the vineyard owners and workers who had said there was no spotted lanternfly.
00:35:32.079 --> 00:35:33.599
We had inspected it visually.
00:35:33.760 --> 00:35:39.360
Other dogs had missed that particular example, and this dog said, nope, it's right there.
00:35:39.679 --> 00:35:45.920
So the most atypical dog is there, sort of solving the problem for the day.
00:35:46.159 --> 00:35:48.000
That's that is amazing.
00:35:48.159 --> 00:36:12.800
And yeah, like you said, you think about certain breeds, and that's because you know, our longer snooted dogs have a larger kind of square footage of uh epiepithelial, olfactory epithelium, and our little dogs have a tiny little real estate in there, and so you would think that they they're using so many more receptors when you have a larger head and snout that you can smell better, but it this is a great story to say.
00:36:12.880 --> 00:36:14.800
It doesn't matter, you can still smell.
00:36:15.360 --> 00:36:18.239
He's like, I can barely breathe, but I can do it.
00:36:18.559 --> 00:36:24.719
Yeah, all of my breathing goes to smelling or oxygenating my body.
00:36:25.039 --> 00:36:26.400
I don't care about that.
00:36:26.559 --> 00:36:27.920
I got those lantern flags.
00:36:28.000 --> 00:36:29.360
Oh my gosh, that's fantastic.
00:36:29.519 --> 00:36:29.840
I love it.
00:36:30.239 --> 00:36:31.840
Fabulous, love it, love it, love it.
00:36:32.000 --> 00:36:32.960
All right, excellent.
00:36:33.119 --> 00:36:35.360
Well, I'm gonna ask each of you separately.
00:36:35.440 --> 00:36:41.039
So, Erica, um, what is one thing that you want the hive mind to take away from today's episode?
00:36:42.079 --> 00:36:47.119
Um, I'm I just love the project because it highlights what your dog can do.
00:36:47.360 --> 00:37:02.079
And uh, you know, I think we see our dog lounging on the couch and they're adorable and they're fun, but I think reminding ourselves of the of the amazing things they can tell us if we train them and that they are experiencing this whole other world that we don't appreciate.
00:37:02.239 --> 00:37:06.000
So uh when your dog wants to sniff on a walk, maybe let him stop and sniff, right?
00:37:06.079 --> 00:37:10.559
It's like letting somebody look at my phone one more time to read the story I'm interested in.
00:37:10.719 --> 00:37:20.000
So, you know, I I think that's what's really exciting is seeing what our average doggos can do and and help help us with our community issues.
00:37:20.639 --> 00:37:21.280
That's cool.
00:37:21.519 --> 00:37:22.320
And Sally?
00:37:23.599 --> 00:37:26.079
I don't know that I could say much beyond that.
00:37:26.159 --> 00:37:39.360
It it's uh get out there and let them sniff, let them experience the world the way they have evolved to to experience it and appreciate how much that sort of rallies their spirit in the moment, right?
00:37:39.440 --> 00:37:44.960
That they they truly do get a lot of enrichment or fulfillment out of that.
00:37:45.119 --> 00:37:50.559
And if we can put that to a sort of a combined purpose, even better.
00:37:51.039 --> 00:37:51.840
That's great.
00:37:52.480 --> 00:37:58.000
And can I make make a plug that we have um a new study coming up that we're recruiting for?
00:37:58.239 --> 00:38:01.280
Um we're looking for dogs in in Virginia.
00:38:01.519 --> 00:38:05.119
Uh our prior study used dogs that had some scent work background.
00:38:05.519 --> 00:38:18.400
It was a proof of concept study just to show that again, these non-purpose bred, non-professional dogs can be useful detection tools for really subtle targets like agricultural targets.
00:38:18.559 --> 00:38:24.480
Um, we're expanding that now to naive dogs, so dogs that don't have any prior uh nosework.
00:38:24.719 --> 00:38:25.440
Yes, that's right.
00:38:25.760 --> 00:38:30.880
So we're looking for dogs uh down in the New River Valley, Roanoke Valley area, and up in northern Virginia.
00:38:31.199 --> 00:38:36.400
So you can always get in touch with us and we can put you on our list if that project doesn't work for you.
00:38:36.480 --> 00:38:41.039
We'll hopefully have projects in the future that might um your dog might be a candidate for.
00:38:41.360 --> 00:38:42.000
Super cool.
00:38:42.159 --> 00:38:42.559
I love it.
00:38:42.800 --> 00:38:44.239
Amy and I are gonna enroll our dogs.
00:38:44.960 --> 00:38:45.360
Perfect.
00:38:45.519 --> 00:38:47.760
I know my dog wants to do it so badly.
00:38:48.239 --> 00:38:51.360
Jenny, the hairs on the on the problem.
00:38:51.920 --> 00:38:52.559
Perfect.
00:38:52.880 --> 00:38:53.599
I love it.
00:38:53.840 --> 00:38:54.639
Oh my goodness.
00:38:54.800 --> 00:38:59.280
Well, I want to move into our next section, which is buzz into your beeswax.
00:38:59.679 --> 00:39:01.599
We want to know more about our guests.
00:39:01.760 --> 00:39:13.199
So um, this is where we pull back that curtain and discuss something off topic about your lives, a special story or a funny anecdote so that we know who you are, aside from just the research that you publish.
00:39:14.000 --> 00:39:20.480
Uh I'll tell you our um my partner Paul and I, our current fascination is our local possum.
00:39:20.639 --> 00:39:27.840
Um, so one we have chickens, and one of our chickens hops out of their coop each day to lay her egg and it's right along our house.
00:39:27.920 --> 00:39:32.559
Um, and we had noticed she'd been doing that, but then also noticed the eggs had been cracked and eaten.
00:39:32.719 --> 00:39:36.480
And it turns out uh we have a lovely possum that comes by.
00:39:36.639 --> 00:39:42.880
And so that's our donation to our our um neighborhood possum is leaving that chicken's egg out there.
00:39:42.960 --> 00:39:48.000
And then every once in a while we're lucky enough to open the door at the right time and see our cute little possum cruising by.
00:39:48.079 --> 00:39:50.480
So that's amazing.
00:39:50.800 --> 00:39:53.360
It's not Jimothy, but it is cool.
00:39:54.000 --> 00:39:58.480
I know we do we do need to name it so that it rises to the level of Jimothy.
00:39:58.719 --> 00:40:02.000
Yeah, we have some we have some toads around here too that we've named as well.
00:40:02.400 --> 00:40:03.039
Oh, cool.
00:40:03.280 --> 00:40:04.159
I love it.
00:40:04.559 --> 00:40:05.119
Love that.
00:40:06.800 --> 00:40:09.679
And Sally, do you want to tell us some stories about your life?
00:40:09.920 --> 00:40:13.119
I sense a theme here that we're all animal people.
00:40:13.199 --> 00:40:18.159
And my my ongoing fascination for a very long time has been with Corvette.
00:40:18.239 --> 00:40:31.039
So I have uh, you know, feeders and I offer trinkets to my feathered friends, and I uh love to just watch them investigate that space and think about it.
00:40:31.119 --> 00:40:35.519
And every once in a while I do get a little treasure left uh for me.
00:40:35.679 --> 00:40:40.880
So that makes my day when when a crow brings me something awesome.
00:40:41.119 --> 00:40:43.760
So I love that so much.
00:40:43.920 --> 00:40:46.239
My daughter tried to do that for one week.
00:40:46.559 --> 00:40:49.679
I was like, well, so yeah.
00:40:49.920 --> 00:40:56.480
So she's like out and she like put a mirror outside to like try to get their attention, like on the roof outside of her bedroom.
00:40:56.639 --> 00:41:00.000
And then she put some food up there, and I was like, How do you know the squirrels aren't gonna eat that?
00:41:00.159 --> 00:41:00.960
Like, are you watching?
00:41:01.119 --> 00:41:01.840
Like, what's happening?
00:41:02.000 --> 00:41:05.440
And she's like, Oh no, I know it's the uh I've never seen a squirrel up there.
00:41:05.519 --> 00:41:07.119
I am I'm sure it's gonna be the crows.
00:41:07.199 --> 00:41:11.519
And I was like, just the crows, no other bird, just the crows.
00:41:11.920 --> 00:41:14.880
And so so I was like, How's it going with your buddies the crow?
00:41:15.039 --> 00:41:16.320
She goes, I'm a crow.
00:41:19.599 --> 00:41:21.360
I've always wanted to do that, right?
00:41:21.440 --> 00:41:35.679
The crows are so amazing, and I love that they bring you trinkets, and I love that um they uh pass on that memory to generations, people that they like and or people that they don't like, and then everybody knows who Kenzie's on that list.
00:41:36.880 --> 00:41:40.719
Yeah, she's like that girl stopped giving us food.
00:41:41.920 --> 00:42:00.079
I love the book Um Gifts of the Crow, and it talks about that, the research they did out in Seattle and uh how they are now having to wear disguises so that they are not subject to uh Crow's distent while walking through town on like a coffee break or whatever.
00:42:00.239 --> 00:42:08.079
So, but uh my my goal coming from a search and rescue background, I my goal is to someday have a search and rescue COVID.
00:42:08.320 --> 00:42:10.159
So we'll see if I have to.
00:42:10.320 --> 00:42:12.239
That would be a good idea.
00:42:12.400 --> 00:42:13.039
Fantastic.
00:42:13.760 --> 00:42:15.199
Oh my gosh, I love that.
00:42:17.760 --> 00:42:19.039
That'll be a future interview.
00:42:19.360 --> 00:42:19.920
I love it.
00:42:20.239 --> 00:42:23.360
Well, here at the Behavior Buzz, we know you are all busy bees.
00:42:23.679 --> 00:42:24.559
Get busy bees!
00:42:24.960 --> 00:42:26.960
Who live and die by the science like we do.
00:42:27.039 --> 00:42:34.559
So we'll put all of the references from today's show on the website, including those two papers written by the lovely doctors.
00:42:34.719 --> 00:42:38.639
So buzz on over to the Honeypot page to find those.
00:42:39.039 --> 00:42:41.679
And we just want to say a big thank you to Dr.
00:42:41.840 --> 00:42:42.639
Erica and Dr.
00:42:42.800 --> 00:42:44.079
Sally for joining us today.
00:42:44.239 --> 00:42:45.599
This was really a fun discussion.
00:42:45.760 --> 00:42:49.119
I love kind of these down and dirty, like, what can we do?
00:42:49.280 --> 00:42:50.559
How can we help the world?
00:42:50.719 --> 00:42:51.679
kind of discussions.
00:42:51.920 --> 00:42:56.800
And that also lends itself to how smart are the animals that we work with, right?
00:42:56.960 --> 00:43:01.599
Because we um don't always appreciate everything that they do for us.
00:43:01.760 --> 00:43:10.239
And so thank you for this discussion about research, about invasive species and citizen science and how we all can help.
00:43:13.519 --> 00:43:18.320
Thank you to our premium sponsor, PRN PharmaCal, the makers of Reconcile.
00:43:18.559 --> 00:43:25.440
Reconcile is an FDA-approved drug for the treatment of canine separation anxiety in conjunction with the behavior modification plan.
00:43:25.920 --> 00:43:36.000
PRN PharmaCal is committed to meeting the evolving needs of modern veterinary medicine and dedicated to developing products to strengthen the bond between pets and their people.
00:43:37.760 --> 00:43:44.400
And HiveMind, we thank you especially because without listeners like you, we would have nothing to buzz on about.
00:43:44.639 --> 00:43:48.800
So grab a drink and join us next time for cocktails and conversations.
00:43:49.280 --> 00:43:51.519
Follow us on all our socials.
00:43:51.760 --> 00:44:00.320
Facebook at Behavior Buzz, Instagram at Behavior Buzz, and our website, BehaviorBuzz.com.
00:44:00.719 --> 00:44:03.679
Be positive, be informed.
00:44:03.920 --> 00:44:06.480
Now Buzz Awesome.
00:44:08.400 --> 00:44:10.400
Okay, if you don't like it, that's fine.
00:44:10.719 --> 00:44:11.280
All right.
00:44:13.840 --> 00:44:16.079
Oh, sorry, Sally, this is you, wasn't it?
00:44:17.119 --> 00:44:18.400
Now you can cut it out.
00:44:19.119 --> 00:44:21.519
I need to I need to pull up my uh questions.
00:44:30.320 --> 00:44:31.119
We're back.
00:44:31.519 --> 00:44:32.400
We're back.
00:44:33.280 --> 00:44:35.360
I'm sure that'll be in the podcast now.
00:00:01.679 --> 00:00:03.359
And we're live.
00:00:04.000 --> 00:00:06.000
I'm sure that'll be in the podcast now.
00:00:06.559 --> 00:00:07.040
Definitely.
00:00:07.919 --> 00:00:09.759
Welcome, Hive Mind.
00:00:09.919 --> 00:00:12.800
We are buzzing with excitement about today's guest.
00:00:13.039 --> 00:00:14.960
We loved her so much the first time around.
00:00:15.039 --> 00:00:16.960
We had to have a repeat guest in Dr.
00:00:17.039 --> 00:00:18.399
Erica Feuerbacher.
00:00:18.640 --> 00:00:24.640
And joining her today is her colleague, KPA dog trainer and PhD applied animal behaviorist, Dr.
00:00:24.719 --> 00:00:25.920
Sally Dickinson.
00:00:26.160 --> 00:00:33.679
These wonderful doctors are joining us to talk about conservation detection dogs and using citizen science in their research.
00:00:33.920 --> 00:00:45.439
This is the Behavior Buzz, a veterinary behavior podcast that aims to bring cutting-edge scientific information and education to pet parents, behavior professionals, and the veterinary community.
00:00:45.600 --> 00:00:46.799
I'm your co-host, Dr.
00:00:46.880 --> 00:00:49.920
Amy Pike, a board-certified veterinary behaviorist.
00:00:50.240 --> 00:00:51.840
And I'm your other co-host, Dr.
00:00:51.920 --> 00:00:54.960
Amy Lern, also a board-certified veterinary behaviorist.
00:00:55.200 --> 00:00:57.359
Hello, Doctors, Erica and Dr.
00:00:57.520 --> 00:00:57.840
Sally.
00:00:58.079 --> 00:01:02.479
We are thrilled to have you join us for a morning of fun and ridiculosity.
00:01:03.039 --> 00:01:06.159
So we just watched, well no, it's not.
00:01:06.480 --> 00:01:09.680
But we last night, my family and I just watched Wicked for Good.
00:01:09.840 --> 00:01:13.519
And that it reminded me of like the first Wicked, right?
00:01:13.599 --> 00:01:15.120
Like they make all these crazy words up.
00:01:15.200 --> 00:01:18.560
And I was like, wouldn't it be just fun if we made up some words?
00:01:18.879 --> 00:01:19.359
I love it.
00:01:19.519 --> 00:01:19.840
I love it.
00:01:20.000 --> 00:01:21.920
So make up a Z word.
00:01:22.400 --> 00:01:22.879
I know.
00:01:23.519 --> 00:01:24.719
I couldn't find any.
00:01:26.159 --> 00:01:27.359
Oh my goodness.
00:01:27.519 --> 00:01:32.159
Um, so we are coming to you live from the PRN PharmaCow Studios.
00:01:32.319 --> 00:01:35.040
You guys know what PRN stands for, right?
00:01:35.680 --> 00:01:39.359
My stomach is growling, so I'm gonna say probably requires nachos.
00:01:39.840 --> 00:01:40.560
Nachos.
00:01:40.879 --> 00:01:43.280
Nachos that go great with the drink I'm drinking.
00:01:43.519 --> 00:01:44.400
Yeah, exactly.
00:01:45.280 --> 00:01:46.799
Does not mean nachos.
00:01:47.120 --> 00:01:48.640
I wish that it meant nachos.
00:01:49.120 --> 00:02:07.920
Um, it does mean, however, pro reinata, a Latin phrase meaning as the circumstance arises, and do you know how long the veterinary community has been trusting PRN Pharmacal to be here when needed with industry-leading research and innovative products to improve animal health and quality of life?
00:02:08.319 --> 00:02:23.039
Yes, since 1978, which is also back when the USDA was ramping up biological control and invasive pest monitoring to tackle the increasing problem of invasive agricultural pests, which is kind of what we're talking about today.
00:02:23.759 --> 00:02:24.240
How are you?
00:02:25.199 --> 00:02:26.639
You are a smarty baby.
00:02:26.800 --> 00:02:30.159
I am super smart with my AI search.
00:02:31.680 --> 00:02:32.960
The computer did it for you.
00:02:33.360 --> 00:02:34.639
The computer did it for me.
00:02:34.879 --> 00:02:35.120
Yep.
00:02:35.280 --> 00:02:43.120
Uh PRN PharmaCal is a proud sponsor of the Behavior Buzz podcast and is committed to the physical and emotional health of animals everywhere.
00:02:43.280 --> 00:02:48.319
Which makes sense why their slogan is PRN PharmaCal, here when you need us.
00:02:48.639 --> 00:02:54.719
Alright, well, let's get started here because I need to know what's getting us all buzzed today.
00:02:55.039 --> 00:03:06.960
I went on Bug Theme and I'm drinking a June bug cocktail, which is a sweet tropical drink made with Midori, coconut rum, banana liqueur, pineapple juice, and citrus juice.
00:03:07.199 --> 00:03:09.120
Oh, that sounds very tropical.
00:03:09.599 --> 00:03:10.080
Summery.
00:03:10.319 --> 00:03:10.719
I love it.
00:03:11.199 --> 00:03:13.120
I also went with a bug theme today.
00:03:13.199 --> 00:03:18.560
I'm drinking a butterfly martini, which has butterfly pea flower gin.
00:03:18.639 --> 00:03:24.560
Uh specifically, I'm using Empress 1908, Elderflower liqueur, and lemon juice.
00:03:24.879 --> 00:03:29.439
The cool thing about this drink is that it changes from blue to purple when you add the lemon juice.
00:03:29.520 --> 00:03:30.560
So it's super fun.
00:03:30.800 --> 00:03:31.439
That is fun.
00:03:31.599 --> 00:03:34.400
I love pea powder butterfly pea powder, pea powder.
00:03:35.280 --> 00:03:35.599
Whatever.
00:03:35.840 --> 00:03:36.000
Whatever.
00:03:36.719 --> 00:03:37.919
That's the stuff that changes.
00:03:38.000 --> 00:03:38.560
Yeah, yeah, yeah.
00:03:39.360 --> 00:03:41.439
What about you guys, Dr.
00:03:41.599 --> 00:03:42.560
Erica and Dr.
00:03:42.639 --> 00:03:43.039
Sally?
00:03:43.120 --> 00:03:44.879
What's getting y'all buzzed today?
00:03:45.199 --> 00:03:49.280
I picked a bug-themed one as well and have the bee's knees, which is also a gin drink.
00:03:49.840 --> 00:03:50.639
And I'm with you.
00:03:50.719 --> 00:03:54.400
I love the uh the butterfly pea in the gin as well.
00:03:54.560 --> 00:03:55.039
That's awesome.
00:03:55.360 --> 00:03:55.840
Yeah, yeah.
00:03:56.080 --> 00:03:56.960
Changing colors.
00:03:57.280 --> 00:03:58.800
Sally, what about you?
00:03:59.280 --> 00:04:10.960
Sticking with the gin, I am doing a traditional uh British drink, just a simple gin and tonic, but always remembering those floral notes from the gin that come from our bugs.
00:04:11.120 --> 00:04:14.319
So it's in there, just slightly more reserved.
00:04:15.439 --> 00:04:19.759
Didn't our last British guest also have the guest also had a gin and tonic?
00:04:20.079 --> 00:04:20.720
So fun.
00:04:20.800 --> 00:04:21.439
I love it.
00:04:21.600 --> 00:04:22.319
I love it.
00:04:22.639 --> 00:04:24.319
Sounds like it's on brand.
00:04:24.560 --> 00:04:26.079
I do love a good G and T.
00:04:26.639 --> 00:04:27.120
Me too.
00:04:27.279 --> 00:04:28.800
That is my go-to drink, really.
00:04:29.680 --> 00:04:36.399
Well, remember, you can find all of our signature cocktail and mocktail recipes on our website, behaviorbuzz.com.
00:04:36.560 --> 00:04:42.399
That's Behavior Buzz with six Zs because we are seriously excited.
00:04:44.160 --> 00:04:44.480
Seriously.
00:04:44.720 --> 00:04:47.120
I was trying to think of something to go with.
00:04:48.000 --> 00:04:49.680
So much for your made-up work.
00:04:50.000 --> 00:04:50.560
Seriously.
00:04:51.360 --> 00:04:52.160
I love it.
00:04:54.800 --> 00:04:56.240
So let's dive in.
00:04:56.319 --> 00:04:58.319
Let's get this whole show in the road.
00:04:58.560 --> 00:05:10.319
The research we are talking about today is about teaching dogs to detect spotted lanternflies, which we did talk about in a previous episode with Melissa McHugh McGrath, who was one of the participants in this research.
00:05:10.480 --> 00:05:14.959
So, how did you both, as the researchers, come up with this topic to study?
00:05:16.000 --> 00:05:18.240
So, you know, we have a colleague, Dr.
00:05:18.319 --> 00:05:21.680
Nathan Hall at Texas Tech, who does a lot of olfactory work.
00:05:21.839 --> 00:05:25.600
Um, and we are in an ag department here at Virginia Tech.
00:05:25.839 --> 00:05:34.959
Uh so trying to meld our uh foci of dog behavior and agricultural outcomes seemed really useful.
00:05:35.199 --> 00:05:38.639
And when I was in Montana, I'd go hiking with my dog every day.
00:05:38.879 --> 00:05:44.959
And we had some partnerships with uh working dogs for conservation out there with our dog training program.
00:05:45.360 --> 00:05:48.000
And I always just thought their work was fascinating and amazing.
00:05:48.079 --> 00:05:55.360
And every time I was hiking with my dog, I kept thinking, wouldn't it be fun if I were out here hiking and we were doing something useful for the community?
00:05:55.680 --> 00:06:02.000
And that really brought up the idea of this community-based uh sort of solution to environmental and agricultural issues.
00:06:02.160 --> 00:06:08.160
So now when we're out here at Virginia Tech, uh we had an opportunity to partner with Texas Tech on this program.
00:06:08.560 --> 00:06:13.199
Uh, and we brought in one of our sort of plant pathology specialists, Dr.
00:06:13.279 --> 00:06:17.360
Mizuho Nita, who kind of helped guide us on what would be some useful targets.
00:06:17.439 --> 00:06:20.319
And we landed on the spotted lantern fly for a number of reasons.
00:06:20.480 --> 00:06:33.120
One, that they are uh increasingly problematic in our area and spreading west, um, that they have cryptic uh egg masses, so they are hard to detect, and that's something where the dogs might outperform humans.
00:06:33.519 --> 00:06:41.360
Um, so it was kind of a uh from an initial idea of we'd love to do something community science-based, and then let's find a find a target.
00:06:41.600 --> 00:06:42.240
That's awesome.
00:06:42.480 --> 00:06:43.040
I love that.
00:06:43.199 --> 00:06:47.120
Yeah, are there um similar studies for like other invasive species out there?
00:06:48.160 --> 00:07:09.759
Yeah, there are a bunch of other aspects that uh conservation detection dogs use, everything from a endangered species, like the studies out of the University of Washington and conservation uh detection dogs out there looking for killer whale scat so that they can analyze sort of population densities, pregnancy, diet, those kind of questions.
00:07:10.000 --> 00:07:19.040
To invasive species, we can use dogs to detect different plants like the yellow uh star thistle out in the Midwest, which is choking out other native species.
00:07:19.279 --> 00:07:27.920
So, you know, uh allowing a dog to sort of detect those areas so they can be eradicated without using sort of broad spectrum herbicides.
00:07:28.160 --> 00:07:34.879
And then sometimes we just want to look at uh um what is in else is going on in the environment.
00:07:35.040 --> 00:07:50.720
So uh, for example, the Hudson mink study, which looked at sort of the mink as the sentinel uh mammal in the environment, uh, meaning that it was uh, you know, living in the water, drinking the water, eating the fish, doing all of the things.
00:07:50.800 --> 00:08:01.759
So it's sort of at the top of the food chain, what may it be accumulating in its body and looking at its scat to analyze like heavy metals in the environment?
00:08:01.920 --> 00:08:19.680
So sort of a uh a secondary byproduct and being able to find either the actual evidence of the species, their maybe their egg sites or their nest sites or the evidence that they were there for a variety of different reasons.
00:08:20.639 --> 00:08:21.519
That's amazing.
00:08:21.759 --> 00:08:27.360
I wish that they could detect poison ivy and find all the poison ivy around my house, and then we could eradicate that.
00:08:27.519 --> 00:08:29.199
That sounds like a good project.
00:08:29.680 --> 00:08:30.399
I know some of them.
00:08:30.560 --> 00:08:31.759
I could just go out there.
00:08:34.000 --> 00:08:35.039
Like, yeah, I'll get it.
00:08:35.360 --> 00:08:36.000
Oh, it's here.
00:08:36.159 --> 00:08:36.799
I know where it is.
00:08:36.960 --> 00:08:37.440
I got some.
00:08:37.519 --> 00:08:38.559
It's over here somewhere.
00:08:38.720 --> 00:08:39.200
Yeah.
00:08:39.440 --> 00:08:47.200
Um, I also it it's fascinating that you're saying that they're finding the scat and detecting this scat so that you can kind of track some of those other parameters.
00:08:47.440 --> 00:09:02.960
But if you have multiple species that have similar diets, is it just a matter of, I mean, like I feel like digestive food is gonna smell similarly, but I guess each individual animal also lends their scent in as they're passing through the digestive tract.
00:09:03.039 --> 00:09:05.039
And is that especially the killer whale?
00:09:05.279 --> 00:09:06.000
That is crazy.
00:09:06.240 --> 00:09:07.919
Like that's so fun.
00:09:08.879 --> 00:09:19.600
For some of the species where they're they're very identical and they're uh experiencing the world in very similar ways and eating very similar things, there may not be a lot of differentiation.
00:09:19.759 --> 00:09:24.960
But it's unusual to find two examples of those living in the same context.
00:09:25.120 --> 00:09:30.720
So sometimes it's oh, you know, we have this species in this context and this species in this context.
00:09:30.879 --> 00:09:39.679
If we put those together, they might be very similar, but separate context, then we can actually differentiate them from any of the other background noise.
00:09:40.080 --> 00:09:41.759
There's the light bulb moment.
00:09:41.840 --> 00:09:44.480
That's why we have scientists on our show.
00:09:45.519 --> 00:09:46.480
Exactly.
00:09:46.960 --> 00:09:47.759
We learned so much.
00:09:48.159 --> 00:09:49.440
Can you I know, right?
00:09:49.759 --> 00:09:50.480
Every moment.
00:09:50.720 --> 00:09:58.000
Um, can you explain really what is meant by citizen science and how that was used in this particular research project?
00:09:58.480 --> 00:10:10.159
Yeah, so the citizen science, sometimes we call it community science, um, is really bringing sort of average citizens, average community members into helping collect data or uh implement an intervention.
00:10:10.240 --> 00:10:17.360
And in this case, we recruited about 180 dog handler teams across the United States that wanted to participate.
00:10:17.519 --> 00:10:26.960
We actually had way more than that in the thousands, which was really encouraging that so many people wanted to do something meaningful with their dog and go out and help their community while playing with their dog.
00:10:27.120 --> 00:10:32.240
So that was really, I think, a buoy to us of how excited people are about projects like this.
00:10:32.480 --> 00:10:39.120
But we brought in those average citizens that uh a lot of them had done some nosework with their dogs, but they were not purpose-bred dogs.
00:10:39.279 --> 00:10:43.600
They were not professional dog uh handlers or trainers necessarily.
00:10:43.759 --> 00:10:45.120
And then we did pair them up.
00:10:45.279 --> 00:10:47.519
We um, and this is a lot of what Dr.
00:10:47.679 --> 00:11:01.840
Sally did was uh put them into uh regional teams that were kind of supported by a local trainer, and that trainer is the person that we would send samples to, and they'd you know, help the folks train through, you know, this new odor.
00:11:02.320 --> 00:11:10.639
But really, we're looking for just average folks that have probably day jobs doing other things and want to participate with their dogs in solving local issues.
00:11:11.279 --> 00:11:12.000
That's so cool.
00:11:12.639 --> 00:11:17.120
So obviously, we know that dogs can smell a lot better than we do.
00:11:17.519 --> 00:11:22.799
So, what odor are the dogs actually learning to identify with the spotted lantern flies?
00:11:23.200 --> 00:11:25.600
So we're training them to detect the egg masses.
00:11:25.840 --> 00:11:27.360
There are a range of targets, right?
00:11:27.440 --> 00:11:39.840
You could do individuals, but the egg masses are uh where we're gonna get more bang for our training buck that uh we can eradicate, eradicate uh 30 to 50 at a time rather than one adult, one adult, one adult.
00:11:40.000 --> 00:11:44.080
So we can hopefully have a bigger impact by going after those egg masses.
00:11:44.240 --> 00:11:52.399
And that's likely where the dogs are gonna be really useful anyway, because they are cryptic and really hard to see, and that's where humans are gonna struggle to identify them.
00:11:52.799 --> 00:11:59.200
Yeah, how and how difficult is it to teach a dog to discriminate like such a specific scent of that egg mass?
00:11:59.600 --> 00:12:01.039
I'll let Sally take this one.
00:12:01.919 --> 00:12:03.919
So it's it's kind of a big question.
00:12:04.000 --> 00:12:05.200
There's a lot to unpack there.
00:12:05.279 --> 00:12:11.759
But when we think about dogs, they've evolved to live their world in an olfactory sort of metric, right?
00:12:11.840 --> 00:12:19.279
We're very visual, we see things, we think in pictures, we can recall in our mind's eye, quote unquote, uh, sort of images.
00:12:19.440 --> 00:12:21.679
And dogs do that through sense.
00:12:21.840 --> 00:12:46.799
So, you know, whether it's from the structures and the sensory sort of organs that they have that, you know, are a lot more prevalent than than we have in humans, or it's their physiology that allows them to smell in stereo and understand uh gradients of odors much more clearly than we can, that we do the equivalent of in vision.
00:12:46.960 --> 00:12:51.679
Uh, they've evolved to understand the world in terms of scent.
00:12:51.759 --> 00:13:08.879
And that's relevant for their evolution, so that they could understand the difference between potentially a threat that was coming to aggress them and their next meal or uh a potential mating partner, that they could understand those just through through the scent.
00:13:09.039 --> 00:13:16.000
And and how to sort of explain that to the dog is part of our job as humans, right?
00:13:16.080 --> 00:13:27.759
They're scenting all of the time and they're they're experiencing this world in this three-dimensional, multifaceted sort of uh array of odas.
00:13:28.080 --> 00:13:37.120
And it's up to us as the humans to sort of appreciate that and then add the emphasis on the places that that we're interested in.
00:13:37.200 --> 00:13:48.159
And because they're so biddable and uh affiliative through our you know uh joint sort of evolution together, we're able to do that.
00:13:48.320 --> 00:13:54.480
But oftentimes it's the humans that adds the confusion in there, the dogs, the dogs are senting things anyway.
00:13:56.080 --> 00:13:58.960
I feel like I heard now.
00:13:59.039 --> 00:14:07.120
I'm ashamed that I don't know this answer because I feel like I should, and I feel like I read it or saw it in some research somewhere about each nostril.
00:14:07.279 --> 00:14:12.320
Like if they sniff towards it one nostril, it's a new scent or something like that.
00:14:12.399 --> 00:14:13.679
And the other nostril, I don't know.
00:14:13.840 --> 00:14:15.759
Do you does that sound familiar to either one of you?
00:14:15.840 --> 00:14:16.799
Do you know what I'm talking about?
00:14:18.240 --> 00:14:18.879
I just make that up.
00:14:19.200 --> 00:14:20.480
She's just making it up.
00:14:21.759 --> 00:14:23.120
You're close, you're really close.
00:14:23.279 --> 00:14:29.600
So they can smell in stereo, so where um they can perceive sort of directionality, right?
00:14:29.679 --> 00:14:38.879
Where we do with our hearing, um even my hearing that's you know been abused by a fire truck for so long, I can still hear in stereo.
00:14:39.120 --> 00:14:41.279
Dogs do the same with their smelling.
00:14:41.360 --> 00:14:49.279
So just standing still or moving through the environment, they can appreciate this has more parts per million on the right side than this on the left side.
00:14:49.440 --> 00:14:52.639
Um, but then other processes are involved.
00:14:52.720 --> 00:15:01.440
Uh when they breathe in, part of that inhale goes into their lungs for biological respiration for all of the things needed for life.
00:15:01.600 --> 00:15:12.399
But part of that is funneled up into the uh the uh sensory areas that can appreciate those different odors that are in the environment.
00:15:12.559 --> 00:15:14.159
All of that is combined with taste.
00:15:14.320 --> 00:15:17.759
So it's basically the all the same sort of functions as taste and smell.
00:15:17.840 --> 00:15:19.519
It's all combined into one.
00:15:19.759 --> 00:15:27.120
And then that can be exhaled at a different rate than their breathing for respirations for biological processes.
00:15:27.440 --> 00:15:32.320
And then you add in uh just even the physiology of the slits on the side of their nose.
00:15:32.399 --> 00:15:45.840
When they exhale, the exhalation part goes out to the side where they can bring in sort of new fresh samples so they get that clean appreciation instead of repeat analysis of the same air that they've just exhaled.
00:15:45.919 --> 00:15:56.720
So so, really, when we think about it, they have evolved and are are just physiologically set up to be experts at this, and and we're simply tapping into that.
00:15:57.600 --> 00:15:58.879
That's amazing.
00:15:59.120 --> 00:16:02.720
So tell us a little bit more about what you found out from your first study.
00:16:02.879 --> 00:16:04.080
Did they do a good job?
00:16:04.320 --> 00:16:05.919
Can any dog do this?
00:16:07.360 --> 00:16:09.200
They did an amazing job.
00:16:09.440 --> 00:16:21.919
And coming from a professional detection dog background, um they they outperformed any sort of uh uh preconceived notions that I at least I had going into it.
00:16:22.159 --> 00:16:30.159
I know dogs can smell things, I knew they were gonna be good, but we always have this idea that you know certain dogs can do this work.
00:16:30.399 --> 00:16:39.840
Uh going into what we call the odor recognition test, which is sort of a standardized 10 trials, six boxes that all look identical.
00:16:39.919 --> 00:16:55.360
You have a minute and a half to examine the boxes by scent alone, not picking up and looking at them, but sent alone, a minute and a half to make a decision of which one has the target in, or to say that there is no target in that array, time after time after time again.
00:16:55.440 --> 00:16:57.279
So a pretty high pressure test.
00:16:57.440 --> 00:17:01.360
Um, they were able to do that at 82% sensitivity.
00:17:01.440 --> 00:17:07.519
And that means if the target was there, 82% of the time they were saying, yep, it's this one, we're getting it right.
00:17:07.759 --> 00:17:16.880
Their sensitivity was a little bit less, which looks at if the target's not there, how well can the dog say, nope, it's all clear, which is understandable.
00:17:16.960 --> 00:17:20.960
You sort of get this inverse relationship between sensitivity and specificity.
00:17:21.039 --> 00:17:30.319
And for an invasive species, we care more about um finding all of the targets that are potentially there versus saying, nope, there's no targets, right?
00:17:30.400 --> 00:17:33.839
If we if we have a little hiccup on the no target one, so be it.
00:17:33.920 --> 00:17:36.799
Um, but we really want to find those individual targets.
00:17:36.960 --> 00:17:38.880
So that that was amazing metrics.
00:17:39.039 --> 00:17:56.480
And then uh as they moved out into the field trials, their sensitivity dropped a little bit, but they're looking for half a gram of material concealed in a variety, uh, an absolute technical or array of of odors that are out there.
00:17:56.559 --> 00:18:06.480
And some of that ability, I think, is is the handless understanding how to work the dog in that environment and and sort of move through that space effectively.
00:18:07.359 --> 00:18:08.640
And what is the distance?
00:18:08.720 --> 00:18:18.319
Like, I know the dogs can smell for miles, but what is the average, like either when you're working with the boxes or if you're out in nature, you know, do they have to be right on top of it?
00:18:18.559 --> 00:18:20.799
Is there an air scent associated?
00:18:20.960 --> 00:18:26.000
Like, how far away to they kind of really collimate in on what they're looking for?
00:18:26.480 --> 00:18:31.519
If we had the answer to that question, we would be ruling all detection things.
00:18:31.680 --> 00:18:34.799
That's the million-dollar question of all detection work.
00:18:35.039 --> 00:18:35.519
Yeah.
00:18:35.680 --> 00:18:42.400
Um, and it actually we touched it a little bit in our second part of our study, which was our range of detection data.
00:18:42.559 --> 00:18:47.039
And what we found there was it was more sort of asking that exact question.
00:18:47.279 --> 00:18:52.720
How closely uh will they miss a target and how far away will they be able to detect a target?
00:18:52.799 --> 00:19:02.240
And what we found there was between zero and five meters, so up to about 15 feet, um, they were able to detect the targets that we placed in those sort of environments.
00:19:02.400 --> 00:19:13.200
So that took out that handler influence of how I'm going to present the search area to my dog and just asked if my dog is following the straight line, um, you know, how close, how far.
00:19:13.440 --> 00:19:30.160
Um, so again, that's half a gram of a very subtle odor that blends very closely with background odors of grass and bark and other insects that are in the space, and they were still able to detect it at that like 15 feet away range.
00:19:30.319 --> 00:19:32.559
So, again, pretty incredible.
00:19:33.359 --> 00:19:33.839
I believe it.
00:19:34.000 --> 00:19:41.119
My dog is um scenting all my backyard and looking for the roots of one specific tree, and then he'll dig a hole and find the root.
00:19:41.200 --> 00:19:49.039
And I'm like, yeah, now I've holes all over where all the roots of this one tree are, and we're gonna like create a pathway to the tree.
00:19:49.680 --> 00:19:51.920
Oh, he's a he's a monster.
00:19:53.920 --> 00:20:00.880
One of the one of the things that I just absolutely love about this project is it does empower kind of your everyday dog owner, right?
00:20:00.960 --> 00:20:06.160
Like you had trainers involved, obviously, as you're like your kind of cohort lead or whatever.
00:20:06.319 --> 00:20:09.680
But so why is involving the public so valuable?
00:20:09.839 --> 00:20:15.279
And and how does citizen science actually expand what researchers are able to accomplish?
00:20:16.240 --> 00:20:27.279
Yeah, that's part of what I think we're most excited about is not just showing that dogs can detect that, but actually providing, you know, a mechanism where a lot of folks could go out and kind of help solve this problem.
00:20:27.359 --> 00:20:31.119
And that's where bringing in the community members is really powerful.
00:20:31.279 --> 00:20:36.960
Um, one of the things, you know, when we propose this project of why we thought dogs would be useful is that they are ubiquitous, right?
00:20:37.039 --> 00:20:38.160
We find them all over.
00:20:38.319 --> 00:20:45.359
And certainly in the United States, we have we have lots of wonderful dogs, and my dogs included because always use more enrichment.
00:20:45.440 --> 00:20:51.440
And so this is really a fun enrichment opportunity for owners to get their dogs involved in, but also then they're helping their communities.
00:20:51.599 --> 00:20:58.640
And I think the number of folks we saw interested in participating really spoke to how many people want to give back and want to help their communities.
00:20:59.359 --> 00:21:03.920
And I, you know, I I get uh down about the environmental issues you see every day.
00:21:04.000 --> 00:21:08.000
And I think this is one place where you can start to feel a little powerful, right?
00:21:08.079 --> 00:21:12.559
Sometimes it seems overwhelming, and what could I do as a single citizen?
00:21:12.799 --> 00:21:20.000
Um, but then you find out, hey, I could contribute in the small way, and there are all these other people contributing in a small way, and we could really make a difference.
00:21:20.240 --> 00:21:23.279
I think about I'm gonna put a plug in for the pollinators.
00:21:23.440 --> 00:21:31.920
Um, I always think about this with the pollinator gardens too, when you see like all this environmental destruction and you think, how is this gonna how can I do anything against these big companies?
00:21:32.160 --> 00:21:36.480
And then you read things about like, well, if you plant your own little pollinator garden and all your neighbors do this, right?
00:21:36.559 --> 00:21:41.359
It actually ends up being a really powerful sort of refuge for all of our lovely pollinators.
00:21:41.519 --> 00:21:47.920
And I think our our project is similar in that um together we can make big differences.
00:21:48.240 --> 00:21:49.519
People want to do this.
00:21:49.759 --> 00:21:56.480
And uh the other thing we liked about this sort of approach is that not every area is gonna have the same issue, right?
00:21:56.559 --> 00:21:59.680
So spotted lanternfly might be the issue where we are.
00:22:00.400 --> 00:22:03.039
But someone in uh Utah might have a different issue.
00:22:03.119 --> 00:22:19.200
And really what we're what we wanted to do with this was a sort of a proof of concept study showing that average owners and average dogs could detect subtle odors and be, you know, a sort of a flexible ubiquitous detection force.
00:22:19.359 --> 00:22:20.640
And so we're really excited about that.
00:22:20.720 --> 00:22:22.799
We still have teams that are continuing to volunteer.
00:22:22.880 --> 00:22:33.119
We have some teams in Ohio that are still working in their parks to help uh kind of clear them of spotted lantern fly egg masses, which is it is it's really exciting.
00:22:33.200 --> 00:22:36.799
And I think it's uh um really helps the citizens too, right?
00:22:36.880 --> 00:22:38.799
Where you're like, I can give back, I can make a difference.
00:22:39.359 --> 00:22:39.519
Yeah.
00:22:40.240 --> 00:22:47.440
Yeah, and we know obviously we've all done research and attrition um throughout a study can be very difficult to deal with.
00:22:47.519 --> 00:22:52.240
And and obviously everyone here is a volunteer, all your citizens are volunteers.
00:22:52.319 --> 00:22:59.680
So, like, what safeguards did you have to kind of put in place to ensure the the data that they collected is reliable?
00:23:00.480 --> 00:23:02.319
Yeah, that's a that's a really great question.
00:23:02.400 --> 00:23:06.480
And I think that's one of the challenges with this sort of approach for research, right?
00:23:06.559 --> 00:23:09.039
Is that you need to make sure you can trust your data.
00:23:09.200 --> 00:23:13.039
Um, and so we had put together some really detailed protocols.
00:23:13.279 --> 00:23:29.279
Sally went out to a number of our ORTs and our OTO-recognition trials and our field evaluations to make sure all things were running as as desired, but then also put together these really lovely protocols, gave a lot of coaching to folks where we weren't going to travel to them.
00:23:29.519 --> 00:23:33.519
And if they didn't follow their protocol, they did have to submit videos, their data sheets.
00:23:33.599 --> 00:23:36.160
And if they didn't follow our protocol, their data was not included.
00:23:36.400 --> 00:23:43.680
So we made sure that the data were from dogs who had participated in trials that were run the correct way.
00:23:43.759 --> 00:23:47.440
Otherwise, their data are really not comparable, and we really can't say much about it.
00:23:47.599 --> 00:23:49.759
So we had some safeguards there.
00:23:50.400 --> 00:23:54.319
And you're right, also about attrition, these are volunteers, and it can take a lot of time.
00:23:54.960 --> 00:23:59.759
And most of our attrition was due to that, that uh it's just hard to sometimes fit this in.
00:24:00.160 --> 00:24:11.920
So again, this this project might not be for everybody, um, but it seems like it's uh really interesting to a lot of folks that are probably gonna want to participate and folks that can't, you know, devote this much time.
00:24:12.000 --> 00:24:14.000
There's probably some other project they can get involved in.
00:24:15.039 --> 00:24:16.319
Yeah, that's so exciting.
00:24:16.559 --> 00:24:21.519
I'm trying to um convince my husband to turn in our yard into a like a wildflower garden, like you said, Paul.
00:24:21.680 --> 00:24:22.880
Yeah, like why do we have grass?
00:24:22.960 --> 00:24:26.640
I don't understand why everybody has lawns and they mow their lawn, like it's such a waste of my life.
00:24:26.799 --> 00:24:28.000
Yeah, he's not on board.
00:24:28.160 --> 00:24:29.680
I don't know how long it's gonna take for.
00:24:30.160 --> 00:24:30.640
We'll see.
00:24:30.799 --> 00:24:32.319
It would save a lot of effort, right?
00:24:32.400 --> 00:24:34.960
Like then he wouldn't have to do so much yard work, but whatever.
00:24:35.200 --> 00:24:35.599
Totally.
00:24:35.759 --> 00:24:36.079
Exactly.
00:24:36.240 --> 00:24:38.319
And it's prettier and it's so much prettier.
00:24:38.640 --> 00:24:39.359
So yeah.
00:24:40.240 --> 00:24:41.599
Someday that's my goal.
00:24:41.839 --> 00:24:44.400
So shame him on the podcast.
00:24:44.799 --> 00:24:51.039
So everybody start getting all these hate calls.
00:24:53.200 --> 00:24:54.240
The pollinators.
00:24:54.799 --> 00:24:57.119
Because a veterinarian is a good pollinator.
00:24:59.200 --> 00:25:00.480
Oh my goodness.
00:25:00.720 --> 00:25:02.640
So let's move on to the second study.
00:25:02.720 --> 00:25:17.200
And Sally, you already talked about this a little bit as far as um putting things into a more real-world context and kind of adding more difficulty and exploring how those dogs could participate or learn or um be successful in those new environments.
00:25:17.359 --> 00:25:19.359
So tell us a little bit more about that.
00:25:19.680 --> 00:25:25.440
Yeah, so the range of detection study was sort of uh one half of the second part of the project.
00:25:25.599 --> 00:25:30.079
The other half was kind of putting this into real-world context.
00:25:30.240 --> 00:25:48.640
And the way we did that was almost to pit the uh human visual searches against the dogs and say, okay, you know, we're you're saying that dogs are fine, but we can also visually search this without the hassle of having to have this dog and this other research and all that comes along with that.
00:25:48.880 --> 00:26:10.799
So uh asking people, even our own uh plant pathologist that was on our team to go out and search areas uh to find spotted Latinfly egg masses, uh, other extension personnel that have been trained and go out and do eradication processes uh to investigate half-acre uh sectors of the environment.
00:26:10.880 --> 00:26:16.960
And that varied between some partially managed woodland to sort of wasteland areas.
00:26:17.039 --> 00:26:30.319
Uh, we had one site in Rhode Island that was sort of tucked in behind a solar farm and a yard uh waste sort of dump site for the the city, and go out there and and log and GPS all of the sites.
00:26:30.400 --> 00:26:35.519
And each one of those human sort of searches did that independently, and then we sent the dogs out.
00:26:35.680 --> 00:26:50.640
And the dogs, I yeah, we were able to find the things that were obvious and within our field of vision, but the dogs were saying, no, no, we have we have Oda from you know this sort of more hidden site, this this cryptic location.
00:26:50.799 --> 00:27:03.839
And and some of those places took a long time for us to actually find where that odor was coming from, to include me uh climbing trees on occasion to look down into where like a branch had been broken off.
00:27:04.000 --> 00:27:05.839
So think about the spotted landfly.
00:27:05.920 --> 00:27:12.720
It's trying to uh maintain its offspring, but it doesn't have the ability to sort of protect the eggs once they're laid.
00:27:12.960 --> 00:27:20.000
So while they put that sort of what looks like a mud covering on top of the eggs, they also are laying them in mindful areas.
00:27:20.079 --> 00:27:33.920
They're putting them down into sort of fractured branches of trees, uh down in the grass areas on the underside of uh branches and and in vineyards, the cordons, which are the horizontal members of the vine, uh, that would lay on the underside of that.
00:27:34.079 --> 00:27:44.319
So it takes some sort of sleuthing on the part of the human to get down on your back and look up or climb a tree and look down into a sort of void area to be able to find them.
00:27:44.480 --> 00:27:49.759
And and that's where we found the egg masses that the humans just simply were not finding.
00:27:49.920 --> 00:28:01.680
Um, to to about a two to one ratio, you know, of uh those dogs were clearing far more in far less time than the humans were able to.
00:28:02.079 --> 00:28:02.640
That's great.
00:28:02.799 --> 00:28:03.440
I love that.
00:28:03.599 --> 00:28:09.519
How like I'm just looking at my backyard right now because my trees are very beautiful, but also really tall.
00:28:09.599 --> 00:28:13.519
Like, how how high up do these egg masses get laid?
00:28:13.839 --> 00:28:20.079
Um, well, uh at the tree of heaven is their really like high price tree in all of this.
00:28:20.160 --> 00:28:24.880
And those trees grow, you know, 40, 50 feet tall when left unchecked.
00:28:25.119 --> 00:28:27.839
I never climbed above like 20 feet off the ground.
00:28:30.400 --> 00:28:35.279
I was a paramedic on duty, so you know, if I felt everybody's down.
00:28:35.680 --> 00:28:36.480
Right, exactly.
00:28:36.640 --> 00:28:40.640
So uh yeah, I mean, but we were finding them that high up.
00:28:40.799 --> 00:28:43.279
I don't know that there's uh any solid data.
00:28:43.359 --> 00:28:50.880
I think that if they can access an area and it's a good sheltered spot and they they sort of like the feel of it, they're gonna lay their eggs there.
00:28:51.039 --> 00:28:57.359
Um the other sort of facet of this is uh we are at the big vector with the spotted lanternfly, right?
00:28:57.599 --> 00:29:00.240
We move this thing around the country.
00:29:00.319 --> 00:29:01.279
That's part of the problem.
00:29:01.359 --> 00:29:04.079
They're they're a hitchhiker uh type insect.
00:29:04.319 --> 00:29:09.279
So uh not only on trees that aren't going anywhere, but on bundles of lumber.
00:29:09.440 --> 00:29:17.440
Or in fact, this is how it got to the United States on a shipment of landscape uh stones that laid on the other side of the stones.
00:29:17.680 --> 00:29:27.119
Visual inspection, you know, didn't seem anything awry, but uh, as they sort of unpack that stone, then that exposed those egg masses and they were able to take a hold.
00:29:27.200 --> 00:29:32.960
And that was that one single shipment that has caused this entire sort of issue throughout the United States.
00:29:33.039 --> 00:29:34.480
So we become the vector.
00:29:34.720 --> 00:29:36.480
We transport this material around.
00:29:36.640 --> 00:29:41.599
So sometimes it's not how high, more how deep into something.
00:29:41.839 --> 00:29:48.720
And and a shipping company is not gonna be willing to take every piece of wood out of a bundle of lumber.
00:29:48.799 --> 00:29:50.640
There could be hundreds of examples there.
00:29:50.720 --> 00:30:00.720
But if we can run a dog through and say, yep, this bundle has a target in it, we're going to investigate this smaller bundle, take that apart, find that example, remove it, send it on its way.
00:30:01.279 --> 00:30:06.240
Is that happening now with like detection dogs like at shipping ports?
00:30:07.039 --> 00:30:18.000
So for the spotted landfly, there is uh one or two areas where sort of um USDA or other uh agency dogs are doing that sort of cargo inspection.
00:30:18.160 --> 00:30:21.680
But that model's really been set by our muscle detection dogs.
00:30:21.759 --> 00:30:30.960
Uh, so the zebra mussels uh being transported on um private boats from one lake area to another lake area and populating them.
00:30:31.119 --> 00:30:35.119
And and that's where this sort of model is is really prolific, right?
00:30:35.279 --> 00:30:54.880
The volunteers go out there to a boat launch and the dog runs around the boat and says, Yep, we need to flush the bilge on this boat and and remove all the examples of the zebra mussel so that when you go to the next uh lake and put in there, you're not then distributing this invasive, highly invasive species uh to that next area.
00:30:55.039 --> 00:31:02.559
So we're hoping that maybe we can set up some of those more sort of um inspection type concepts.
00:31:02.799 --> 00:31:08.799
Um right now it's being controlled by these in a very infested area.
00:31:08.960 --> 00:31:16.880
There's a limit on sort of the uh import and export from that area, and that we have to actually say, you know, it's been visually searched.
00:31:16.960 --> 00:31:19.599
But as we've shown, that is not 100%.
00:31:20.400 --> 00:31:22.160
Yeah, not even close.
00:31:22.480 --> 00:31:23.759
Yeah, that's amazing.
00:31:24.079 --> 00:31:32.880
So we've all done research and it is never really perfect exactly the way that you planned it to be on paper.
00:31:33.279 --> 00:31:39.279
What were some of the obstacles or challenges that you encountered on the way in this project?
00:31:39.920 --> 00:31:44.640
I think uh managing the people was uh a lot, right?
00:31:44.799 --> 00:31:47.279
Uh especially that many people.
00:31:47.359 --> 00:31:56.400
And of course, we all know being in the dog world and the animal world, everybody has big opinions about the way things should be and and whether or not something's important.
00:31:56.640 --> 00:31:58.319
And as uh Dr.
00:31:58.480 --> 00:32:08.319
Eriger already mentioned, you know, maybe a citizen scientist or a dog handler is not as interested in the the sort of um robustness of a data set.
00:32:08.400 --> 00:32:13.519
They're more interested in sort of getting getting to the end point or success or the ribbon, right?
00:32:13.599 --> 00:32:15.039
This is the world that they've come from.
00:32:15.119 --> 00:32:32.319
And so trying to bridge that gap and really uh translate what we're trying to achieve from a research study perspective into what is exciting and obtainable and something that is tangible for our citizen scientists.
00:32:32.480 --> 00:32:34.559
And I think that was one of our biggest challenges.
00:32:34.640 --> 00:32:37.519
And we we overcame that a lot with uh, like Dr.
00:32:37.680 --> 00:32:51.759
Erica was saying, you know, the the protocols that we developed and sort of the those safety rails that we put up, but also uh creating a sense of camaraderie within the uh study group, population group, uh community.
00:32:51.920 --> 00:33:07.759
So a lot of uh closed Facebook group to be able to share sort of experiences and and how to overcome sort of technical challenges that they faced uh was one of the big, big sort of uh uh things that we did to overcome some of those challenges.
00:33:09.039 --> 00:33:11.599
Yeah, I bet they uh really enjoyed all that.
00:33:11.759 --> 00:33:14.079
I could see like because of the dog world, right?
00:33:14.319 --> 00:33:17.920
The dog sport world, everyone goes for the social stuff too, right?
00:33:18.079 --> 00:33:22.079
So like the fact that you have all these uh folks um together.
00:33:22.160 --> 00:33:23.039
I think that's great.
00:33:24.160 --> 00:33:26.559
So I want to know can any dog do this?
00:33:26.640 --> 00:33:27.839
I have a kind of corso.
00:33:28.240 --> 00:33:30.000
Um, what if you have a bulldog or a pug?
00:33:30.079 --> 00:33:32.079
What if you have a dog that doesn't really have a great nose?
00:33:32.240 --> 00:33:33.759
Like, are there special breeds?
00:33:33.920 --> 00:33:38.160
We always think about certain breeds for search and rescue and drug detection and sniffer dogs.
00:33:38.400 --> 00:33:40.079
Who's who's the best dog?
00:33:40.480 --> 00:33:42.640
Yeah, all dogs are the best dog.
00:33:42.880 --> 00:33:46.160
They're absolutely every single one of them is the best dog ever.
00:33:46.480 --> 00:33:50.880
Uh, but our research actually followed up on what we know from other researchers.
00:33:51.039 --> 00:33:51.519
So, Dr.
00:33:51.599 --> 00:33:54.480
Nathan Hall down at Texas Tech, and then more recently, Dr.
00:33:54.640 --> 00:34:00.720
Salomon from Budapest found that there are very little differences between breeds.
00:34:00.799 --> 00:34:09.519
And in fact, the most recent study showed there was more variation um within breed than between breeds uh when set on a detection task.
00:34:09.760 --> 00:34:12.239
So, fun story from our project.
00:34:12.400 --> 00:34:18.960
One of our star sniffers was a little Boston terrier who was searching in a vineyard.
00:34:19.280 --> 00:34:22.239
So classic little brachiocephalic dog, right?
00:34:22.320 --> 00:34:23.679
Not your typical detection.
00:34:24.159 --> 00:34:25.039
Can barely breathe.
00:34:25.360 --> 00:34:26.719
Can barely breathe, right?
00:34:26.880 --> 00:34:28.320
And we have other considerations.
00:34:28.559 --> 00:34:29.599
Are they heat tolerant?
00:34:29.679 --> 00:34:36.639
All of that stuff, exercise tolerant, but but we're searching in a vineyard that was supposedly clear of spotted Lentenfly.
00:34:36.800 --> 00:34:44.000
There was no samples that we had placed in that area, and this dog kept saying, no, no, no, mom, it's this plant right here.
00:34:44.079 --> 00:34:45.519
It's this plant, it's this plant.
00:34:45.760 --> 00:34:49.519
And I assumed I hadn't seen the dog work very much before.
00:34:49.599 --> 00:34:54.559
And I assumed, well, maybe it's just a little confused, or the hen was prompting it, or something else is going on.
00:34:54.719 --> 00:34:56.079
Um it was a very hot day.
00:34:56.239 --> 00:34:59.920
And and I looked, I visually looked, and I couldn't see anything.
00:35:00.079 --> 00:35:04.400
And finally the hen was like, like, I really feel like my dog is not wrong.
00:35:04.559 --> 00:35:07.440
I'm really, I'm like, okay, well, I'll get down on the ground.
00:35:07.599 --> 00:35:15.199
So here I am laying on my back, looking up at the sky through the uh the vines of the vineyard.
00:35:15.360 --> 00:35:21.599
And sure enough, there on the underside of the cordon was a naturally laid spotted lanfly egg mass.
00:35:21.679 --> 00:35:25.039
And when I looked to my left and right, there were other examples.
00:35:25.199 --> 00:35:32.000
And so that that had passed the muster of the vineyard owners and workers who had said there was no spotted lanternfly.
00:35:32.079 --> 00:35:33.599
We had inspected it visually.
00:35:33.760 --> 00:35:39.360
Other dogs had missed that particular example, and this dog said, nope, it's right there.
00:35:39.679 --> 00:35:45.920
So the most atypical dog is there, sort of solving the problem for the day.
00:35:46.159 --> 00:35:48.000
That's that is amazing.
00:35:48.159 --> 00:36:12.800
And yeah, like you said, you think about certain breeds, and that's because you know, our longer snooted dogs have a larger kind of square footage of uh epiepithelial, olfactory epithelium, and our little dogs have a tiny little real estate in there, and so you would think that they they're using so many more receptors when you have a larger head and snout that you can smell better, but it this is a great story to say.
00:36:12.880 --> 00:36:14.800
It doesn't matter, you can still smell.
00:36:15.360 --> 00:36:18.239
He's like, I can barely breathe, but I can do it.
00:36:18.559 --> 00:36:24.719
Yeah, all of my breathing goes to smelling or oxygenating my body.
00:36:25.039 --> 00:36:26.400
I don't care about that.
00:36:26.559 --> 00:36:27.920
I got those lantern flags.
00:36:28.000 --> 00:36:29.360
Oh my gosh, that's fantastic.
00:36:29.519 --> 00:36:29.840
I love it.
00:36:30.239 --> 00:36:31.840
Fabulous, love it, love it, love it.
00:36:32.000 --> 00:36:32.960
All right, excellent.
00:36:33.119 --> 00:36:35.360
Well, I'm gonna ask each of you separately.
00:36:35.440 --> 00:36:41.039
So, Erica, um, what is one thing that you want the hive mind to take away from today's episode?
00:36:42.079 --> 00:36:47.119
Um, I'm I just love the project because it highlights what your dog can do.
00:36:47.360 --> 00:37:02.079
And uh, you know, I think we see our dog lounging on the couch and they're adorable and they're fun, but I think reminding ourselves of the of the amazing things they can tell us if we train them and that they are experiencing this whole other world that we don't appreciate.
00:37:02.239 --> 00:37:06.000
So uh when your dog wants to sniff on a walk, maybe let him stop and sniff, right?
00:37:06.079 --> 00:37:10.559
It's like letting somebody look at my phone one more time to read the story I'm interested in.
00:37:10.719 --> 00:37:20.000
So, you know, I I think that's what's really exciting is seeing what our average doggos can do and and help help us with our community issues.
00:37:20.639 --> 00:37:21.280
That's cool.
00:37:21.519 --> 00:37:22.320
And Sally?
00:37:23.599 --> 00:37:26.079
I don't know that I could say much beyond that.
00:37:26.159 --> 00:37:39.360
It it's uh get out there and let them sniff, let them experience the world the way they have evolved to to experience it and appreciate how much that sort of rallies their spirit in the moment, right?
00:37:39.440 --> 00:37:44.960
That they they truly do get a lot of enrichment or fulfillment out of that.
00:37:45.119 --> 00:37:50.559
And if we can put that to a sort of a combined purpose, even better.
00:37:51.039 --> 00:37:51.840
That's great.
00:37:52.480 --> 00:37:58.000
And can I make make a plug that we have um a new study coming up that we're recruiting for?
00:37:58.239 --> 00:38:01.280
Um we're looking for dogs in in Virginia.
00:38:01.519 --> 00:38:05.119
Uh our prior study used dogs that had some scent work background.
00:38:05.519 --> 00:38:18.400
It was a proof of concept study just to show that again, these non-purpose bred, non-professional dogs can be useful detection tools for really subtle targets like agricultural targets.
00:38:18.559 --> 00:38:24.480
Um, we're expanding that now to naive dogs, so dogs that don't have any prior uh nosework.
00:38:24.719 --> 00:38:25.440
Yes, that's right.
00:38:25.760 --> 00:38:30.880
So we're looking for dogs uh down in the New River Valley, Roanoke Valley area, and up in northern Virginia.
00:38:31.199 --> 00:38:36.400
So you can always get in touch with us and we can put you on our list if that project doesn't work for you.
00:38:36.480 --> 00:38:41.039
We'll hopefully have projects in the future that might um your dog might be a candidate for.
00:38:41.360 --> 00:38:42.000
Super cool.
00:38:42.159 --> 00:38:42.559
I love it.
00:38:42.800 --> 00:38:44.239
Amy and I are gonna enroll our dogs.
00:38:44.960 --> 00:38:45.360
Perfect.
00:38:45.519 --> 00:38:47.760
I know my dog wants to do it so badly.
00:38:48.239 --> 00:38:51.360
Jenny, the hairs on the on the problem.
00:38:51.920 --> 00:38:52.559
Perfect.
00:38:52.880 --> 00:38:53.599
I love it.
00:38:53.840 --> 00:38:54.639
Oh my goodness.
00:38:54.800 --> 00:38:59.280
Well, I want to move into our next section, which is buzz into your beeswax.
00:38:59.679 --> 00:39:01.599
We want to know more about our guests.
00:39:01.760 --> 00:39:13.199
So um, this is where we pull back that curtain and discuss something off topic about your lives, a special story or a funny anecdote so that we know who you are, aside from just the research that you publish.
00:39:14.000 --> 00:39:20.480
Uh I'll tell you our um my partner Paul and I, our current fascination is our local possum.
00:39:20.639 --> 00:39:27.840
Um, so one we have chickens, and one of our chickens hops out of their coop each day to lay her egg and it's right along our house.
00:39:27.920 --> 00:39:32.559
Um, and we had noticed she'd been doing that, but then also noticed the eggs had been cracked and eaten.
00:39:32.719 --> 00:39:36.480
And it turns out uh we have a lovely possum that comes by.
00:39:36.639 --> 00:39:42.880
And so that's our donation to our our um neighborhood possum is leaving that chicken's egg out there.
00:39:42.960 --> 00:39:48.000
And then every once in a while we're lucky enough to open the door at the right time and see our cute little possum cruising by.
00:39:48.079 --> 00:39:50.480
So that's amazing.
00:39:50.800 --> 00:39:53.360
It's not Jimothy, but it is cool.
00:39:54.000 --> 00:39:58.480
I know we do we do need to name it so that it rises to the level of Jimothy.
00:39:58.719 --> 00:40:02.000
Yeah, we have some we have some toads around here too that we've named as well.
00:40:02.400 --> 00:40:03.039
Oh, cool.
00:40:03.280 --> 00:40:04.159
I love it.
00:40:04.559 --> 00:40:05.119
Love that.
00:40:06.800 --> 00:40:09.679
And Sally, do you want to tell us some stories about your life?
00:40:09.920 --> 00:40:13.119
I sense a theme here that we're all animal people.
00:40:13.199 --> 00:40:18.159
And my my ongoing fascination for a very long time has been with Corvette.
00:40:18.239 --> 00:40:31.039
So I have uh, you know, feeders and I offer trinkets to my feathered friends, and I uh love to just watch them investigate that space and think about it.
00:40:31.119 --> 00:40:35.519
And every once in a while I do get a little treasure left uh for me.
00:40:35.679 --> 00:40:40.880
So that makes my day when when a crow brings me something awesome.
00:40:41.119 --> 00:40:43.760
So I love that so much.
00:40:43.920 --> 00:40:46.239
My daughter tried to do that for one week.
00:40:46.559 --> 00:40:49.679
I was like, well, so yeah.
00:40:49.920 --> 00:40:56.480
So she's like out and she like put a mirror outside to like try to get their attention, like on the roof outside of her bedroom.
00:40:56.639 --> 00:41:00.000
And then she put some food up there, and I was like, How do you know the squirrels aren't gonna eat that?
00:41:00.159 --> 00:41:00.960
Like, are you watching?
00:41:01.119 --> 00:41:01.840
Like, what's happening?
00:41:02.000 --> 00:41:05.440
And she's like, Oh no, I know it's the uh I've never seen a squirrel up there.
00:41:05.519 --> 00:41:07.119
I am I'm sure it's gonna be the crows.
00:41:07.199 --> 00:41:11.519
And I was like, just the crows, no other bird, just the crows.
00:41:11.920 --> 00:41:14.880
And so so I was like, How's it going with your buddies the crow?
00:41:15.039 --> 00:41:16.320
She goes, I'm a crow.
00:41:19.599 --> 00:41:21.360
I've always wanted to do that, right?
00:41:21.440 --> 00:41:35.679
The crows are so amazing, and I love that they bring you trinkets, and I love that um they uh pass on that memory to generations, people that they like and or people that they don't like, and then everybody knows who Kenzie's on that list.
00:41:36.880 --> 00:41:40.719
Yeah, she's like that girl stopped giving us food.
00:41:41.920 --> 00:42:00.079
I love the book Um Gifts of the Crow, and it talks about that, the research they did out in Seattle and uh how they are now having to wear disguises so that they are not subject to uh Crow's distent while walking through town on like a coffee break or whatever.
00:42:00.239 --> 00:42:08.079
So, but uh my my goal coming from a search and rescue background, I my goal is to someday have a search and rescue COVID.
00:42:08.320 --> 00:42:10.159
So we'll see if I have to.
00:42:10.320 --> 00:42:12.239
That would be a good idea.
00:42:12.400 --> 00:42:13.039
Fantastic.
00:42:13.760 --> 00:42:15.199
Oh my gosh, I love that.
00:42:17.760 --> 00:42:19.039
That'll be a future interview.
00:42:19.360 --> 00:42:19.920
I love it.
00:42:20.239 --> 00:42:23.360
Well, here at the Behavior Buzz, we know you are all busy bees.
00:42:23.679 --> 00:42:24.559
Get busy bees!
00:42:24.960 --> 00:42:26.960
Who live and die by the science like we do.
00:42:27.039 --> 00:42:34.559
So we'll put all of the references from today's show on the website, including those two papers written by the lovely doctors.
00:42:34.719 --> 00:42:38.639
So buzz on over to the Honeypot page to find those.
00:42:39.039 --> 00:42:41.679
And we just want to say a big thank you to Dr.
00:42:41.840 --> 00:42:42.639
Erica and Dr.
00:42:42.800 --> 00:42:44.079
Sally for joining us today.
00:42:44.239 --> 00:42:45.599
This was really a fun discussion.
00:42:45.760 --> 00:42:49.119
I love kind of these down and dirty, like, what can we do?
00:42:49.280 --> 00:42:50.559
How can we help the world?
00:42:50.719 --> 00:42:51.679
kind of discussions.
00:42:51.920 --> 00:42:56.800
And that also lends itself to how smart are the animals that we work with, right?
00:42:56.960 --> 00:43:01.599
Because we um don't always appreciate everything that they do for us.
00:43:01.760 --> 00:43:10.239
And so thank you for this discussion about research, about invasive species and citizen science and how we all can help.
00:43:13.519 --> 00:43:18.320
Thank you to our premium sponsor, PRN PharmaCal, the makers of Reconcile.
00:43:18.559 --> 00:43:25.440
Reconcile is an FDA-approved drug for the treatment of canine separation anxiety in conjunction with the behavior modification plan.
00:43:25.920 --> 00:43:36.000
PRN PharmaCal is committed to meeting the evolving needs of modern veterinary medicine and dedicated to developing products to strengthen the bond between pets and their people.
00:43:37.760 --> 00:43:44.400
And HiveMind, we thank you especially because without listeners like you, we would have nothing to buzz on about.
00:43:44.639 --> 00:43:48.800
So grab a drink and join us next time for cocktails and conversations.
00:43:49.280 --> 00:43:51.519
Follow us on all our socials.
00:43:51.760 --> 00:44:00.320
Facebook at Behavior Buzz, Instagram at Behavior Buzz, and our website, BehaviorBuzz.com.
00:44:00.719 --> 00:44:03.679
Be positive, be informed.
00:44:03.920 --> 00:44:06.480
Now Buzz Awesome.
00:44:08.400 --> 00:44:10.400
Okay, if you don't like it, that's fine.
00:44:10.719 --> 00:44:11.280
All right.
00:44:13.840 --> 00:44:16.079
Oh, sorry, Sally, this is you, wasn't it?
00:44:17.119 --> 00:44:18.400
Now you can cut it out.
00:44:19.119 --> 00:44:21.519
I need to I need to pull up my uh questions.
00:44:30.320 --> 00:44:31.119
We're back.
00:44:31.519 --> 00:44:32.400
We're back.
00:44:33.280 --> 00:44:35.360
I'm sure that'll be in the podcast now.
