Can AI Benefit Wildlife? Conservation Technology Says Yes!

00:00:04
Speaker 1: Hi, I'm Rick Schwartz.

00:00:06
Speaker 2: Wuendosis World, Marco went.

00:00:08
Speaker 1: Welcome to Amazing Wildlife, where we explore unique stories of wildlife from around the world and uncover fascinating animal facts. This podcast is a production of iHeartRadio's Ruby Studios and San Diego Zoo Wildlife Alliance, an international nonprofit conservation organization which oversees the San Diego Zoo and Safari Bard.

00:00:26
Speaker 3: All Right, Rick, so we wrapped up our last episode telling everyone that you learned about something called conservation technology, and I see that we have an appointment at the San Diego Zoo Wildlife Alliance Beckmann Center. That's the main building where a lot of our conservation scientists work. This episode was going to be about our Asian Rainforest Hub. So how does conservation technology fit in all of this?

00:00:49
Speaker 1: Well, Marco, all I can say is be prepared for a whole different side of conservation that often happens behind the scenes, or at least if not behind the scenes, it can often go unnoticed. And you're right. We started planning this episode around our Asian Rainforest Hub and this includes our projects and partners that are focused on needed work to maintain sustainable habitats for tigers and orangutans, and one of my favorites beingerings and sun bears and hundreds of other species that inhabit the region.

00:01:16
Speaker 2: Oh man, that sounds really interesting. Now. I know a lot of our conservation scientists.

00:01:21
Speaker 3: Do amazing work, like studying and preserving genetic materials in the Frozen Zoo as an example, and definitely a whole lot more. But we're talking about conservation technology. So is this about radio collars and like trail cameras.

00:01:36
Speaker 1: Oh yeah, yeah, radio callers, trailcams sometimes called camera traps. These are both pieces of conservation technology. But get this, I found out there is much more to it now than just camera traps and callers. With the rapid growth of technology from algorithms and artificial intelligence, these are all things we get a sort of at a consumer level. Conservation technology is also rapidly advancing along with these technologies.

00:01:59
Speaker 2: Oh man, that's super fascinating.

00:02:00
Speaker 3: I mean, seriously, I can't wait to discuss what else is there and how it's all going to be used in our Asian rainforest hub to help with these conservation efforts.

00:02:08
Speaker 1: And that's the interesting twist to the story, Marko. Oh yeah, Well, As I was digging around to find out more about our work in the Asian Rainforest Hub, I spent some time with our conservation technology team and found out that some of the latest technology has been deployed here in the Southwest Hub and more recently in the Amazonian Hub. The success of some of these technologies in the Amazonian Rainforest now has our conservation team very excited to deploy it in the similar habitat of the Asian Rainforest Hub.

00:02:36
Speaker 3: Oh wow, So when do we get to talk to someone from the conservation technology team.

00:02:40
Speaker 1: Well, I say we head over to the Beckman Center now and go have a conversation.

00:02:44
Speaker 2: Oh man, that's a great idea, you know, I'm sure the guests.

00:02:46
Speaker 3: Now, we're eighteen hundred acre conservation park and there's a portion that's inaccessible to guests, but it's a unique area called the Beckman Center where all of our conservation scientists and researchers do all this incredible work.

00:02:57
Speaker 2: So yeah, I'm super pub let's go.

00:02:59
Speaker 3: Rick.

00:03:01
Speaker 4: I am Ian Ingram. I am a conservation technology scientists here at the San Diego Zoo Wildlife Alliance, and I lead the Conservation Technology Lab.

00:03:11
Speaker 1: And what does conservation technology mean for the average person, what would that mean.

00:03:17
Speaker 4: I mean, technology is a pretty broad term. Everything in humans build is technology. We're really mostly focused on the use of computers, embedded computers and devices like that, so digital electronics that's applied to the conservation problem.

00:03:32
Speaker 3: And actually with this location, we're in a unique spot here at the Safari Park. What's the name of this building and what's so important about this location?

00:03:38
Speaker 4: We're in the Beckmann building, which houses at least the majority of the conservation science and wildlife health team. So there are numerous scientists and researchers of all sorts of stripes working here in conservation, genetics, disease investigations, recovery, cology, population sustainability.

00:03:56
Speaker 1: I mean they're actually four more.

00:03:57
Speaker 4: But so there's, you know, a very broad swath of folks who are tackling our conservation goals with different tools.

00:04:08
Speaker 3: I'm curious to because admittedly I was doing a little like stalking of you on the internet and I found some interesting facts about you that you have a background in an artist and in robotics. Right, Can you share a little bit about some of your ast history.

00:04:20
Speaker 4: Sure, I'm trained as a roboticist, specifically in underwater robots. My goal when I was a kid was to find the Lockness Monster, and at the time, it seemed as if the best way to go about that was to use underwater robots. So I studied that, and then I segued and look at it for the giant squid. I was advised that maybe looking for an animal that may or may not exist might be a bad career move. I took that advice, and so I segued in the giant squid, which definitely existed, but was still kind of mystical in a way. The time, nobody had seen them alive. Really, they'd always been washed ashore or more buns floating on the surface. And I mean to summarize the arc of my career has sort of been about initially looking for really large animals that may or may not exist, to working with very small animals that definitely exist, and working with animals that might not exist for much longer if we don't help them.

00:05:14
Speaker 3: Yeah, no, kidding, you're working at We always just talk about, you know, especially when you're a child, like what sparks your interest and conservations.

00:05:20
Speaker 2: I picture you, you know, in love because.

00:05:22
Speaker 3: Someone actually hinted at me that you're in love with the locknest monster too, so it's interesting that's where all it came from. And now you're knee deep in like really essential conservation projects with real life animals.

00:05:31
Speaker 2: Not to say that the locknest monster doesn't exist.

00:05:33
Speaker 3: Or does you know, I'm not conferving or denying it, but now you're doing some really wicked work out there.

00:05:38
Speaker 2: Can you talk about the projects that you're involved in at the moment.

00:05:42
Speaker 4: We're doing a number of different things. A lot of them relate to the application of machine learning, you know, artificial intelligence, to processing data from sensor systems. So that can mean image data basically photos that are coming back from camera traps and similar devices, to video data from similar sorts of camera devices, to audio data coming from audio recorders, and also movement data that comes from accelerometers, which are for people who aren't familiar with accelerometers, they're tiny little sensors that measure acceleration, which is to say, movements, and you've got them in your phone, and your phone actually is using them to learn things about you too, and we're applying some of that same technology to elephants and similar species. So the machine learning aspect makes processing what are essentially massive data sets at this point much more efficient in collaboration with humans. Still, who check that the mL isn't totally misleading us.

00:06:40
Speaker 1: Well, I have a couple of questions about that. To start off with, why do we need, as you say, mL or machine learning to help with this? For instance, you mentioned camera traps or cameras that are set out in the wild by humans to take pictures when wildlife walks by. So I guess my question is how does that work? How does the camera know what to take.

00:06:57
Speaker 2: A picture of?

00:06:57
Speaker 1: And then I guess additionally, why do we need to computer to go through that set of pictures or data? Why can't we just look at it and say, okay, there's a leopard or or there's a monkey and so on.

00:07:06
Speaker 4: You absolutely can do that, and that's how it was done, and it still is done to a large extent. People look at the photos and identify what they are. On Zooniverse, for instance, which is a partner that we work with. The fact is that the bulk of images coming in is so large that that's actually prohibitive at this point, and a lot of times something goes wrong and the camera trap captures something that isn't even real data, like just grass blowing in the wind. And I'll get back to that in a moment, since you asked how they work, So the mL can buy MLME machine learning algorithm can very quickly look at those images and throw out the ones that don't have animals in them at all, meaning that the citizens scientists who contribute on zooniverse have that many fewer false positives as we call them, to look at images that don't contain any animals, and it can also identify which animals are in there, and that speeds things up greatly. So, as an example, a data set that was comparable one that took us six months to have citizens scientists label doing it the old way only took us three weeks. And that's when the mL goes through first and says what everything is, and then a person goes back and looks at it on zoooniverse and says, yeah, that's right, that's right, that's right, and then that's not right, and then it gets thrown back through the soup. The camera traps are triggered by a passive infrared sensor. So this is a sensor that's looking for the movement of an animal in front, but also a warm animal, an animal that's warmer than the background, which actually gets into a totally other thing that we're interested in doing, because a lot of animals aren't warm blooded and they don't trigger the camera traps particularly well, so ecdotherms like reptiles of various sorts and amphibians, and so if you're using a camera trap in that context, there's some hacks you can use to try to create that signal that the PIR will trigger on the passive infrared sensor. But what you can definitely do is apply another kind of machine learning sort of paradigm which is called EDJAI, which is the use of write on the device itself. So we have another project called scrubcam where instead of being an off the shelf camera trap that triggers with the PIR, the scrubcam is using an EDGAI machine learning model constantly looking at what it's seen and identifying it with the AI and then triggering only when it sees what it needs to, or actually really just recording what it sees when it needs to.

00:09:22
Speaker 3: Whoa a camera trap that knows when it should or should not record.

00:09:26
Speaker 2: Wow, that's pretty wild.

00:09:27
Speaker 3: I mean, it sounds like a lot of tech and computer science to me, But this might be a good time to ask, I mean, why are these images so important? Why can't you research or just go out in the wild and look for footprints from the animals or maybe even look into their droppings. Why is having this AI or artificial intelligence out there so essential.

00:09:48
Speaker 4: Well, there's two answers that question. One is that you can't take action if you don't know what the problem is, and you don't know the extent of the problem. So we can't just have sort of anecdotal ideas of whether a given animal is reducing in population size or actually improving. We have to do population studies and that's a big part of what we're applying those kinds of techniques to the camera traps and things like that. The other part of your question is, well, there's two factors to that. You were talking about a person going out and looking for tracks or spore and a that's a lot of labor. That's a lot of people that have to be there to equate the efficacy of a large array of camera traps. There's actually a lot of different pacets to this, because there's also the fact that people themselves disturb the habitat when they go in there, and a lot of what we're trying to do is to gain this data in the least invasive possible way, and just the as one of my colleagues called it, the ball of smells that a human being represents is the problem. I mean, you leave a little bit of that on the camera trap when you deploy it, which actually gets into another thing, which is stuff we're experimenting with where we would use robots to deploy the sensors too, so humans wouldn't even have to enter the habitat being studied. And then there is the fact that animals don't show up when people are around, and animals don't always go to the places where people are. And then there's the fact that that people don't detect a lot of things, So we have the potential to use sensors that can detect things that people can't detect. When it comes to the acoustic side of things, and we have an acoustic recorder out there, that often means recording into the ultrasound. So what defines ultrasound is sound that humans can't hear. It's higher frequency, higher pitch than humans can hear. And a lot of animals are vocalizing in that range. So bats and rodents, for instance, are vocalizing in that range. And on the other side of the frequency spectrum is the infrasound, the sound that's too low for humans to hear, and elephants are using that too, So some of our sensor systems are recording in those places and humans wouldn't even know there was something happening there to begin with.

00:11:47
Speaker 3: That's really interesting for me because you know, in our nocturnal episode I reference you know, the realities behind the realities of the realities, and you're mentioning, you know, different acoustics and sounds, and it's a jungle as an example. And in the past I've worked with castwres, you know, very nique vocalizations that humans can't pick up. So it gets me really excited thinking that we have technology now I can immerse themselves in a habitat where wildlife isn't necessarily going to react to, say maybe a human giving a cough in the middle of the jungle or sloughing up some skin cells that a mammal might pick up that now we might be able to like be there observed behavior with that physically being there but see some really unique stuff that we probably never would have really in seene or you mentioned, technology can pick up sounds or maybe even some visuals that we just can't pick up, and we'll know even more about a certain species, which kind of makes me think about there's really unique environments that are really hard to get to, you. I mean, everything from like the Arctic of the polar there, right, I have frigid temperatures, or my favorite front is like rainforest, you know, but even then, like that presents challenges in its own, right. I mean, can you talk a little bit at maybe some projects we're trying to focus on in our Amazona a rainforest habitat with really unique species and like thick thick jungle, it's kind of almost in near impossible to get to and access data, right, Yeah.

00:12:56
Speaker 4: I mean one of the maybe more prosaic aspects of making any of these things is powering the devices. So they have to have enough power to run, so either have to be low power and run on just batteries like you would have and consumer device, or they might use solar power. But in the Amazonian rainforest, it is not easy to use solar power because the trees are working against you, so you can put them panel a solar panel up higher and then run the power down, which isn't really that easy to do, but that's a major concern working in that kind of Habitat to the question of what we're doing there. We do a lot of different things across the organization in the Amazon, mostly the proving Amazon. A lot of the projects of the Conservation Technology Lab is involved in are connected to camera trap arrays that are deployed that are paralleled by audio moapacoustic recorder arrays, so we're getting images and video and sound of different animals that are there. It's probably worth bringing up that there's this whole idea that that which you see in a camera trap is different than that which you'll hear, because a lot of cryptic species will never show up in a camera trap either because they're not on the forest floor, they're not big enough, but they often will be making noises that you'll pick up with the audio recorders, and so that's why having those two different paradigms of sensing are so important.

00:14:14
Speaker 1: Now, I like that you brought that up. I think that's important to remember too that for as many camera traps are deployed in thousands and thousands of images that come in from them, that it is a very narrow window. You have to have the camera trap pointed in the right direction, right at the right level for a particular species, and hope that they walk on the right side of the tree where your camera is and not the left side of the tree where your camera's not pointing. So that's a really good point that it's a very limited window and which you're getting. Obviously, we can use our best guests by understanding trails and how the environment is used by the species. But that does then bring us right back to what you said about the acoustics needing that audible side of things, which is picked up differently, it travels differently through the forest, et cetera. We were talking a little bit beforehand too about all this work that is being done in the Amazon for us and how how what we have learned, what's developed over time is now going to be something we can apply to the Asian rainforest as well, because the challenges there aren't dissimilar. What would you say has been in your time doing this work, the most interesting thing you have learned from something going right or wrong out in the field.

00:15:17
Speaker 4: I mean, I guess. The first thing I'd say, and this is just something that I know after many decades of working as an engineer, is that things go wrong and you have to know that they're going to go wrong, and you have to test. It's pretty much the cornerstone of making something work that you just tested a lot. We in the Conservation Technology Lab have this internal mnemonic that we use Bora Bora which stands for bench. Is the b O is the outdoor learning lamb, which is just this teaching lamb that's right outside this building, the Beckmann building, and our is the reserve for the Biodiversity Reserve, which is this eight hundred acres of land that's immediately adjacent to the Safari Park, so just a little further afield. And then a is a field like our remote locations in Small Bar or in the Asian rainforest or in other places. And so we work as hard as we can to make something work on the bench, which is the first part, and then we take it to the outdoor learning lab which is just outside the door, and immediately realize that we forgot something.

00:16:15
Speaker 2: That's why it's called the learning lab, right, Yeah.

00:16:17
Speaker 4: For us, it's the learning what we've forgotten what we were done about lab and then once we get that, we take it to Sagebrush, which is our sensor network in the biodiversity reserve. It's the brush stands for Biodiversity Reserve ubiquitous Sensing and habitat. And then we prove it out there, and we do that with projects that we're doing there with cougars and rattlesnakes that are very interesting species that are local to our southwest area. And then if it passes that test and it goes further afield. And the reason it's Bora Bora instead of just Bora is that entering, like many things, is iterative, and you end up going back to the drawing board like Wiley coyote and having to start the whole process over again. So you know, there are stories of things going around. The reason I'm pausing so much is I don't want to like accidentally a point of finger at some point.

00:17:03
Speaker 2: Yeah, you don't have to out I guess.

00:17:05
Speaker 1: I guess what I was getting at is that I know, just from you know, working outdoors, working with the wild, and working in conservation, that sometimes your best lesson comes from something you didn't even realize, something you had to learn or it's the most unexpected thing that was like, oh duh, you know, and try and outthink the situation all you want, but it seems that the animals will always teach us something about the outdoors in general, teach us something.

00:17:27
Speaker 4: So I mean, this isn't an example the worst thing that ever happened, But we often find that wood rats like to nibble on the cables of the things that we've got out in the biodiversity reserve. So, and it's a simple solution. You just don't run the cable past where they live. That's the area they care about, and that's where they start exploring with their teeth, which is.

00:17:45
Speaker 2: What rodents do.

00:17:47
Speaker 1: So I've been out on the reserve saw a lot of the different locations where the camera traps are. So for here, just locally in our southwest environment, we have this stuff deployed to look at what's been some of the most surprising, the most interesting things you have seen come up in all this data or were there no surprises? You're like, no, everything's out there, we.

00:18:05
Speaker 4: Expected well, you know, it kind of speaks to your earlier question about trying to do surveys with just people out in the woods versus doing it with the equipment. I've never seen a bobcat in the wild ever. I think someone pointed one out once and I couldn't tell that it was about. But we actually see them on the camera trap images all the time. So with these regular camera traps were able to kind of see this aspect of what's going on in there that otherwise you wouldn't see at all. We see a lot of other kinds of things. I mean, it's always interesting to see who's eating whom. We get a lot of camera trap images of cougars would say a skunk in their mouth, you know, and yeah, maybe that's not so crazy, but you know, then you know that they're actually catching skunks and enjoying them.

00:18:49
Speaker 3: I remember seeing one of those field cams out in the reserve and I saw one of a skunk walking out with look like a gopher in its mouth, and I forget, like they're omnivores, but you know, like to your point, you don't really see that with your eye, you know, so you notice these field cams and it's think even being a wildlife you're specialist, and having these field cams as a tool for us to use is a good angle to really capture certain behaviors, like for instance, the western growingou was at Conder Ridge. There were certain aspects of their behavior between the pair that I had never witnessed. Even though I was trying to be the sneakiest specialist that I could be hiding behind a pine tree, they knew I was there.

00:19:20
Speaker 2: You know, they still modified their behavior.

00:19:22
Speaker 3: The second I lot they knew it, and they just showed me some really interesting stuff utilizing those field cams. So there's a lot of good potential in tech, I think with that.

00:19:29
Speaker 4: Yeah, something that I often think about is how animals are alive all the time. It sounds like a simple thing, but as a bird watcher, an animal watcher for most of my life, you get to the point where you're realizing that you're only seeing them at certain times, but they're doing everything else the rest of the time. If there's a storm, they're finding someplace that they're a bird to roost, and they're wet and so on, and then they're muddling in their nests if they're a squirrel or whatnot. But we're diurnal beans, and even when we occasionally stay up to be nocturnal, we're not doing it all the time, and so there's a lack of overlap between when we're active and when we're not, and when the animals are active and when they're not. And the device is the camera, traps and other sensors can be active all the time, so they can catch all those little secrets.

00:20:16
Speaker 3: Yeah, and I'm kidding, right, and all the nuances we we'll know with a certain species like the polar bear. And again back to the Asiatic Hub where we're doing work with the Asiatic black bear as an example. And still you know, in a thick, vibrant rainforest habitats, it's really hard to track a bear, and even if you could, it's going to modify its behavior in some regard to and me. We were talking about this before we started recording Buddy how I'm definitely not a tech guy, but I can't appreciate technology and the advances that it's helping us out in these conservation efforts, and tied to that, helping out communities that are trying to live next to this wildlife too, which I think it's just one of the most important things I.

00:20:51
Speaker 1: And I want to take a moment and ask about the rapid developments and technology for example, and then probably dating myself. But back when I started working professionally with wildlife care and conservation, the trail camera was just a box with a thirty five millimeters camera and it with film and everything. And then that got updated eventually as technology moved forward to digital and then it was HD as technology advanced even further, but you still had to go out there and retrieve the data cards and so on, you know. And with Selle technology now where we're seeing many devices that just upload data right to the cloud directly via a sale signal, so no need to go back out there and have the humans disturb the environment. It's amazing and we hear in the future and probably not too distant future where robots are going to be helping us with conservation as well. What are some of the things robots will be used for in conservation.

00:21:33
Speaker 4: There's a broad spectrum of things we think we can use them for. A lot of those would require that sort of turnaround moment where the longevity of the robots is greater the short term application that we're planning to use, and I wouldn't say proprietary because the philosophy of our organization and also the Conservation Technology Lab is that we share these kinds of results pretty openly. Is to use the quadrupedal robots and VHF tracking. So we have a collaboration with the Engineer Exploration at UCSD, the University California, San Diego, and they had developed a device to do VHF tragging. So this is where an animal has a beacon attached to it, micro radio collars, lego radio collar. There's lots of different kinds of collars, but there are some that use these VHF tags as a way of tracking them. And historically you'd go out there with a large antenna and you'd zero in on where they are by wandering around and seeing where the signal strength was stronger. Are collaborates at UCSD. They build a system that could be borne on a drone and fly around and zero in on that. That was a project with Glenn Gerber connected a lot to his work with iguanas and the Caribbean and elsewhere, and we're now going to use it with rattlesnakes and the Biodiversity Reserve. And we're going to do that not just with aerial drones but also with the leged robots. So that's a plan to begin to understand whether we can use the leged robots to do that sort of thing. And there's always going to be a balance where certain applications would be better served by using the droness is the legged robots, but there are plenty of spaces where the drones aren't going to do the thing we need, and so this is an exciting new thing where you can have these essentially little robot dogs wandering around trying to track down the snakes and being able to do that kind of exhaustively in the space, wanting the fire roads and sitting down when they need to rest and charge up, and then going back on to duty to figure out where the snakes are so that we can get a really good map of the snake's activity in the space.

00:23:26
Speaker 1: Wow, that's amazing. And you had mentioned earlier that the deploying sometimes of audio tracking equipment or the cameras, just the human going into the space and doing that can be disruptive enough that perhaps changes or alters the behavior at least for a few days, if not longer, of the species in the environment. Therefore, the data you're collecting isn't actually accurate. Would these robots potentially be an opportunity to deploy these type of devices into the environment without a human going in and disturbing that space.

00:23:57
Speaker 2: Yeah.

00:23:57
Speaker 4: Absolutely. One of the more blue sky ideas for using these robots is to deploy one of our other systems, the Dencam system that goes to Smallbar to monitor maternal polar bears, and as it stands, in collaboration with Polar Bears International, we work very closely with them on the Dencam project. They fly out in the helicopter and then they land somewhere near where the bear is. They know where it is because of the GPS fix, and then they ski the last few kilometers in and then they have to you know, make sure they don't get too close, yeah, for the reasons for the bear's safety and for their savis, so on and so forth. But we imagine that something like that and this might be more like five six years out instead of humans having to deploy that robotic terrestrial robot, well, this quadrupedal terrestrial robot likely quadrupedal. There's other versions. There's six legged ones and things like that would just slowly wander across the landscape bearing that device and then over the course of probably weeks, get to the location, sit down and monitor the polar bears. So there's a science fiction aspect to that whole idea, and part of what we we have to do is sort of tease it apart and see whether there's some real potential there and it's really actually going to be as helpful as we hope it will be, or whether there's some massive gotcha that makes it kind of nonsense. But by having that blue sky idea, we can explore it, and we can find all these other places where there's a utility for this technology that's just about to become very prevalent and ubiquitous.

00:25:23
Speaker 3: Oh you know, And I really think it's worth noting some of the concepts of the work that we do here with wildlife. Here at the San Diego Zoo Safari Park or the San Diego Zoo, we learn new aspects of wildlife behavior and health because of the work we do. I mean, we're literally side by side with amazing wildlife and we share this information with our partners in those conservation hubs. But honestly, I've never really thought about the tech side of things and how we're applying all these technological advances I mean like robots, are you kidding? And then being able to share this information.

00:25:55
Speaker 2: Around the world too, It's just epic.

00:25:58
Speaker 3: And that leads me to wanting to ask you, dude, what does this mean to you to be part of this conservation technology to be at this level of conservation, this level of making sure that this world says in some form of ecological balance.

00:26:11
Speaker 2: What it means for me?

00:26:13
Speaker 4: Well, I mean I feel good about the work we're doing, and that's important to me. I think that's important to the people who work here in general. And as you mentioned, it's a lot of different kinds of people who are tackling this problem in a lot of different kinds of ways. And yeah, we the Conservation Technology Lab, facilitate that work by providing data that the ecologists and other sorts of scientists can do the sort of studies that they've done, but often faster or with more data than they otherwise could have done it.

00:26:42
Speaker 1: It is truly amazing work, and I really appreciate you spending time with us today and sharing everything you know with our listening audience. All this work you do, all the work your team does, it's truly amazing.

00:26:52
Speaker 2: Oh yeah, I definitely agree.

00:26:53
Speaker 4: Right.

00:26:53
Speaker 3: I mean, I think I learned a lot today. I really appreciate it. Ian, thank you so much.

00:26:59
Speaker 4: Yeah, it was a pleasure to y'all.

00:27:02
Speaker 2: Rick.

00:27:02
Speaker 3: You know, I can't believe I'm going to say this, because, like I've said before, I'm definitely not a tech person at Oh, but after listening to Ian, I am super stoked about everything tech can do for conservation.

00:27:15
Speaker 4: Now.

00:27:15
Speaker 1: I know exactly what you mean, Marco. I just love hearing Ian's passionate excitement for his work, knowing that he and his team are doing a lot for conservation now, and then knowing there's so much more new technology on the horizon.

00:27:28
Speaker 2: Oh yeah, and it.

00:27:28
Speaker 3: Really reminds me of what you know, we've been saying before San Diego Zoo Wildlife Alliance. It's like a city with every kind of job and career working for wildlife conservation, from being a mechanic to being a chef or a writer, wildlife care even social media.

00:27:45
Speaker 1: Yeah, it's true, and that brings me to yet another point. I want to make something I was reminded of as Ian was sharing his.

00:27:51
Speaker 2: Work with us.

00:27:52
Speaker 1: Now, I've had plenty of people ask me throughout my career how they can be a part of wildlife care or conservation if they aren't a biologist, or they've already gone through school and they have a profession to something else. And I think all of this talk about conservation technology really shows us that you can be a computer programmer or an engineer, or a roboticist or like you said, social media specialist or a chef or whatever and still be directly involved with saving wildlife.

00:28:13
Speaker 3: I mean, that's absolutely true, man, a one hundred percent degree, And I hope everyone listening understands that you can make a difference for wildlife no matter what you do.

00:28:23
Speaker 2: Oh so true.

00:28:24
Speaker 1: And you know, Marco, I realized this episode was supposed to have a main focus on our Asian Rainforest Hub, but I'm really glad and researching our work there that we found out about Ian and what the conservation technology team is doing and how all of that is going to benefit really all of our hubs.

00:28:38
Speaker 2: Well, I mean, I absolutely agree.

00:28:40
Speaker 3: I mean the work is expanding, if the Amazon, the Pacific Islands here in the Southwest, and of course the Asian Rainforest Hub. So you know, what do you think about taking some time to maybe focus a little bit more on some of that wildlife in that Asian rainforest hub.

00:28:54
Speaker 1: Well that sounds good to me.

00:28:55
Speaker 2: What are you thinking about? Well, you know International Tire Day is coming up this month.

00:28:59
Speaker 1: Oh well you heard them.

00:29:01
Speaker 2: Folks.

00:29:01
Speaker 1: Be sure to subscribe and tune into our next episode, in which Mark and I talk about everything whiskers and stripes for International Tiger Day.

00:29:11
Speaker 2: At Proximo. I'm Marco Away, then I'm Rich Schwartz.

00:29:14
Speaker 1: Thanks for listening. For more information about the San Diego Zoo and San Diego Zoo Safari Park, go to SDZWA dot org. Amazing Wildlife is a production of iHeartRadio's Ruby Studios. Our supervising producer is Nikia Swinton and our sound designer and editor is Sierra Spreen. For more shows from iHeartRadio, check out the iHeartRadio app, Apple Podcasts, or wherever you listen to your favorite shows.