1 00:00:00,600 --> 00:00:15,640 The good natured podcast comes to you from conservation optimism and its founding partners, Synchronicity, Earth and the University of Oxford. 2 00:00:15,640 --> 00:00:21,730 Welcome to Good-Natured, a podcast where you can join us for uplifting chats that shine a light on conservation challenges. 3 00:00:21,730 --> 00:00:25,620 In each episode, we interview an inspiring conservationist. 4 00:00:25,620 --> 00:00:31,950 A fascinating guest come from many backgrounds, artists, scientists, activists and many more. 5 00:00:31,950 --> 00:00:39,910 I'm Sophia APHC student focussing on marine conservation. I love doing science and telling stories through film writing, improvised comedy. 6 00:00:39,910 --> 00:00:44,630 And now. And I'm Julia, a science communicator and journalist. 7 00:00:44,630 --> 00:00:54,780 I'm passionate about sharing what people are doing to make the world a better place. Hey, Sophia. 8 00:00:54,780 --> 00:01:01,560 Hi, Julia. Today, I'm really thrilled to let you know that we are having Meredith Palmer on the podcast. 9 00:01:01,560 --> 00:01:05,490 Meredith is a researcher at the Pringle Lab at Princeton University. 10 00:01:05,490 --> 00:01:09,270 And a lot of the research she does is about predator prey interaction. 11 00:01:09,270 --> 00:01:13,200 So she look at how predators change prey behaviour. 12 00:01:13,200 --> 00:01:22,200 She looks at stuff like demography, the impact of coexistence and ecosystem functioning and lots of different aspects of these interactions. 13 00:01:22,200 --> 00:01:25,560 Meredith is also an expert in citizen science. 14 00:01:25,560 --> 00:01:34,830 So basically working with people who don't have formal scientific training to conduct scientific research by processing photos from her camera traps, 15 00:01:34,830 --> 00:01:44,160 which she puts out in the Serengeti in order to record lots of exciting animals like zebras and lions and civets. 16 00:01:44,160 --> 00:01:51,600 And if you've never heard of citizen science, Meredith is going to give us a bit of an explanation of what it means. 17 00:01:51,600 --> 00:01:57,240 But what you need to know is that she has various projects that you can actually take part in. 18 00:01:57,240 --> 00:02:03,090 And so one of these projects is the Snapshot Serengeti. Another one is called Snapshot Safari. 19 00:02:03,090 --> 00:02:07,790 So if you tie these projects online, you'll find all the information and how you can join. 20 00:02:07,790 --> 00:02:12,840 And Meredith Walk is very interesting because it's this balance between doing a lot of tech. 21 00:02:12,840 --> 00:02:18,000 So, for example, making these cameras work and collaborating with partners in the tech sector such as Google, 22 00:02:18,000 --> 00:02:22,230 Deep Mind, Microsoft, Phreak Labs, Wildlands and others. 23 00:02:22,230 --> 00:02:26,640 But then the other thing that she does is actually empowering people and working with 24 00:02:26,640 --> 00:02:31,530 people who come from all sorts of backgrounds in order to do this conservation work. 25 00:02:31,530 --> 00:02:34,770 Meredith is already passionate about outreach and science communication. 26 00:02:34,770 --> 00:02:41,970 And she also cares a lot about growing the next generation of scientists, including women and other underrepresented groups. 27 00:02:41,970 --> 00:02:45,540 So she's going to tell us a little bit about why that is so important to her. 28 00:02:45,540 --> 00:02:49,950 And some of the work that she's doing to make sure that comes to the fore and happens. 29 00:02:49,950 --> 00:02:57,480 I can't wait to hear a little bit more about that aspect, because as a science communicator, I always love when scientists do outreach. 30 00:02:57,480 --> 00:03:03,000 And if you know us already, you know that conservation optimism, we believe that everyone can be a conservationist. 31 00:03:03,000 --> 00:03:14,300 So let's hear what Meredith has to say about all these different exciting projects. 32 00:03:14,300 --> 00:03:18,080 Hi, murda. Thank you so much for joining us today. 33 00:03:18,080 --> 00:03:21,680 We're really excited to hear about all the work that you're doing to help us. 34 00:03:21,680 --> 00:03:28,820 Question is quite a general one, really. Why do you think that it's good or important or useful for people who don't have 35 00:03:28,820 --> 00:03:33,190 formal scientific training to participate in scientific research to citizens? 36 00:03:33,190 --> 00:03:38,150 Science is, of course, a way for people. No matter what your background, no matter what your expertise, 37 00:03:38,150 --> 00:03:45,830 no matter what kind of training you've had to partake in real, meaningful, authentic science and research. 38 00:03:45,830 --> 00:03:49,580 So there's a whole bunch of scientists out there. We are. 39 00:03:49,580 --> 00:03:57,980 Many of us are completely inundated with data as part of the technological revolution, especially that's happened over the last decade. 40 00:03:57,980 --> 00:04:04,640 Researchers are now collecting far more data than know a single researcher or a single research team can process on our own. 41 00:04:04,640 --> 00:04:07,330 And so we often turn to the general public. 42 00:04:07,330 --> 00:04:16,700 We start crowdsourcing the processing of this data so that we can quickly go from a camera trap image or a satellite image or an audio recording. 43 00:04:16,700 --> 00:04:21,710 Turn nine to the numbers that we can crunch in order to do our research on our conservation. 44 00:04:21,710 --> 00:04:28,580 And a big component of this. This is science for me isn't just using citizen scientists as a machine. 45 00:04:28,580 --> 00:04:33,920 That is part of it. And it's a part we're extremely grateful for and excited about as scientists. 46 00:04:33,920 --> 00:04:42,170 But like you said, it's also a really cool way to bring people who might not have experience with science into science. 47 00:04:42,170 --> 00:04:47,710 And I think it's been particularly powerful during the lockdown because people 48 00:04:47,710 --> 00:04:50,540 were stuck at home and they were looking for something meaningful to do. 49 00:04:50,540 --> 00:04:56,120 And I know that we work quite closely with Penguin Watch and they have this citizen science part of it, 50 00:04:56,120 --> 00:05:00,440 where people can look at photos and say if they see penguins. 51 00:05:00,440 --> 00:05:06,600 And I know that it's really Budarin looked down because people really want it to be doing something definitely and in lockdown. 52 00:05:06,600 --> 00:05:10,880 You know, we're feeling listless. We can't travel. We can't go out and see things. 53 00:05:10,880 --> 00:05:17,270 We can't go into nature. Like you said, a lot of people might feel like they don't have a sense of purpose. 54 00:05:17,270 --> 00:05:21,740 And you can essentially go on a I call an armchair safari. 55 00:05:21,740 --> 00:05:27,440 Like all you need to participate in my kind of citizen science is you a computer or a phone. 56 00:05:27,440 --> 00:05:36,290 You go to our website, you download our app, and all of a sudden you're in Africa looking at cheetahs and lions from the comfort of your own home. 57 00:05:36,290 --> 00:05:40,370 And at the same time, you are doing incredibly important work, 58 00:05:40,370 --> 00:05:49,130 work that allows us to do kinds of research and conservation that simply is not possible without the help of citizen scientists. 59 00:05:49,130 --> 00:05:52,950 It sounds like there are just so many opportunities, like you say, 60 00:05:52,950 --> 00:06:00,050 both for the people who are participating and being citizen scientists, but then also for the researchers. 61 00:06:00,050 --> 00:06:00,800 So I'm curious, 62 00:06:00,800 --> 00:06:10,370 what made you decide to use citizen science in your research and what was some of the challenges that you found in making that project work? 63 00:06:10,370 --> 00:06:19,640 So our projects, I think, are very exciting because we were sort of at the forefront of using online citizen science to help process data. 64 00:06:19,640 --> 00:06:23,360 So I work with a project called Snapshot Serengeti, 65 00:06:23,360 --> 00:06:33,320 and we started out over 10 years ago with a large scale camera trapping project set up in the middle of Serengeti National Park in Tanzania. 66 00:06:33,320 --> 00:06:36,830 And it's this beautiful, pristine ecosystem. 67 00:06:36,830 --> 00:06:43,220 So we go out and we have 200 camera traps and they're triggered by heat and motion to take pictures of passing wildlife. 68 00:06:43,220 --> 00:06:47,750 So they're running 24 hours a day taking pictures of anything that comes in front of them. 69 00:06:47,750 --> 00:06:52,880 Like I said, we've had this one camera trap grid out in the field for 10 years now. 70 00:06:52,880 --> 00:06:56,990 That's tens of millions of photographs. 71 00:06:56,990 --> 00:07:05,630 But, you know, we're quite literally drowning in wildlife pictures, which is amazing because it gives us this really fine scale, 72 00:07:05,630 --> 00:07:13,010 high resolution look at what entire communities are of animals are doing in this ecosystem. 73 00:07:13,010 --> 00:07:17,440 But we can't analyse a photograph directly. 74 00:07:17,440 --> 00:07:22,790 And so I once calculated I did this calculation if I was to take a year's worth 75 00:07:22,790 --> 00:07:27,770 of data collected from this one camera tracker in Serengeti National Park. 76 00:07:27,770 --> 00:07:32,720 And I had to sit down and process all of that data myself and say it takes, you know, 77 00:07:32,720 --> 00:07:36,920 like 10 to 20 seconds for me to look at each picture and write down what's in it. 78 00:07:36,920 --> 00:07:40,630 And if I'm working eight hours a day and if I'm working seven days a week. 79 00:07:40,630 --> 00:07:45,590 So no weekends, 52 weeks a year. So no sick days, no holidays, no time off. 80 00:07:45,590 --> 00:07:51,170 It would take me something like seven or eight years to process a single year's worth of camera trap data. 81 00:07:51,170 --> 00:07:56,690 So many of our research teams are just, you know, a couple of scientists, a handful of researchers. 82 00:07:56,690 --> 00:08:00,440 We don't have the capacity to do that ourselves. 83 00:08:00,440 --> 00:08:09,950 And another very important driving factor of this kind of data collection is that we use this data for conservation. 84 00:08:09,950 --> 00:08:17,700 We use this data to evaluate. What happens? We put up a fence or increase our anti poaching or reintroduce a predator. 85 00:08:17,700 --> 00:08:23,880 How does the system change? What do we need to do to protect this community? And if I'm not getting that data back. 86 00:08:23,880 --> 00:08:30,780 You know, until several years later, I can't react and respond and adapt and to do conservation effectively. 87 00:08:30,780 --> 00:08:35,040 And so we turned to a platform called the Zooniverse. 88 00:08:35,040 --> 00:08:41,460 And the Zooniverse had just done this really amazing thing with images from the Hubble Space Telescope. 89 00:08:41,460 --> 00:08:47,670 So they had a project called Galaxy Zoo where they took images from the Hubble Space Telescope. 90 00:08:47,670 --> 00:08:50,730 And there's some things that the human brain just does really, really well. 91 00:08:50,730 --> 00:08:57,660 So image pattern recognition is something that, you know, our brains have evolved for millennia to be amazingly good at. 92 00:08:57,660 --> 00:09:01,080 But computers and things like that aren't super good at that yet. 93 00:09:01,080 --> 00:09:09,360 So Galaxy Zoo took all these images from the Hubble Space Telescope and put them on line and asked people to just identify galaxies. 94 00:09:09,360 --> 00:09:17,400 And this was really the first online citizen science project where we were crowdsourcing image data to people on the Internet. 95 00:09:17,400 --> 00:09:23,670 And it was a massively huge success. We created this project called Snapshot Serengeti. 96 00:09:23,670 --> 00:09:31,890 We put a year and a half's worth of image data online and ecorp processed in three days. 97 00:09:31,890 --> 00:09:36,720 So now we have camera tracking grids, not just in the Serengeti. But we have them in Mozambique. 98 00:09:36,720 --> 00:09:40,530 We have them in South Africa. We get them in some way. We have them in Botswana. 99 00:09:40,530 --> 00:09:48,480 We have them in North America. Been able to scale up this effort, which is producing exponentially more camera trap pictures. 100 00:09:48,480 --> 00:09:52,590 But again, with the help of citizen scientists, 101 00:09:52,590 --> 00:10:01,950 we're actually able to process those images at a pace rapid enough for us to to get used that data in research and conservation. 102 00:10:01,950 --> 00:10:09,300 Are you able to share with us some of the exciting findings that you've unveiled in these different locations through citizen science? 103 00:10:09,300 --> 00:10:14,040 So our Serengeti project, we've had a lot of really great papers come out. 104 00:10:14,040 --> 00:10:20,340 So as a as a scientist, like the scientist, part of me studies predator prey interactions. 105 00:10:20,340 --> 00:10:30,300 And so we've been able to do some really fascinating work looking at how prey animals navigate a landscape in order to avoid predators. 106 00:10:30,300 --> 00:10:36,750 And this may sound like a very simple question, but if you think about it, we're studying an incredibly complex system. 107 00:10:36,750 --> 00:10:41,790 And without the camera traps, I wouldn't be able to study where hundreds of zebra go. 108 00:10:41,790 --> 00:10:44,760 I wouldn't be able to study what lions do at night. 109 00:10:44,760 --> 00:10:50,040 For the most part, researchers don't go out into the Serengeti at night and would be eaten by hippopotamus. 110 00:10:50,040 --> 00:10:57,990 You know, like we don't know what goes on at night. Camera traps have revealed a lot of nocturnal behaviour that was previously unknown. 111 00:10:57,990 --> 00:11:04,170 We had a study that was actually this is my favourite study that's come out of our Serengeti project. 112 00:11:04,170 --> 00:11:07,290 And this was a discovery made by citizen scientists. 113 00:11:07,290 --> 00:11:14,670 So we had some citizen scientists flagged this really, really weird behaviour in some of our nocturnal camera traps data. 114 00:11:14,670 --> 00:11:22,620 They were looking at pictures, giraffe at night and noticing that there were these weird things hanging out on the bottom of the giraffe. 115 00:11:22,620 --> 00:11:30,180 We discovered that there's this species of bird that rather than going to go nest in a tree like a normal bird would do at night, 116 00:11:30,180 --> 00:11:34,620 they would instead roost in the armpits of giraffe. 117 00:11:34,620 --> 00:11:39,150 Would have thought to even look for. No one goes out into the field at night. 118 00:11:39,150 --> 00:11:43,790 The most dangerous time, you know, poking around under Drath, looking for bird. 119 00:11:43,790 --> 00:11:49,280 That sounds amazing, so many different projects and so many interesting species as well. 120 00:11:49,280 --> 00:11:52,820 That must be really interesting for all the different citizen scientists to do that. 121 00:11:52,820 --> 00:11:56,210 It was really important for you to show that everyone can be a scientist. 122 00:11:56,210 --> 00:12:03,860 And I've seen on your website that your heavily invested in growing the next generation of women and underrepresented groups in STEM. 123 00:12:03,860 --> 00:12:08,870 Could you tell us a bit more about the aspects of your work and why it's important to you? 124 00:12:08,870 --> 00:12:16,650 That's such a great question. Thank you for highlighting that, because it's something that's very important to me and my philosophy of doing science. 125 00:12:16,650 --> 00:12:23,280 I'm a woman. I have purple hair and tattoos, and I am a doctor and I am a scientist and I do conservation. 126 00:12:23,280 --> 00:12:30,300 And if I can be a scientist, you can be a scientist. And I think there's really two or three ways that I work. 127 00:12:30,300 --> 00:12:36,600 Two, to grow the next generation of scientists. We do a lot of work in Africa. 128 00:12:36,600 --> 00:12:42,330 So I'm incredibly privileged and grateful to be able to go to Africa and conduct 129 00:12:42,330 --> 00:12:48,150 research and help support African scientists in the incredible work that they're doing. 130 00:12:48,150 --> 00:12:56,460 And so some of what I do when I'm in the field is not just running around checking camera traps and changing batteries and getting SD cards, 131 00:12:56,460 --> 00:13:02,340 but working with local scientists, working with local students, doing training exercises and workshops, 132 00:13:02,340 --> 00:13:08,730 trying to make sure that the continuity of these research and conservation projects is in good hands. 133 00:13:08,730 --> 00:13:13,710 These people on the ground are the ones. This is their wildlife. It's their backyard. 134 00:13:13,710 --> 00:13:19,770 They're deal with the causes and consequences of conservation and for conservation efforts to work. 135 00:13:19,770 --> 00:13:27,840 I think we need to do a much better job. We may be speaking to all of the Western scientists out there of making conservation 136 00:13:27,840 --> 00:13:32,130 a collaborative effort so that capacity building is incredibly important to me. 137 00:13:32,130 --> 00:13:39,810 And it's ultimately our goal to hand over the ownership and the running of all of these camera trapping grids to local scientists, 138 00:13:39,810 --> 00:13:45,180 because they're the ones who need this data to make change in their ecosystems. 139 00:13:45,180 --> 00:13:53,160 As a woman in science, I'm very passionate both about bringing more women and more diverse voices into science 140 00:13:53,160 --> 00:13:58,260 and also making science a supportive place for those people once they get here. 141 00:13:58,260 --> 00:14:05,960 I don't know about either of your experiences as women in science, but it's not as easy as it should be. 142 00:14:05,960 --> 00:14:15,420 I'm not saying that it should be easy, but I think there are a lot of barriers faced by women and other minority groups in STEM. 143 00:14:15,420 --> 00:14:19,650 I mean, honestly, like, I'd never had a dream to be anything other than a scientist, 144 00:14:19,650 --> 00:14:25,290 but I couldn't have done it without the support of other women in STEM. 145 00:14:25,290 --> 00:14:32,910 And I think it's really important for us to build each other up as women, as in STEM and support each other. 146 00:14:32,910 --> 00:14:39,840 And as I mentor a number of women, graduate students, undergraduate students, 147 00:14:39,840 --> 00:14:47,190 I think we need to show the world essentially that women are an important and integral part of science. 148 00:14:47,190 --> 00:14:56,880 So it's not just women doing science, but also women talking about science and speaking to the public and being a face for the work that we do. 149 00:14:56,880 --> 00:15:05,250 The citizen science platform is not only a way for me to show other women and girls that they too can be a scientist just like me. 150 00:15:05,250 --> 00:15:09,480 But it's also a good way for people who might not have the opportunity or the 151 00:15:09,480 --> 00:15:16,170 privilege to engage in scientific experiences to get some science experiences. 152 00:15:16,170 --> 00:15:17,810 I think that makes a lot of sense. 153 00:15:17,810 --> 00:15:26,050 I mean, I'm glad you raise these issues about being inclusive and about creating opportunities for science to be available, 154 00:15:26,050 --> 00:15:32,000 something that's a lot of different people can participate in. What makes you optimistic about the future of nature? 155 00:15:32,000 --> 00:15:36,630 Honestly, it's the passion of the citizen scientists. 156 00:15:36,630 --> 00:15:47,400 I feel that when I was doing strict ecology in the field, watching forests get chopped down and my study animals go extinct by incredibly depressing, 157 00:15:47,400 --> 00:15:52,800 it's hard and it wears you down and it's hard to find those little sparks of optimism. 158 00:15:52,800 --> 00:15:58,530 And one thing about working with citizen scientists is that they have that optimism. 159 00:15:58,530 --> 00:16:04,550 They are so excited and so keen. These are people who are dedicating their own time. 160 00:16:04,550 --> 00:16:06,810 You know, they could be doing anything with that. 161 00:16:06,810 --> 00:16:14,760 You know, half hour that they spend looking at your camera trap photos and instead they're doing conservation work freely of their own incentive. 162 00:16:14,760 --> 00:16:22,050 And I get to interact with those people. I get to talk to them on social media and through our message boards and in person. 163 00:16:22,050 --> 00:16:29,070 And they are just so enthusiastic and so keen and so passionate about what they're doing and what we're doing. 164 00:16:29,070 --> 00:16:33,240 And it just brings you back to life as a conservation scientist, 165 00:16:33,240 --> 00:16:41,280 seeing how many people out there actually do have hope and are willing to take it into their own hands to try and make a difference. 166 00:16:41,280 --> 00:16:44,860 It is amazing. See how many people are doing it. 167 00:16:44,860 --> 00:16:50,340 I've been on Zooniverse and there's lots and lots and lots of projects and they all find volunteers to do them. 168 00:16:50,340 --> 00:16:55,820 So it must be this incredible number of people overall doing citizen science of some sort. 169 00:16:55,820 --> 00:17:00,920 It's it's really amazing. Another wonderful thing about citizen science and something for everyone. 170 00:17:00,920 --> 00:17:08,240 There is projects where you can transcribe civil war diaries or old ships logs or where you can helpful. 171 00:17:08,240 --> 00:17:14,550 Genes. Or figure out how malaria works. So much going on, it's really impressive. 172 00:17:14,550 --> 00:17:17,040 And I think we've got one more question for you. 173 00:17:17,040 --> 00:17:21,510 I have a feeling it might be a difficult one, because you've already mentioned lots of different species. 174 00:17:21,510 --> 00:17:25,170 But if you could make a case for only one species to save. 175 00:17:25,170 --> 00:17:28,890 What would it be and why? Oh, I've been dreading this question. 176 00:17:28,890 --> 00:17:35,070 To be honest, it's such a different way of thinking from how I approach conservation. 177 00:17:35,070 --> 00:17:39,300 I've been privileged to work with many fascinating animals. 178 00:17:39,300 --> 00:17:44,160 I've worked with everything from fish and frogs, monkeys and lions. 179 00:17:44,160 --> 00:17:49,500 But really, when I'm doing conservation, I'm thinking about wildlife communities. 180 00:17:49,500 --> 00:17:55,150 I'm interested in all of the relationships between species. And this is such a cop out answer. 181 00:17:55,150 --> 00:17:59,370 But it doesn't really matter to me if the predator is a lion or a tiger. 182 00:17:59,370 --> 00:18:05,160 I'm just interested in how that big cat interacts with whatever its lunges. 183 00:18:05,160 --> 00:18:08,970 I think that's fair enough. We will accept the answer. Wonderful. 184 00:18:08,970 --> 00:18:12,900 Thank you so much. It is such a pleasure to talk to you. No. Thank you so much. 185 00:18:12,900 --> 00:18:17,490 And actually, we've got a very nice voice. Notes from Sue show. 186 00:18:17,490 --> 00:18:24,480 He's eight and Ruben, who is 10, and they've been participating in the Wild Time gringo's for about half a year. 187 00:18:24,480 --> 00:18:27,750 Take a listen to what they had to say about it. Hi. 188 00:18:27,750 --> 00:18:37,490 I like being a certain time based on wall cam Glasgow set because we get to learn all about animals and identify them. 189 00:18:37,490 --> 00:18:42,900 Catch my breath. And I've also seen since scientists. 190 00:18:42,900 --> 00:18:47,170 And the reason why I really like you is because it gives us something to do well. 191 00:18:47,170 --> 00:18:53,830 Well, we're in the Functionalism. Me and my family. I love identifying animals for wild Cambourne goes in. 192 00:18:53,830 --> 00:19:03,370 We feel like it brings us together. Such an interesting answer to that last question. 193 00:19:03,370 --> 00:19:07,510 I've really been thinking about whether we can change that question or update it in some 194 00:19:07,510 --> 00:19:12,550 way to be more inclusive of the different types of conservation that people are doing. 195 00:19:12,550 --> 00:19:19,060 For sure. I think we've had various people now struggling or saying that they think they might be cheating on that question. 196 00:19:19,060 --> 00:19:26,770 So I think it might be time for an update. So if you have any suggestions on how we could update this specific question, please reach out to us. 197 00:19:26,770 --> 00:19:32,950 We'd love to hear from you. And you can find us at conservation optimism on Twitter, Instagram and Facebook. 198 00:19:32,950 --> 00:19:38,730 We'd love to hear from you. I felt like Merideth raised such a range of interesting points. 199 00:19:38,730 --> 00:19:45,400 A lot of them around inclusive city and conservation and just thinking about how different people can participate in the 200 00:19:45,400 --> 00:19:52,420 scientific process and how citizen science can allow people to do that potentially when they wouldn't have been able to before. 201 00:19:52,420 --> 00:19:56,800 And I think it's also really rewarding, actually, for people who take part in citizen science, 202 00:19:56,800 --> 00:20:05,020 because you can make really interesting discoveries like the one that she mentioned about these bird living under giraffes, armpits. 203 00:20:05,020 --> 00:20:07,330 It's things that you might not imagine. 204 00:20:07,330 --> 00:20:13,720 And and yet your, you know, annual so far looking at these photos and being like there's something a bit odd here. 205 00:20:13,720 --> 00:20:18,010 The other thing to think about is that amateur science used to be much more of a thing. 206 00:20:18,010 --> 00:20:23,410 You definitely didn't need to have scientific training in order to collect data that's relevant. 207 00:20:23,410 --> 00:20:29,500 I mean, for example, that was Henry David Thoreau, who was a writer who lived on Walden Pond. 208 00:20:29,500 --> 00:20:34,420 And I remember going to Walden Pond and actually then reading some top scientific 209 00:20:34,420 --> 00:20:39,550 papers where they had taken all of his notes about when things flowered. 210 00:20:39,550 --> 00:20:44,260 And now that's been used to understand changes in phonology. 211 00:20:44,260 --> 00:20:51,100 So the way that plants are responding to different seasons and cues potentially in response to climate change. 212 00:20:51,100 --> 00:20:53,230 And he didn't have scientific training. 213 00:20:53,230 --> 00:21:01,450 And then also most women in history who were interested in science or wanted to do science didn't actually have access to scientific training. 214 00:21:01,450 --> 00:21:07,030 So people who ended up contributing a lot to science, for example, Mary Anning, 215 00:21:07,030 --> 00:21:12,040 who collected a lot of amazing fossils, she collected the first Polizia saw. 216 00:21:12,040 --> 00:21:17,920 She had very little education at all at the moment, like a normal kind of day and age. 217 00:21:17,920 --> 00:21:23,590 Science is seen as something that you do. Having had like a very strict educational path. 218 00:21:23,590 --> 00:21:26,110 But that hasn't always been the case. And actually, 219 00:21:26,110 --> 00:21:32,800 a lot of really amazing discoveries and specimens and data have come from people who didn't have that 220 00:21:32,800 --> 00:21:37,060 sort of training and who were just interested in the natural world and the things that they were seeing. 221 00:21:37,060 --> 00:21:41,210 And they went out and just collected that. That's amazing. 222 00:21:41,210 --> 00:21:49,660 And I think that's one one aspect of citizen science that I really love about how it's empowering everyone to just be a conservationist. 223 00:21:49,660 --> 00:21:55,210 And we were giving a tour of the Natural History Museum at Oxford recently. 224 00:21:55,210 --> 00:21:58,570 So conservation optimism tool, he can fight it on YouTube if you want. 225 00:21:58,570 --> 00:22:05,110 And as part of this tool, we were showing a specimen that they have that has been collected by a little girl who was, 226 00:22:05,110 --> 00:22:10,660 I think, like less than 10 years old. And just on that species that hadn't been recorded in Oxfordshire for absolutely 227 00:22:10,660 --> 00:22:15,100 ages and just brought it to the museum with her parents to see what what it was. 228 00:22:15,100 --> 00:22:22,840 And then it was just very surprising. So, as you said, these discoveries as well by people who are not trained scientists keep happening. 229 00:22:22,840 --> 00:22:29,680 And I think that link to something that we've not yet mentioned in this episode. But citizen science is not just an online thing. 230 00:22:29,680 --> 00:22:33,970 So in this case, the project that we talked about, our online citizen science. 231 00:22:33,970 --> 00:22:38,800 But you can also be an on the ground citizen scientists, for example, 232 00:22:38,800 --> 00:22:47,260 every year you can look at in your garden and say the species that you've recorded or there are some campaigns to look at butterfly species. 233 00:22:47,260 --> 00:22:50,650 And, you know, you can use apps or sometimes you just go on a Web site. 234 00:22:50,650 --> 00:22:57,070 And it's another really good way if you want to, you know, get muddy and have a bit more of the on the ground experience. 235 00:22:57,070 --> 00:23:01,510 That's another way as well that you can get involved with citizen science. Absolutely. 236 00:23:01,510 --> 00:23:05,080 I have walked on citizen science projects in the past, 237 00:23:05,080 --> 00:23:11,740 and it's pretty cool to see how people can be so passionate and just kind of like show up and get involved in data collection. 238 00:23:11,740 --> 00:23:17,830 It's interesting, though, to think about how in this pandemic, I think it's just changed so many of the ways that we do things. 239 00:23:17,830 --> 00:23:23,470 And citizen science and in particular this project seems to just have been so well poised for 240 00:23:23,470 --> 00:23:29,950 this moment to take advantage of the way that people are sitting at home and can contribute. 241 00:23:29,950 --> 00:23:33,340 Hundred percent. We've been told that the project, 242 00:23:33,340 --> 00:23:39,400 the number of people doing citizen science for the specific projects that made this work on tripled during the lockdown. 243 00:23:39,400 --> 00:23:43,750 So we've seen these massive booms of people using it more and more. 244 00:23:43,750 --> 00:23:50,740 And I think it's just because, as you said, we are discovering all these different ways to do things virtual is becoming more common. 245 00:23:50,740 --> 00:23:58,900 And so now you're like, well, if I am to sit on the sofa because I can't really go out, then I might as well look at photos of. 246 00:23:58,900 --> 00:24:03,520 Animals and plants and just be useful for these research projects. 247 00:24:03,520 --> 00:24:13,990 I think it's wonderful that people without leaving their house at all can actually have a positive impact in terms of conservation research. 248 00:24:13,990 --> 00:24:19,810 I mean, I think in a moment when so many of us have felt so frustrated and maybe like hemmed in in a 249 00:24:19,810 --> 00:24:24,430 bunch of ways to actually still be able to have these impacts and to do science is so cool. 250 00:24:24,430 --> 00:24:27,910 Well, on that note, Sophia, I think that's it for this episode. 251 00:24:27,910 --> 00:24:33,790 And if you've enjoyed this episode, don't forget to subscribe to the podcast on Apple podcast and Spotify. 252 00:24:33,790 --> 00:24:40,930 This episode was funded by an E. S r c impact exploration account Ground through the Investor of Oxford. 253 00:24:40,930 --> 00:24:58,723 Original theme music composed and produced by Matthew.