1 00:00:06,247 --> 00:00:10,345 Pollinator decline is a grand challenge in the modern world. 2 00:00:10,345 --> 00:00:13,426 Of the 200,000 species of pollinators, 3 00:00:13,426 --> 00:00:16,187 honeybees are the most well-understood, 4 00:00:16,187 --> 00:00:18,305 partly because of our long history with them, 5 00:00:18,305 --> 00:00:20,417 dating back 8,000 years ago 6 00:00:20,421 --> 00:00:23,760 to our cave drawings in what's now modern-day Spain, 7 00:00:23,760 --> 00:00:27,629 and yet, we know that this indicator species is dying off. 8 00:00:27,629 --> 00:00:32,879 Last year alone, we lost 40% of all beehives in the United States, 9 00:00:32,879 --> 00:00:35,690 and that number is even higher in areas with harsh winters, 10 00:00:35,690 --> 00:00:37,121 like here in Massachusetts, 11 00:00:37,121 --> 00:00:41,487 where we lost 47% of beehives in one year alone. 12 00:00:41,504 --> 00:00:45,116 Can you imagine if we lost half of our people last year? 13 00:00:45,136 --> 00:00:48,035 And if those were the food-producing people? 14 00:00:48,828 --> 00:00:51,572 And I predict that in 10 years, 15 00:00:51,922 --> 00:00:53,956 we will lose our bees. 16 00:00:53,956 --> 00:00:58,345 If not for the work of beekeepers replacing these dead beehives, 17 00:00:58,345 --> 00:01:01,281 we would be without foods that we rely upon: 18 00:01:01,281 --> 00:01:05,430 Fruits, vegetables, crunchy almonds and nuts, 19 00:01:05,430 --> 00:01:08,780 tart apples, sour lemons, 20 00:01:08,780 --> 00:01:11,817 even the food that our cattle relies upon to eat - 21 00:01:11,817 --> 00:01:14,207 hay and alfalfa, gone - 22 00:01:14,207 --> 00:01:17,921 causing global hunger, economic collapse, 23 00:01:17,921 --> 00:01:20,428 a total moral crisis across Earth. 24 00:01:20,428 --> 00:01:23,986 And it's going to take an effort from every single one of you here 25 00:01:23,986 --> 00:01:26,868 to become what we call "citizen scientists," 26 00:01:26,868 --> 00:01:30,048 to activate all the things that you're probably already doing - 27 00:01:30,048 --> 00:01:32,891 Yes, planting flowers, getting beehives, 28 00:01:32,891 --> 00:01:37,055 making bee hotels, habitats for those lesser known pollinators, 29 00:01:37,055 --> 00:01:39,349 but beyond that, going a step further, 30 00:01:39,349 --> 00:01:42,597 and adding a data collection element to it. 31 00:01:42,597 --> 00:01:45,255 So, I'm going to show you how to do this here today, 32 00:01:45,255 --> 00:01:49,050 and I'm going to show you how when my team shifted our perspective 33 00:01:49,050 --> 00:01:51,037 away from bees that were dying 34 00:01:51,037 --> 00:01:54,677 and toward maps and looking at hot spots for bee health, 35 00:01:54,677 --> 00:01:56,986 and shifting our perspective away from honey 36 00:01:56,986 --> 00:01:59,846 as just a sweet, sticky delicious food, 37 00:01:59,846 --> 00:02:03,832 but to a source of information that contains a blueprint 38 00:02:03,832 --> 00:02:09,338 for healthy habitat suspended in time in the only food that doesn't go bad. 39 00:02:09,338 --> 00:02:11,185 That's where we find the hope 40 00:02:11,185 --> 00:02:13,543 and that's what we'll do together today. 41 00:02:13,543 --> 00:02:16,237 Now, I first started keeping bees here in Cape Cod, 42 00:02:16,237 --> 00:02:19,576 right after I finished my doctorate in honeybee immunology. 43 00:02:19,576 --> 00:02:21,177 (Laughter) 44 00:02:21,177 --> 00:02:22,689 It was - 45 00:02:22,689 --> 00:02:25,327 (Applause) 46 00:02:26,668 --> 00:02:30,372 So, imagine getting such a degree in a good economy - 47 00:02:30,372 --> 00:02:31,466 (Laughter) 48 00:02:31,466 --> 00:02:35,353 And it was 2009 - the great recession. 49 00:02:36,193 --> 00:02:37,703 And I was onto something - 50 00:02:37,703 --> 00:02:40,961 I knew that I could find out how to improve bee health. 51 00:02:40,961 --> 00:02:44,139 And that's when word started to spread and I came here to Cape Cod 52 00:02:44,139 --> 00:02:46,299 and I realized that this deep connection 53 00:02:46,299 --> 00:02:50,391 that people have to the land here is so true and long-standing - 54 00:02:50,392 --> 00:02:54,066 probably because there's so little of the land that is here, 55 00:02:54,066 --> 00:02:55,627 we're connected to it. 56 00:02:55,627 --> 00:02:58,795 And so the community on Cape Cod, here in Provincetown, 57 00:02:58,795 --> 00:03:00,556 was right for citizen science, 58 00:03:00,556 --> 00:03:03,506 people looking for ways to get involved and to help. 59 00:03:03,516 --> 00:03:05,794 And so, we met with people in coffee shops. 60 00:03:05,794 --> 00:03:09,304 A wonderful woman named Natalie got eight beehives at her home in Truro, 61 00:03:09,304 --> 00:03:11,356 and she introduced us to her friend Valerie, 62 00:03:11,356 --> 00:03:16,766 who let us set up 60 beehives at an abandoned tennis court on her property. 63 00:03:17,676 --> 00:03:20,706 And so we started testing vaccines for bees. 64 00:03:20,706 --> 00:03:23,351 We were starting to look at probiotics. 65 00:03:23,351 --> 00:03:25,176 We called it bee yogurt. 66 00:03:25,176 --> 00:03:27,596 Ways to make bees healthier. 67 00:03:27,596 --> 00:03:30,576 And our citizen science project started to take off. 68 00:03:30,576 --> 00:03:32,974 Word started to spread and people started to think, 69 00:03:32,974 --> 00:03:35,864 "Wow, I can get bees of my own and their little data factory, 70 00:03:35,864 --> 00:03:37,274 that's great!" 71 00:03:37,474 --> 00:03:42,309 Meanwhile, back in my apartment here, I was a bit nervous about my landlord. 72 00:03:42,309 --> 00:03:44,509 I figured I should tell him what we were doing. 73 00:03:44,509 --> 00:03:45,639 (Laughter) 74 00:03:45,639 --> 00:03:46,769 I was terrified. 75 00:03:46,769 --> 00:03:49,297 I really thought I was going to get an eviction notice 76 00:03:49,297 --> 00:03:51,597 which really was the last thing we needed, right? 77 00:03:51,597 --> 00:03:53,737 I must have caught him on a good day, though, 78 00:03:53,737 --> 00:03:56,687 because when I told him what we were doing and how we started 79 00:03:56,687 --> 00:03:58,758 our non-profit urban beekeeping laboratory, 80 00:03:58,758 --> 00:04:02,578 he said, "That's great! Let's get a beehive in the back alley." 81 00:04:02,578 --> 00:04:04,046 I was shocked! 82 00:04:04,046 --> 00:04:05,716 I was completely surprised. 83 00:04:05,716 --> 00:04:07,961 I mean, instead of getting an eviction notice, 84 00:04:07,961 --> 00:04:09,626 we got another data point. 85 00:04:09,645 --> 00:04:12,075 And in the back alley of this image, 86 00:04:12,075 --> 00:04:14,524 what you see here, this hidden beehive, 87 00:04:14,524 --> 00:04:17,163 that beehive produced more honey that first year 88 00:04:17,163 --> 00:04:20,669 than we had ever experienced in any beehive we had managed. 89 00:04:20,669 --> 00:04:23,689 Over a hundred pounds of honey that one year alone. 90 00:04:23,689 --> 00:04:25,309 We didn't know what to do with it. 91 00:04:25,309 --> 00:04:28,547 I mean, we were filling up pickle jars with the stuff. 92 00:04:28,547 --> 00:04:31,037 And since honey is the only food that never goes bad, 93 00:04:31,037 --> 00:04:33,060 the residents and tenants in the community 94 00:04:33,060 --> 00:04:35,023 are still enjoying that honey today. 95 00:04:35,023 --> 00:04:37,926 It shifted our research perspective forever. 96 00:04:37,926 --> 00:04:40,050 It changed our research question away 97 00:04:40,050 --> 00:04:42,529 from how do we save the dead and dying bees 98 00:04:42,529 --> 00:04:44,897 to where are bees doing best. 99 00:04:44,897 --> 00:04:47,816 And we started to be able to put maps together, 100 00:04:47,816 --> 00:04:50,236 looking at all of these citizen science beehives 101 00:04:50,236 --> 00:04:54,604 from people who had beehives at home decks, gardens, business rooftops. 102 00:04:54,634 --> 00:04:56,397 And we started to engage the public, 103 00:04:56,397 --> 00:04:58,887 and the more people who got these little data points, 104 00:04:58,887 --> 00:05:00,577 the more accurate our maps became. 105 00:05:00,617 --> 00:05:03,809 And so when you're sitting here thinking, "How can I get involved?", 106 00:05:03,809 --> 00:05:06,188 you might think about the story of my friend Fred, 107 00:05:06,188 --> 00:05:08,108 who's a commercial real estate developer 108 00:05:08,108 --> 00:05:10,073 and he was thinking the same thing. 109 00:05:10,073 --> 00:05:14,008 But even if you own a business, you can be a citizen scientist too. 110 00:05:14,008 --> 00:05:16,858 He was at a meeting thinking about what he could do 111 00:05:16,858 --> 00:05:20,018 for tenant relations and sustainability at scale. 112 00:05:20,018 --> 00:05:21,878 And while he was having a tea break, 113 00:05:21,878 --> 00:05:24,775 he put honey into his tea and noticed, on the honey jar, 114 00:05:24,775 --> 00:05:28,825 a message about corporate sustainability from the host company of that meeting 115 00:05:28,825 --> 00:05:30,712 and it sparked an idea. 116 00:05:30,712 --> 00:05:35,486 He came back to his office, an email, a phone call later, and boom! 117 00:05:35,486 --> 00:05:38,018 We went national together. 118 00:05:38,018 --> 00:05:41,168 We put dozens of beehives on the rooftops of their skyscrapers 119 00:05:41,168 --> 00:05:44,066 across nine cities nationwide. 120 00:05:44,626 --> 00:05:46,408 Nine years later - 121 00:05:46,408 --> 00:05:48,543 (Applause) 122 00:05:49,639 --> 00:05:54,627 Nine years later we have raised over a million dollars for bee research. 123 00:05:54,627 --> 00:05:59,246 We have a thousand beehives as little data points across the country, 124 00:05:59,246 --> 00:06:00,936 18 states and counting, 125 00:06:00,936 --> 00:06:05,406 where we have created paying jobs for local beekeepers - 65 of them - 126 00:06:05,406 --> 00:06:07,945 to manage beehives in their own communities, 127 00:06:07,945 --> 00:06:12,865 to connect with people, everyday people who are now data points, 128 00:06:12,865 --> 00:06:15,306 together, making a difference. 129 00:06:15,306 --> 00:06:17,996 So, in order to explain what's actually been saving bees, 130 00:06:17,996 --> 00:06:19,166 where they're thriving, 131 00:06:19,166 --> 00:06:21,686 I need to first tell you what's been killing them. 132 00:06:21,686 --> 00:06:24,900 The top three killers of bees are agricultural chemicals, 133 00:06:24,900 --> 00:06:27,685 such as pesticides, herbicides, fungicides; 134 00:06:27,685 --> 00:06:30,118 diseases of bees, of which there are many; 135 00:06:30,118 --> 00:06:31,717 and habitat loss. 136 00:06:31,717 --> 00:06:33,742 So, what we did is we looked at our maps 137 00:06:33,742 --> 00:06:36,328 and we identified areas where bees were thriving, 138 00:06:36,328 --> 00:06:38,868 and this was mostly in cities, we found. 139 00:06:38,868 --> 00:06:41,850 Data are now showing that urban beehives produce more honey 140 00:06:41,850 --> 00:06:44,440 than rural beehives and suburban beehives. 141 00:06:44,460 --> 00:06:49,608 Urban beehives have a longer lifespan than rural and suburban beehives. 142 00:06:49,608 --> 00:06:52,257 And bees in the city are more biodiverse; 143 00:06:52,257 --> 00:06:54,425 there are more bee species in urban areas. 144 00:06:54,765 --> 00:06:55,945 (Laughter) 145 00:06:55,945 --> 00:06:57,126 Right? 146 00:06:57,336 --> 00:06:59,376 "Why is this?" 147 00:06:59,376 --> 00:07:00,716 That was our question. 148 00:07:00,716 --> 00:07:04,006 So, we started with these 3 killers of bees and we flipped it. 149 00:07:04,006 --> 00:07:06,206 Which of these is different in the cities? 150 00:07:06,206 --> 00:07:07,686 So, the first one: Pesticides. 151 00:07:07,686 --> 00:07:10,350 We partnered up with the Harvard School of Public Health. 152 00:07:10,350 --> 00:07:12,725 We shared our data with them, we collected samples 153 00:07:12,725 --> 00:07:15,855 from our citizen science beehives at people's homes and business rooftops. 154 00:07:15,855 --> 00:07:17,485 And we looked at pesticides levels 155 00:07:17,485 --> 00:07:21,615 and we thought there'd be less pesticides in areas where bees are doing better. 156 00:07:21,615 --> 00:07:22,731 That's not the case. 157 00:07:22,731 --> 00:07:27,201 What we found here in our study is that the orange bars are Boston, 158 00:07:27,201 --> 00:07:29,381 and we thought those bars would be the lowest, 159 00:07:29,381 --> 00:07:32,841 there would be the lowest level of pesticides and, in fact, 160 00:07:32,841 --> 00:07:35,941 there are the most pesticides in the cities. 161 00:07:36,221 --> 00:07:38,998 So, the pesticide hypothesis for what saving bees, 162 00:07:38,998 --> 00:07:40,477 less pesticides in cities, 163 00:07:40,477 --> 00:07:42,007 is not it. 164 00:07:42,007 --> 00:07:45,397 And this is very typical of my life as a scientist. 165 00:07:45,877 --> 00:07:47,737 Any time I've had a hypothesis, 166 00:07:47,737 --> 00:07:50,667 not only is it not supported, but the opposite is true. 167 00:07:50,667 --> 00:07:51,888 (Laughter) 168 00:07:51,898 --> 00:07:54,156 Which is still an interesting finding, right? 169 00:07:54,156 --> 00:07:55,156 We moved on. 170 00:07:55,156 --> 00:07:56,556 The disease hypothesis. 171 00:07:56,556 --> 00:07:58,946 We looked at diseases all over our beehives 172 00:07:58,946 --> 00:08:03,527 and what we found in similar study to this one with the North Carolina State: 173 00:08:03,527 --> 00:08:07,418 There's no difference between disease and bees in urban, suburban and rural areas, 174 00:08:07,418 --> 00:08:10,061 diseases are everywhere; bees are sick and dying. 175 00:08:10,061 --> 00:08:12,717 In fact, there were more diseases of bees in cities. 176 00:08:12,717 --> 00:08:14,767 This was from Raleigh, North Carolina. 177 00:08:14,767 --> 00:08:17,028 So again, my hypothesis was not supported, 178 00:08:17,028 --> 00:08:18,669 the opposite was true. 179 00:08:18,669 --> 00:08:19,969 We're moving on. 180 00:08:20,169 --> 00:08:22,369 (Laughter) 181 00:08:22,369 --> 00:08:24,518 The habitat hypothesis. 182 00:08:24,518 --> 00:08:28,358 This said that areas where bees are thriving have a better habitat. 183 00:08:28,358 --> 00:08:29,928 More flowers, right? 184 00:08:29,928 --> 00:08:31,768 But we didn't know how to test this. 185 00:08:31,768 --> 00:08:34,608 So, I had a really interesting meeting and an idea sparked 186 00:08:34,608 --> 00:08:38,285 with my friend and colleague, Anne Madden, fellow TED speaker, 187 00:08:38,285 --> 00:08:42,715 and we thought about genomics, kind of like AncestryDNA or 23andMe - 188 00:08:42,715 --> 00:08:43,936 Have you done these? 189 00:08:43,936 --> 00:08:47,241 You know, you spit in the tube and you find out, "I'm German," right? 190 00:08:47,241 --> 00:08:48,166 (Laughter) 191 00:08:48,166 --> 00:08:50,288 We developed this for honey, right? 192 00:08:50,288 --> 00:08:51,838 And so we have a sample of honey 193 00:08:51,838 --> 00:08:55,186 and we look at all the plant DNA and we find out, "I'm Sumac." 194 00:08:55,186 --> 00:08:56,416 (Laughter) 195 00:08:56,416 --> 00:08:58,929 And that's what we found here in Provincetown, 196 00:08:58,929 --> 00:09:01,599 and so, for the first time ever I'm able to report to you 197 00:09:01,599 --> 00:09:04,402 what type of honey is from right here in our own community. 198 00:09:04,402 --> 00:09:06,722 Honey DNA, a genomics test. 199 00:09:06,722 --> 00:09:09,899 Spring honey in Provincetown is from Privet. 200 00:09:09,899 --> 00:09:11,129 What's Privet? 201 00:09:11,129 --> 00:09:12,287 Hedges. 202 00:09:12,287 --> 00:09:13,467 What's the message? 203 00:09:13,467 --> 00:09:15,387 Don't trim your hedges to save the bees. 204 00:09:15,387 --> 00:09:16,217 (Laughter) 205 00:09:16,217 --> 00:09:16,991 Right? 206 00:09:16,991 --> 00:09:19,719 I know we're getting crunchy here, and it's controversial, 207 00:09:19,719 --> 00:09:21,345 so before you throw your tomatoes, 208 00:09:21,345 --> 00:09:24,091 let's move to the summer honey, which is water-lily honey. 209 00:09:24,091 --> 00:09:27,018 If you have honey from Provincetown, right here in the summer, 210 00:09:27,018 --> 00:09:28,750 you're eating water-lily juice. 211 00:09:28,750 --> 00:09:31,050 In the fall, sumac honey. 212 00:09:31,050 --> 00:09:34,360 We're learning about our food for the first time ever 213 00:09:34,360 --> 00:09:37,838 and now we are able to report, if you need to do any city planning, 214 00:09:37,838 --> 00:09:39,753 what are good things to plant, 215 00:09:39,753 --> 00:09:43,419 what do we know the bees are going to that's good for your garden. 216 00:09:43,447 --> 00:09:46,807 What's more interesting for us is deeper in the data. 217 00:09:46,807 --> 00:09:50,379 So, if you are from the Caribbean and you want to explore your heritage - 218 00:09:50,379 --> 00:09:55,415 Bahamian honey is from the Laurel family; cinnamon and avocado flavors. 219 00:09:55,445 --> 00:09:58,406 But what's more interesting is 85 different plant species 220 00:09:58,406 --> 00:10:00,236 in one teaspoon of honey. 221 00:10:00,236 --> 00:10:01,886 That's the measure that we want. 222 00:10:01,886 --> 00:10:02,966 The big data. 223 00:10:02,966 --> 00:10:04,536 Indian honey. 224 00:10:04,536 --> 00:10:05,806 That is oak. 225 00:10:05,806 --> 00:10:08,221 Every sample we tested from India is oak 226 00:10:08,221 --> 00:10:13,198 and that's a 172 different flavours in one taste of Indian honey. 227 00:10:13,198 --> 00:10:16,828 Provincetown honey goes from a 116 plants in the spring 228 00:10:16,828 --> 00:10:20,067 to over 200 plants in the summer. 229 00:10:20,067 --> 00:10:23,535 These are the numbers that we need to test the habitat hypothesis 230 00:10:23,535 --> 00:10:25,373 in another citizen science approach. 231 00:10:25,373 --> 00:10:28,533 You find out about your food and we get some interesting data. 232 00:10:28,533 --> 00:10:30,748 So, we're finding out now that in rural areas 233 00:10:30,748 --> 00:10:34,238 there are a 150 plants on average in a sample of honey. 234 00:10:34,238 --> 00:10:35,897 That's a measure for rural. 235 00:10:35,897 --> 00:10:37,927 Suburban areas, what might you think? 236 00:10:37,927 --> 00:10:42,109 Do they have less or more plants in suburban areas with lawns - 237 00:10:42,109 --> 00:10:45,894 that look nice for people but they're terrible for pollinators? 238 00:10:45,894 --> 00:10:48,649 Suburbs have very low plant diversity, 239 00:10:48,649 --> 00:10:50,394 so if you have a beautiful lawn, 240 00:10:50,394 --> 00:10:53,558 well, good for you, but you can do more. 241 00:10:53,558 --> 00:10:56,748 You can have a patch of your lawn that's a wildflower medow 242 00:10:56,748 --> 00:11:00,492 to diversify your habitat to improve pollinator health. 243 00:11:00,492 --> 00:11:02,280 Anybody can do this. 244 00:11:02,280 --> 00:11:04,778 Urban areas have the most habitat. 245 00:11:04,778 --> 00:11:08,370 The best habitat they have - 246 00:11:08,370 --> 00:11:10,778 As you can see here, over 200 different plants. 247 00:11:10,778 --> 00:11:14,864 We have, for the first time ever, support for the habitat hypothesis. 248 00:11:14,864 --> 00:11:18,014 We also now know how we can work with cities. 249 00:11:18,014 --> 00:11:22,016 The city of Boston has eight times better habitat than its nearby suburbs. 250 00:11:22,016 --> 00:11:25,822 And so when we work with governments, we can scale this. 251 00:11:25,832 --> 00:11:29,776 You might think on my tombstone it'll say, "Here lies Noah, plant a flower," right? 252 00:11:29,776 --> 00:11:31,006 (Laughter) 253 00:11:31,006 --> 00:11:33,697 It's exhausting after all of this, right? 254 00:11:33,697 --> 00:11:35,376 But when we scale together, 255 00:11:35,376 --> 00:11:37,549 when we go to governments and city planners - 256 00:11:37,549 --> 00:11:39,929 Like, in Boston, the honey is mostly Linden trees, 257 00:11:39,929 --> 00:11:43,069 and we say, "If a dead tree needs to be replaced, consider Linden." 258 00:11:43,069 --> 00:11:46,587 So, when we take this information to governments, we can do amazing things. 259 00:11:46,587 --> 00:11:48,873 This is a rooftop from Fred's company. 260 00:11:48,879 --> 00:11:52,428 We can plant those things on top of rooftops worldwide 261 00:11:52,428 --> 00:11:55,548 to start restoring habitat and securing food systems. 262 00:11:55,548 --> 00:11:57,239 We've worked with the World Bank, 263 00:11:57,239 --> 00:11:59,966 and the presidential delegation from the country of Haiti. 264 00:11:59,966 --> 00:12:03,606 We've worked with wonderful graduate students at Yale University in Ethiopia, 265 00:12:03,606 --> 00:12:07,415 and in these countries we can add value to their honey by identifying what it is, 266 00:12:07,415 --> 00:12:10,599 but informing the people of what to plant to restore their habitats 267 00:12:10,599 --> 00:12:12,722 and secure their food systems. 268 00:12:12,722 --> 00:12:16,541 But what I think is even more important is when we think about natural disasters. 269 00:12:16,541 --> 00:12:19,691 For the first time, we now know how we can have a baseline measure 270 00:12:19,691 --> 00:12:22,761 of any habitat before it might be destroyed. 271 00:12:22,776 --> 00:12:24,456 Think about your hometown. 272 00:12:24,456 --> 00:12:28,036 What risks does the environment pose to it? 273 00:12:28,036 --> 00:12:31,368 This is how we're going to save Puerto Rico after Hurricane Maria. 274 00:12:31,368 --> 00:12:34,378 We now have a baseline measure of honey, 275 00:12:34,378 --> 00:12:37,068 honey DNA from before and after the storm. 276 00:12:37,092 --> 00:12:39,179 We started in Humacao. 277 00:12:39,179 --> 00:12:41,629 This is right where Hurricane Maria made landfall. 278 00:12:41,629 --> 00:12:45,520 And we know what plants to replace, and in what quantity and where, 279 00:12:45,520 --> 00:12:48,399 by triangulating honey DNA samples. 280 00:12:48,977 --> 00:12:52,637 You might even think about right here, the beautiful land that connected us, 281 00:12:52,637 --> 00:12:55,876 that primed all the citizen science to begin with. 282 00:12:55,876 --> 00:13:00,007 The erosion, the winter storms that are getting more violent every year. 283 00:13:00,007 --> 00:13:03,657 What are we going to do about this, our precious land? 284 00:13:03,657 --> 00:13:04,994 While looking at honey DNA, 285 00:13:04,994 --> 00:13:07,205 we can see what plants are good for pollinators 286 00:13:07,205 --> 00:13:10,049 that have deep roots that can secure the land. 287 00:13:10,049 --> 00:13:14,916 And together, everybody can participate and the solution fits in a teaspoon. 288 00:13:14,916 --> 00:13:19,682 If your hometown might get swept away or destroyed by a natural disaster, 289 00:13:19,682 --> 00:13:25,899 we now have a blueprint suspended in time for how to restore that on Earth, 290 00:13:25,899 --> 00:13:29,982 or perhaps even in a greenhouse on Mars. 291 00:13:29,982 --> 00:13:33,653 I know it sounds crazy, but think about this, 292 00:13:33,653 --> 00:13:35,485 a new Provincetown, 293 00:13:35,935 --> 00:13:38,004 a new hometown, 294 00:13:38,164 --> 00:13:39,813 a place that might be familiar 295 00:13:39,813 --> 00:13:42,938 that's also good for pollinators for a stable food system, 296 00:13:42,938 --> 00:13:44,913 when we're thinking about the future. 297 00:13:44,913 --> 00:13:47,963 Now, together we know what's saving bees: 298 00:13:47,963 --> 00:13:50,092 By planting diverse habitats. 299 00:13:50,092 --> 00:13:52,352 We know how bees are going to save us: 300 00:13:52,352 --> 00:13:55,796 By being barometers for environmental health, 301 00:13:55,796 --> 00:13:58,578 by being blueprints, sources of information, 302 00:13:58,578 --> 00:14:01,656 little data factories, suspended in time. 303 00:14:01,656 --> 00:14:05,396 And now you all know exactly what you can do as citizen scientists 304 00:14:05,396 --> 00:14:07,033 to get beehives. 305 00:14:07,033 --> 00:14:08,275 Thank you. 306 00:14:08,275 --> 00:14:10,395 (Applause)