WEBVTT FILE 1 00:00:00.010 --> 00:00:04.050 So in the Central Valley, we once had a vast system 2 00:00:04.070 --> 00:00:08.150 of about four million acres of wetlands and wetland-like features, 3 00:00:08.170 --> 00:00:12.180 this integrated mosaic of wet stuff. And since that time, 4 00:00:12.200 --> 00:00:16.250 we've lost about 95% of those habitats. 5 00:00:16.270 --> 00:00:20.330 [chime] California's Central Valley is home to one of the largest agro-ecosystems 6 00:00:20.350 --> 00:00:24.460 on the planet. Since the loss of the wetlands, migratory birds 7 00:00:24.480 --> 00:00:28.520 are now very dependant on what's happening on that agricultural land 8 00:00:28.540 --> 00:00:32.700 to find places to forage and to spend the winter. 9 00:00:32.720 --> 00:00:36.750 So our work with farmers is really an integral part of the flyway. 10 00:00:36.770 --> 00:00:40.900 Waterbirds need water to give them access to a lot 11 00:00:40.920 --> 00:00:44.960 of the food resources. Shorebirds in particular eat a lot of the 12 00:00:44.980 --> 00:00:49.130 aquatic invertebrates that grow in the water. And so, by knowing where the water 13 00:00:49.150 --> 00:00:53.260 is, we can really maximize the value of the 14 00:00:53.280 --> 00:00:57.340 restoration and conservation we do for waterbirds in the Central Valley. 15 00:00:57.360 --> 00:01:01.500 The NASA and USGS data that are available allow us to make 16 00:01:01.520 --> 00:01:05.530 the distribution maps on the probability that water might be in any given pixel – 17 00:01:05.550 --> 00:01:09.570 so that's a 30-meter by 30-meter cell anywhere within the Central Valley of California. 18 00:01:09.590 --> 00:01:13.700 The real value of the satellite, and Landsat archive, 19 00:01:13.720 --> 00:01:17.720 is that we are able to look at the water distribution at a very fine spatial scale 20 00:01:17.740 --> 00:01:21.740 so that's a 30- by 30-meter pixel, which is really relevant in terms of 21 00:01:21.760 --> 00:01:25.820 understanding habitat for migratory water birds. We're also able then to look at that 22 00:01:25.840 --> 00:01:29.860 across a very large spatial extent of the entire Central Valley. 23 00:01:29.880 --> 00:01:33.930 Point Blue Conservation Science has been one of our 24 00:01:33.950 --> 00:01:38.050 trusted conservation partners at the Nature Conservancy for many many years 25 00:01:38.070 --> 00:01:42.090 and so I reached out to our partners at Point Blue and we started working 26 00:01:42.110 --> 00:01:46.170 together on these Landsat data to try and perfect ways that 27 00:01:46.190 --> 00:01:50.200 we could use to predict water availability in the Central Valley. 28 00:01:50.220 --> 00:01:54.250 So simultaneously, the Cornell Lab of Ornithology 29 00:01:54.270 --> 00:01:58.430 was really making great strides in using citizen science data 30 00:01:58.450 --> 00:02:02.490 to predict when and where birds would occur. 31 00:02:02.510 --> 00:02:06.520 I'm Steve Kelling, I'm the Director of Information sciences 32 00:02:06.540 --> 00:02:10.590 at the Cornell Lab of Ornithology. 33 00:02:10.610 --> 00:02:14.690 I run a project called eBird. 34 00:02:14.710 --> 00:02:18.770 eBird is a citizen scientist project that engages the public 35 00:02:18.790 --> 00:02:22.910 to submit checklists of their bird observations 36 00:02:22.930 --> 00:02:27.000 to a central database. 37 00:02:27.020 --> 00:02:34.170 Currently we collect about a 100 million observations per year. 38 00:02:34.190 --> 00:02:39.270 What makes eBird unique, though, 39 00:02:39.290 --> 00:02:43.400 is that because we collect data year round, we can essentially 40 00:02:43.420 --> 00:02:47.470 describe the entire life history of a bird or a population 41 00:02:47.490 --> 00:02:53.430 of birds as the move throughout the landscape. 42 00:02:53.450 --> 00:03:00.510 So when we think of how a bird moves across a hemisphere, 43 00:03:00.530 --> 00:03:04.560 we can use MODIS landcover information to allow us 44 00:03:04.580 --> 00:03:08.630 to make these habitat relationships 45 00:03:08.650 --> 00:03:12.660 with particular species of birds. 46 00:03:12.680 --> 00:03:16.730 And with that kind of relationship we can then make predictions 47 00:03:16.750 --> 00:03:20.860 in areas where we don't have information about birds 48 00:03:20.880 --> 00:03:24.880 but we do have information about the habitats that they're in. 49 00:03:24.900 --> 00:03:28.970 What we're able to show 50 00:03:28.990 --> 00:03:33.140 was that there was a high correlation between 51 00:03:33.160 --> 00:03:37.310 abundance of shorebirds 52 00:03:37.330 --> 00:03:41.390 with rice farming 53 00:03:41.410 --> 00:03:45.510 and then go out to the rice fields and feed during the day 54 00:03:45.530 --> 00:03:49.540 and then go to the refuges at night to roost. 55 00:03:49.560 --> 00:03:53.610 And the a-ha moment really came with putting 56 00:03:53.630 --> 00:03:57.750 these two datasets together and realizing that there were some horrible mis-matches. 57 00:03:57.770 --> 00:04:01.810 So we had models predicting high abundances of birds 58 00:04:01.830 --> 00:04:05.870 at times when there was not very much water. 59 00:04:05.890 --> 00:04:09.970 And that made us realize there was something we could do out there to make that place better 60 00:04:09.990 --> 00:04:14.010 for birds at that time. 61 00:04:14.030 --> 00:04:18.090 Our program Bird Returns allows us to work directly with farmers to help them help us create bird habitat. 62 00:04:18.110 --> 00:04:22.120 Rice farmers typically flood their fields 63 00:04:22.140 --> 00:04:26.210 to grow the crop, so we knew there was water available 64 00:04:26.230 --> 00:04:30.240 just not at the right times and places to help the birds. So by working with 65 00:04:30.260 --> 00:04:34.300 rice farmers we were able to essentially rent their fields 66 00:04:34.320 --> 00:04:38.350 for a couple weeks a year, and instead of growing rice, create the conditions 67 00:04:38.370 --> 00:04:42.410 which would grow birds or create habitat for wintering birds. 68 00:04:42.430 --> 00:04:46.580 Look, these birds are migratory superheroes. 69 00:04:46.600 --> 00:04:50.670 They mystify us. The shorebirds we work with are breeding in Alaska 70 00:04:50.690 --> 00:04:54.750 and wintering as far south as southern Peru. 71 00:04:54.770 --> 00:04:58.890 We're talking up to 20,000 kilometers each year, some of these small birds 72 00:04:58.910 --> 00:05:02.950 are flying. So it behooves us to 73 00:05:02.970 --> 00:05:07.130 help them on their journey, provide this food resource for them 74 00:05:07.150 --> 00:05:11.300 but to do it in a responsible way that again shows our respect 75 00:05:11.320 --> 00:05:15.450 for the limited resource that water is, today, in the Western United States. 76 00:05:15.470 --> 00:05:19.530 Bird Returns is having a lot of impact, 77 00:05:19.550 --> 00:05:23.660 both for wetland conditions and for farmers, but also for the birds. 78 00:05:23.680 --> 00:05:27.700 And we're monitoring birds on all of the fields that we flood 79 00:05:27.720 --> 00:05:31.800 out there and we're comparing those with bird observations on fields that aren't flooded. 80 00:05:31.820 --> 00:05:35.990 So we're finding densities that are 30 times 81 00:05:36.010 --> 00:05:40.090 greater on our fields than on the comparison control fields. 82 00:05:40.110 --> 00:05:44.110 The ability of them to use refuge lands and 83 00:05:44.130 --> 00:05:48.160 a compatible agricultural landscape allows us to manage 84 00:05:48.180 --> 00:05:52.210 the whole valley as an integrated matrix. 85 00:05:52.230 --> 00:05:56.310 This is really the power of using these kinds of data to make conservation decisions. 86 00:05:56.330 --> 00:06:00.330 Not only for the Nature Conservancy but also for 87 00:06:00.350 --> 00:06:04.400 public lands and private land managers. 88 00:06:04.420 --> 00:06:09.812