WEBVTT Kind: captions Language: en 00:00:00.500 --> 00:00:01.420 - [Richard] Can you hear me? 00:00:03.200 --> 00:00:04.280 - [Claire] We can. Thank you, Richard. 00:00:04.480 --> 00:00:05.100 - Okay, great. 00:00:05.520 --> 00:00:07.960 Awesome, yeah. Thanks for joining me. 00:00:07.960 --> 00:00:10.020 I'm glad you guys are interested in my talk. 00:00:10.020 --> 00:00:14.480 Yeah, so, I'll be giving you a little talk about the research that I do. 00:00:14.480 --> 00:00:18.880 I'm using genetics to look at connectivity across the Hawaiian Archipelago. 00:00:20.240 --> 00:00:22.520 Just to give you a little background about myself 00:00:22.520 --> 00:00:27.080 Yeah I recently received my PhD in zoology at the University of Hawaii. 00:00:27.080 --> 00:00:31.800 This is an image of the Hawaii Institute of Marine Biology, where I do my research. 00:00:32.340 --> 00:00:35.040 This is the marine station for the University of Hawaii. 00:00:35.560 --> 00:00:38.560 And I'm pretty fortunate to be able to do my research here. 00:00:38.560 --> 00:00:42.960 As you can see, it's our own little island where we get to do all of our own research. 00:00:44.020 --> 00:00:47.640 The whole island is it dedicated just to marine science. 00:00:48.200 --> 00:00:52.060 and we have access to all the reefs of Kaneohe Bay. 00:00:52.060 --> 00:00:56.440 So this is Kaneohe Bay on the eastern side of the island of Oahu in the Hawaiian Islands 00:00:57.600 --> 00:01:03.160 and I'm part of the lab run by Dr. Brian Bowen and Rob Tonin. 00:01:03.420 --> 00:01:09.700 And our research looks at how biodiversity is generated in the marine realm. 00:01:09.700 --> 00:01:14.940 And one of the larger components that we use is genetics to be able to answer some of these questions. 00:01:17.160 --> 00:01:21.300 So this is just a brief history of how genetics has evolved. 00:01:21.300 --> 00:01:24.420 So, you know, going back all the way to the eighteen hundreds 00:01:24.420 --> 00:01:29.140 we have Darwin who published on his theory of evolution by natural selection. 00:01:29.700 --> 00:01:36.320 And there's been progress since then but it wasn't until maybe around the 1950s 00:01:36.320 --> 00:01:40.040 where it's really started to amplify in terms of the technology 00:01:40.040 --> 00:01:44.440 and the amount of resources and data that we were able to get from using genetics. 00:01:45.160 --> 00:01:50.420 So, in the 1950s we have the double helix was finally described. 00:01:50.420 --> 00:01:53.260 in 1995 the first genome was sequenced. 00:01:53.260 --> 00:01:58.500 And then up till now in about 2006 he started using Illumina sequencing 00:01:58.500 --> 00:02:03.720 which is being able to see sequence large parts of the genome 00:02:03.720 --> 00:02:10.260 and that accounts for greater than more than 70% of the current market and in terms of genetic studies. 00:02:12.140 --> 00:02:16.660 and here's just a graph that shows how much the cost of doing genomic sequencing 00:02:16.660 --> 00:02:18.320 has declined. 00:02:18.320 --> 00:02:21.020 Since starting in 2001, we had 00:02:21.020 --> 00:02:25.300 it costed maybe a hundred million dollars to be able to sequence the genome. 00:02:25.300 --> 00:02:29.300 Whereas we fast forward to now, it only costs a few thousand dollars. 00:02:29.640 --> 00:02:33.880 and so the ability and availability to be able to sequence 00:02:34.580 --> 00:02:38.040 do genomic sequencing which provides much more data for us to have. 00:02:38.040 --> 00:02:42.660 much more resolution in terms of the being able to answer some of the questions 00:02:42.660 --> 00:02:43.820 that were interested in. 00:02:43.820 --> 00:02:48.680 Whether or not they're evolution, or for what I do, where I'm looking at dispersal and connectivity 00:02:48.680 --> 00:02:50.040 which I'll get into further. 00:02:51.840 --> 00:02:56.040 So there's a variety of ways that you could use genetics 00:02:56.040 --> 00:02:58.680 to answer questions regarding biodiversity. 00:02:58.680 --> 00:03:02.900 and so you can look at evolutionary theory, you can identify different species 00:03:02.900 --> 00:03:06.500 and basic species as an example, is something that I've used in the past. 00:03:08.000 --> 00:03:13.100 but again I'm more interested in looking at connectivity and it's personal patterns of marine fishes. 00:03:14.700 --> 00:03:21.540 And you could use genetics for a for a variety of different organisms. 00:03:21.540 --> 00:03:27.280 So this is just a really small subset of the species that we've looked at in my lab. 00:03:27.280 --> 00:03:31.360 So we've looked at the genetics of sea cucumbers 00:03:31.360 --> 00:03:33.580 it's the cross Crown of Thorns Star 00:03:33.580 --> 00:03:37.620 grasses, Niba tones, lobsters, Opihi-- or limpets. 00:03:37.620 --> 00:03:38.900 fishes. 00:03:38.900 --> 00:03:44.840 and so there's really no limit to what you could or what organisms you could work on 00:03:44.840 --> 00:03:46.820 to be able to do genetics and 00:03:47.920 --> 00:03:50.680 depending on the question that you're interested in addressing. 00:03:52.220 --> 00:03:56.160 And as I mentioned, I'm particularly interested in looking at coral reef fishes. 00:03:56.780 --> 00:04:00.620 And this is a really diverse group of organisms. 00:04:00.620 --> 00:04:02.840 They're really important to the ecosystem. 00:04:02.840 --> 00:04:06.980 But, more importantly, they're really important for food availability. 00:04:06.980 --> 00:04:15.620 A large part of the global population relies on marine resources for the primary source of protein. 00:04:15.620 --> 00:04:22.080 So it's important for us to properly understand what is occurring within this group 00:04:22.080 --> 00:04:25.640 to ensure that they the ecosystem is healthy 00:04:25.640 --> 00:04:28.480 since they're large a component of a healthy ecosystem 00:04:28.480 --> 00:04:33.180 But also to ensure long-term sustainability for these nations or communities 00:04:33.180 --> 00:04:37.620 that rely on these organisms for for sustenance. 00:04:39.660 --> 00:04:42.880 So, as I mentioned, I'm interested in understanding connectivity 00:04:42.880 --> 00:04:48.660 and by connectivity what it can do is it can help us provide insights and some mechanisms 00:04:48.660 --> 00:04:50.240 that influence evolution. 00:04:50.240 --> 00:04:57.020 So we could look at identifying areas that might encourage evolution to, to occur. 00:04:57.020 --> 00:04:59.860 So if we know that there's two areas that are separating 00:04:59.860 --> 00:05:01.840 they might diverge into separate species. 00:05:01.840 --> 00:05:05.960 But it's also useful to inform management and conservation strategies. 00:05:07.480 --> 00:05:14.100 And the way it's able to do that is we can identify areas that are barriers to dispersal. 00:05:14.420 --> 00:05:20.620 We can also identify areas that are vulnerable to over-fishing, for example 00:05:20.620 --> 00:05:24.060 and we can identify particular management units. 00:05:24.060 --> 00:05:27.960 And one of the things that I'm interested in doing or that I've done with my research 00:05:27.960 --> 00:05:30.780 is characterizing source-sink population. 00:05:30.780 --> 00:05:36.520 So by source populations what I mean are populations that are the origin 00:05:36.520 --> 00:05:40.120 and they're the source for receiving other areas 00:05:40.120 --> 00:05:44.720 and the sink populations are receiving recruitment from their source populations. 00:05:44.720 --> 00:05:49.440 And I'm gonna go through a quick diagram of sort of what I mean by that. 00:05:49.440 --> 00:05:52.960 When I'm talking about source-sink populations and genetic connectivity. 00:05:52.960 --> 00:06:00.240 But first we need to discuss or we need to know sort of the life cycle of marine fishes. 00:06:00.240 --> 00:06:03.360 And this might be review for a lot of people, but I want to quickly go over it. 00:06:04.400 --> 00:06:10.300 So we have the adults and they spawn typically, a lot of marine fishes do free spawning 00:06:10.300 --> 00:06:13.080 where they form these really large aggregations 00:06:13.080 --> 00:06:15.840 and they throw their gametes into the water column 00:06:15.840 --> 00:06:17.620 where fertilization occurs 00:06:17.620 --> 00:06:23.160 and then the larvae form and they're possibly floating around the water column for a while. 00:06:23.160 --> 00:06:28.200 And then they form its or early larvae and then smaller versions of the adults 00:06:28.200 --> 00:06:30.320 and they finally settle out of the water column. 00:06:31.540 --> 00:06:36.800 But it's at is this planktonic stage, when they're in this early larvae late larvae stage where 00:06:36.800 --> 00:06:39.380 the source of dispersal is actually occurring. 00:06:39.380 --> 00:06:44.000 'Cause as adults, once they settle down that's the area that they settle down in 00:06:44.000 --> 00:06:45.100 and it's where they stay. 00:06:45.100 --> 00:06:48.760 So this larval stage, which can last between 40 and 60 days, 00:06:48.760 --> 00:06:54.980 is an area or the time frame where they're able to move long distances as a species. 00:06:56.400 --> 00:07:02.160 And here's a quick cartoon of what I'm, what I mean when I'm discussing the population connectivity. 00:07:02.940 --> 00:07:09.220 So we have this hypothetical fish population here on the eastern side of Oahu. 00:07:09.220 --> 00:07:10.900 This is Kanohe Bay, 00:07:11.700 --> 00:07:16.080 They spawn and the larvae are released and are floating around passively. 00:07:16.080 --> 00:07:20.260 And then they form little, they turn into little larvae and then they swim north 00:07:20.260 --> 00:07:26.020 head to the northern part of Oahu where they finally settle out of the water column. 00:07:26.660 --> 00:07:30.240 So we would call the Kaneohe a population as a source population 00:07:30.240 --> 00:07:32.960 and the northern population as a sink population 00:07:32.960 --> 00:07:35.400 because they're the ones receiving recruitment from Kaneohe. 00:07:36.800 --> 00:07:39.780 What's important or where the issue arises is 00:07:39.780 --> 00:07:42.980 if the source population is decimated for some reason. 00:07:42.980 --> 00:07:45.160 So let's just say as an example they're over-fished 00:07:45.680 --> 00:07:49.460 So the Kaneohe population is over fished, the downstream effect is that the 00:07:49.460 --> 00:07:53.140 northern population will also decline because they're dependent on Kaneohe 00:07:53.320 --> 00:07:55.760 to receive recruitment for that area. 00:07:55.760 --> 00:08:00.100 And you can imagine that this network is much more complex 00:08:00.100 --> 00:08:04.860 but it's important to understand or identify these disposal pathways 00:08:04.860 --> 00:08:08.680 if we want to identify areas I might be vulnerable for over-fishing 00:08:08.680 --> 00:08:11.420 or identify areas that are just under threat. 00:08:12.600 --> 00:08:18.640 Another issue that could arise is the presence of barriers to gene flow 00:08:18.640 --> 00:08:22.260 and this could be cause for numerous reasons 00:08:22.260 --> 00:08:27.700 so it could be a freshwater outflow, so the fish wouldn't be able to cross it. 00:08:27.700 --> 00:08:32.820 So this is just this sort of barrier that just exists, it could be something geological, 00:08:32.820 --> 00:08:38.620 there might be currents or oceanic currents that are preventing larvae from being able to 00:08:38.620 --> 00:08:40.080 get past that area. 00:08:40.080 --> 00:08:42.660 But a lot of these things are not obvious. 00:08:43.560 --> 00:08:46.940 But we can identify them and we can see the signal when we look at the genetics. 00:08:47.900 --> 00:08:53.400 So when we see barriers in a terrestrial system there they're often very easy to identify. 00:08:53.400 --> 00:08:57.960 So this picture, just as an example, this is a large river, I believe it's the Amazon River 00:08:57.960 --> 00:09:03.220 and so you could just think of maybe a species of snails on either side. 00:09:03.720 --> 00:09:07.820 If we look at the genetics and we see that they're very different genetically 00:09:07.820 --> 00:09:12.860 then we could identify that it's likely the river, itself, is acting as a barrier. 00:09:12.860 --> 00:09:15.480 But you could also look at it in a larger sense. 00:09:15.480 --> 00:09:18.600 So you can look at two populations on either side of a mountain. 00:09:18.600 --> 00:09:21.080 The mountain itself is acting as a barrier 00:09:21.080 --> 00:09:26.240 or if you're looking at different populations of let's say a butterfly 00:09:26.240 --> 00:09:27.720 on an island, on different islands. 00:09:28.600 --> 00:09:31.760 The islands themselves, there's just distance too much distance between them 00:09:31.760 --> 00:09:33.260 where they're not able to cross it. 00:09:34.680 --> 00:09:39.000 and as I mention in the marine world realm, it's not so obvious. 00:09:39.000 --> 00:09:44.400 So the prevailing idea was that because these organisms are possibly flying around 00:09:44.400 --> 00:09:47.100 or floating around, that they're getting everywhere. 00:09:47.100 --> 00:09:50.820 But the more and more that we look at the genetics 00:09:50.820 --> 00:09:56.958 and investigate these systems, we see that there's a larger number of barriers that exist 00:09:56.960 --> 00:09:59.060 than previously thought. 00:10:01.000 --> 00:10:05.460 And so, for my research, I'm looking interested in looking at connectivity and identifying 00:10:05.680 --> 00:10:10.140 these barriers or pathways of dispersal across a variety of different spatial scales. 00:10:10.840 --> 00:10:15.360 So part of my research data from my dissertation looked across entire species ranges. 00:10:15.360 --> 00:10:19.100 So a species that's an accomplice in Indian Ocean in the Pacific Ocean 00:10:19.660 --> 00:10:21.880 I looked at the scale of an archipelago. 00:10:21.880 --> 00:10:25.380 So across the Hawaiian archipelago and also with a scale with an island. 00:10:26.240 --> 00:10:30.720 And so for the purpose of this talk, I'll be talking about the range ride 00:10:30.720 --> 00:10:35.720 and I'll just be focusing on the archipelago and Island scale. 00:10:35.720 --> 00:10:40.850 and as I go through when you consider the implications in terms of conservation 00:10:40.850 --> 00:10:47.360 and management how they can be applied to confirm inform these these methods. 00:10:48.840 --> 00:10:51.160 so looking at the archipelago scale. 00:10:52.740 --> 00:10:56.220 So all of my research for this is occurred in Hawaii. 00:10:56.220 --> 00:10:59.940 And so when we think of Hawaii-- for a lot a lot of people when we think of Hawaii, 00:10:59.940 --> 00:11:01.820 we think of the the main Hawaiian Islands. 00:11:01.820 --> 00:11:05.509 These are the eight islands that people would go on vacation to. like the Big Island, 00:11:05.509 --> 00:11:10.000 Honolulu, here on Oahu, Maui, Kauai. 00:11:10.720 --> 00:11:17.260 But the Hawaiian archipelago actually extends quite much more than just what is typically known. 00:11:17.260 --> 00:11:21.880 So we have the main Hawaiian Islands which is more touristy areas. 00:11:22.520 --> 00:11:27.240 But northwest of that is the or the northwestern Hawaiian Islands 00:11:27.240 --> 00:11:33.060 which in 2006 was designated Papahanaumokuakea marine national monument. 00:11:33.060 --> 00:11:38.860 So this is a protected area that encompasses about 2600 kilometers 00:11:39.780 --> 00:11:43.520 and so if you're to superimpose that on the United States 00:11:43.520 --> 00:11:46.740 you can get a sense for how large of an area this is 00:11:46.740 --> 00:11:49.600 So if you put the Big Island on New Orleans, 00:11:49.600 --> 00:11:56.260 the last Island cure way and the for the Hawaiian archipelago will sit on top of Las Vegas. 00:11:56.260 --> 00:12:00.570 So this is not a trivial amount in terms of the area that it covers. This is a large area 00:12:00.570 --> 00:12:03.480 and makes up a substantial portion of the state of Hawaii. 00:12:05.520 --> 00:12:09.120 And so there's kind of two areas of the Hawaiian archipelago. 00:12:09.120 --> 00:12:12.960 So there's the main Hawaiian Islands, as I mentioned, these are the high islands. 00:12:12.960 --> 00:12:17.260 They're typically thought of. So you have these big mountain ranges, 00:12:17.260 --> 00:12:19.580 there's a lot of people that live on these islands, 00:12:20.420 --> 00:12:22.780 there's a lot of waterfalls, a lot of freshwater. 00:12:23.380 --> 00:12:28.040 But as you go north west and as you enter the Papahanaumokuakea marine national monument 00:12:28.040 --> 00:12:31.680 you get to these low-lying Islands or atolls 00:12:31.680 --> 00:12:36.380 that in some areas don't sit more than ten feet above sea level, 00:12:36.920 --> 00:12:40.140 And so and there's no inhabitants and these are in in these areas. 00:12:40.480 --> 00:12:45.540 And so this is a really pristine area and I felt very fortunate to be able to 00:12:45.540 --> 00:12:49.980 conduct my research in this area, just because it's there's no people there. 00:12:49.980 --> 00:12:53.360 And because of that, the reefs themselves are largely intact. 00:12:53.360 --> 00:12:58.889 The fish communities are really diverse, there's a lot of abundance, the coral reefs are really 00:12:58.889 --> 00:13:06.820 healthy, and it provides just importance of having that protection. To be able to have this area 00:13:06.820 --> 00:13:08.580 that is just off-limits to people. 00:13:11.160 --> 00:13:13.940 But for my research, I was interested in knowing 00:13:13.940 --> 00:13:19.040 does the protected area, Papahanaumokuakea, does that act as a source 00:13:19.040 --> 00:13:21.220 of recruitment for the main Hawaiian Islands. 00:13:21.220 --> 00:13:25.440 So the main Hawaiian Islands because it's overpop-- or because there's a large population 00:13:25.440 --> 00:13:30.760 and people fish recreationally 00:13:30.760 --> 00:13:32.080 they fish for sustenance 00:13:32.080 --> 00:13:37.140 there's the prospect of that this region might be over-fished at some point. 00:13:37.140 --> 00:13:42.240 so I'm curious to know, does the Marine National Monument act as a source 00:13:42.240 --> 00:13:45.700 to see Hawaiian at the main Hawaiian Islands, if they're over-fished. 00:13:45.700 --> 00:13:50.880 But conversely, what's the influence of the main Hawaiian Islands to the Marine National Monument. 00:13:51.460 --> 00:13:53.020 Are they acting as a source 00:13:54.620 --> 00:13:58.420 and the the monuments acting as the sink in this area. 00:13:58.420 --> 00:14:03.860 Or is there just a clear barrier between the two. Are they just really completely separate regions 00:14:03.860 --> 00:14:06.560 that don't have any interactions with each other. 00:14:08.080 --> 00:14:11.800 And so for my research I, we, I, 00:14:11.810 --> 00:14:18.140 got to be at sea for about 30 days, at least, and on three different occasions. 00:14:18.140 --> 00:14:21.860 So we started on Oahu and 00:14:22.620 --> 00:14:26.940 we went to all the different islands across the entire Hart Hawaiian Archipelago. 00:14:26.940 --> 00:14:29.980 So from the Big Island all the way to Kure Atoll. 00:14:31.080 --> 00:14:35.480 And so we stay on this ship and it's a pretty cool ship is. 00:14:36.320 --> 00:14:39.360 There's a bunch of rooms for scientists. 00:14:39.860 --> 00:14:41.420 We're fed pretty well. 00:14:41.420 --> 00:14:44.820 And we have these little small boats that we do all of our research on. 00:14:44.820 --> 00:14:50.100 So we'll, every morning at 7:00 we'll have a briefing of the objectives for the day 00:14:50.100 --> 00:14:54.300 and by 8 o'clock all the scientists go on these small red boats. 00:14:54.300 --> 00:14:59.780 and we remain at sea for until around 4 or 5 o'clock 00:14:59.780 --> 00:15:02.860 where you go back to the larger boat and 00:15:03.820 --> 00:15:06.720 we get back on and that's where we process all of our samples 00:15:08.540 --> 00:15:10.440 And so when we go out to collect, 00:15:11.080 --> 00:15:15.740 we only need a really small piece of tissue from the fish in order to 00:15:15.740 --> 00:15:18.840 to get a genetic identity that were interested in. 00:15:18.840 --> 00:15:21.960 And so it's only the size of maybe the head of a nail. 00:15:22.520 --> 00:15:26.880 So we collect the tissue, we extract the DNA, 00:15:26.880 --> 00:15:30.820 And so this isn't all occurring on the boat. We actually take this back to the lab here on Oahu. 00:15:30.820 --> 00:15:35.180 So we extract the DNA, we do some other methods 00:15:35.180 --> 00:15:39.000 and we finally send it off to get the to the sequencing machine. 00:15:39.000 --> 00:15:43.680 And we get an output that gives us a visual representation of what the DNA looks like. 00:15:43.680 --> 00:15:47.080 And from that we're able to analyze our data. 00:15:49.480 --> 00:15:54.520 And this is just an example of sort of just to easily conceptualize what I'm talking about. 00:15:54.600 --> 00:15:59.840 So if you're familiar with 23andMe, it's really what we do in terms of looking at genetic connectivity 00:16:00.080 --> 00:16:04.400 So 23andMe, you submit your DNA 00:16:04.400 --> 00:16:09.760 and the site assigns you to some region of the world that you're most likely originating from. 00:16:10.540 --> 00:16:15.600 And so, in this example, this individual has a genetic signature that 00:16:15.600 --> 00:16:19.160 belongs both to Europe and also to East Asia. 00:16:19.160 --> 00:16:23.260 But it's because each of these regions of the worlds have has a unique genetic signature 00:16:23.260 --> 00:16:27.200 that we can trace an individual back to where they originated from. 00:16:27.200 --> 00:16:30.120 and we're essentially doing that in terms of fish. 00:16:30.120 --> 00:16:33.720 Instead of looking at it from a global scale we're looking at it from an island scale. 00:16:33.720 --> 00:16:38.480 So each island has a unique genetic signature or or may have a unique just a genetic signature. 00:16:38.480 --> 00:16:45.080 and we're trying to figure out if it's one large cosmopolitan group 00:16:45.080 --> 00:16:47.440 or if each island is unique. 00:16:48.840 --> 00:16:52.280 And this is another example doing using population genetics. 00:16:52.740 --> 00:16:57.980 So we have here we have four hypothetical populations. 00:16:58.620 --> 00:17:03.380 Each circle represents an individual and each color represents the gene. 00:17:03.380 --> 00:17:06.500 So there's the blue gene and an orange gene. 00:17:07.040 --> 00:17:13.720 And so in this example population one is largely blue and population four is mostly orange. 00:17:13.720 --> 00:17:16.900 But as you move from left to right 00:17:16.900 --> 00:17:21.640 blue the blue gene gets, becomes less and less prominent. 00:17:22.320 --> 00:17:24.380 so what we would say in this example is 00:17:24.380 --> 00:17:27.960 population one is genetically distinct from population four 00:17:27.960 --> 00:17:31.860 the population one is more similar to population two 00:17:31.860 --> 00:17:34.140 populations two is more similar population three 00:17:34.140 --> 00:17:36.400 and population three is most similar to population four. 00:17:38.580 --> 00:17:41.340 Here's another just more concrete example. 00:17:41.340 --> 00:17:50.160 So population 1 is all blue, population is all 4, populations 2 & 3 have the presence of the blue gene 00:17:50.160 --> 00:17:52.580 but it's largely overrun by orange. 00:17:52.580 --> 00:17:57.460 And what we would say if we looked at this from a, in terms of population genetics 00:17:58.120 --> 00:18:03.000 perspective is that there's a genetic break that's occurring between population 1 and the rest of the 00:18:03.010 --> 00:18:03.840 population. 00:18:03.840 --> 00:18:10.800 If that break didn't exist, we would see blue more prominent in the rest of the populations. 00:18:14.440 --> 00:18:19.880 And so my my lab group has done a lot of research across the Hawaiian Archipelago. 00:18:19.880 --> 00:18:23.860 and so we've looked at at this stage is probably more than 50 different species. 00:18:23.860 --> 00:18:28.780 And from these species we found common breaks that are found 00:18:28.780 --> 00:18:30.420 in different regions. 00:18:30.420 --> 00:18:35.380 So, starting from the right, we see a break between the Big Island and Maui. 00:18:35.380 --> 00:18:40.220 For in this example 16 of the 24 species that we looked at, 00:18:40.220 --> 00:18:44.080 there's a smaller break between Lanai and Oahu. 00:18:44.080 --> 00:18:47.920 We also see a break between Oahu and Kauai. 00:18:47.920 --> 00:18:52.628 and then as we move forward north west there's a break that occurs somewhere around Nihoa 00:18:52.628 --> 00:18:53.940 and French Frigate Shoals 00:18:54.720 --> 00:19:01.720 And then there's this large segment where all the islands are genetically homogeneous 00:19:01.720 --> 00:19:04.640 meaning they're all really similar to each other so there's no break 00:19:04.640 --> 00:19:07.680 until you get to around Pearl and Hermes. 00:19:09.100 --> 00:19:11.900 and around Pearl and Hermes. 00:19:12.960 --> 00:19:15.760 And so this is common across multiple species 00:19:15.760 --> 00:19:21.400 which indicates that there's something in the ecosystem that is causing these breaks. 00:19:21.400 --> 00:19:25.260 so between Hawaii and Maui 00:19:25.260 --> 00:19:29.500 the prevailing idea, or the accepted idea is that 00:19:31.100 --> 00:19:32.620 there's just way too much current. 00:19:32.620 --> 00:19:36.940 So between that those two islands it drops to about a thousand meters. 00:19:37.700 --> 00:19:41.820 So it's just too deep, there's too much current, so any larvae that is trying to cross it 00:19:42.060 --> 00:19:43.100 will just get lost. 00:19:43.100 --> 00:19:47.040 There's also eddies so these circular ocean circulation patterns. 00:19:47.040 --> 00:19:51.680 So if a larvae were get caught in it he will just remain in this sort of tornado 00:19:51.680 --> 00:19:52.760 in the middle of the ocean. 00:19:52.760 --> 00:19:54.900 so it's not able to get to either area. 00:19:54.900 --> 00:19:58.440 And then as you move along there's other reasons that that could occur. 00:20:00.880 --> 00:20:06.540 But so for this study a lot of the a lot of the research incorporated for this study used 00:20:06.540 --> 00:20:08.500 just one single marker. 00:20:08.500 --> 00:20:13.860 So one genetic marker so just think of it as maybe looking at the gene for eye color. 00:20:13.860 --> 00:20:17.100 So it's just one simple marker that was to used. 00:20:18.020 --> 00:20:22.560 So for my research I'm incorporating genetics-- or genomics. 00:20:22.560 --> 00:20:27.720 So I'm interested in looking at the across the entire genome of an organism 00:20:27.720 --> 00:20:29.620 to see whether or not this pattern holds . 00:20:30.100 --> 00:20:34.560 And what I'm saying genomics what I mean is that instead of looking at one single marker 00:20:34.560 --> 00:20:37.420 I'm now looking at literally thousands of different markers. 00:20:37.420 --> 00:20:41.880 So not just the marker genetic marker for eyesight 00:20:41.880 --> 00:20:46.040 or for eye color but for everything that your genes are made of. 00:20:46.520 --> 00:20:53.680 And having more that information also provides more resolution for identifying genetic patterns. 00:20:55.840 --> 00:21:00.020 And so for my research I looked at two different species. 00:21:00.020 --> 00:21:05.620 So we have Manini which is which is what it's locally known as 00:21:05.620 --> 00:21:07.480 so Acanthurus triostegus 00:21:07.480 --> 00:21:11.300 and Ctenocheatus strigosus which is locally known as as Kole 00:21:11.300 --> 00:21:13.960 So these are two species of surgeon fishes. 00:21:13.960 --> 00:21:18.760 The refer to a surgeon fishes because they have to have this scalpel that's right at the base of their tail 00:21:18.760 --> 00:21:19.840 that they use as defense. 00:21:20.900 --> 00:21:24.280 and because of part of the same family 00:21:24.280 --> 00:21:31.480 I wanted to compare whether or not patterns that I see are indicative of this family in general. 00:21:31.880 --> 00:21:34.820 So there are PLD or pelagic larval duration. 00:21:34.820 --> 00:21:38.640 So the amount of time that they stay in the water column as larvae. 00:21:38.640 --> 00:21:43.960 It's kind of similar so for a Manini it can go up to about 70 days 00:21:43.960 --> 00:21:46.140 wherein Kole it can go about to 60 00:21:47.060 --> 00:21:50.460 And so Manini is down throughout the entire Indo-Pacific. 00:21:50.460 --> 00:21:53.880 So it's found throughout all of the Indian Ocean and all of the Pacific. 00:21:53.880 --> 00:21:57.700 And Kole is a Hawaiian endemic so it's only found Hawaii. 00:21:58.640 --> 00:22:02.720 So having that sort of different distribution pattern I also found interesting 00:22:02.720 --> 00:22:07.120 or wanted to investigate how that would influence their dispersal connectivity. 00:22:07.860 --> 00:22:12.700 And so for Manini is, an example, because it's found throughout two oceans 00:22:12.700 --> 00:22:16.660 it must be highly dispersive or I would predict that would be highly dispersive. 00:22:16.660 --> 00:22:21.680 meaning that the islands might be more highly connected 00:22:21.680 --> 00:22:25.400 than say Kole, which is a Hawaiian endemic. 00:22:26.360 --> 00:22:32.260 So what's but could have happened with Kole and other Hawaiian endemics is that 00:22:32.260 --> 00:22:39.720 it was its sister species or the most the most closely related species that exists there 00:22:39.720 --> 00:22:41.740 is found in the Pacific Ocean. 00:22:42.580 --> 00:22:47.440 Some random event brought it to Hawaii and it speciated into its own species 00:22:47.440 --> 00:22:48.820 where it became Kole. 00:22:49.960 --> 00:22:54.000 But it must be low dispersing because it's not able to get back to the Pacific 00:22:54.010 --> 00:22:58.000 and the Pacific wasn't able to retain a connection with Hawaii. 00:22:58.000 --> 00:23:01.600 So it allowed it to evolve into its own different species. 00:23:03.240 --> 00:23:07.020 And so I'm my question is do these species show similar connectivity patterns 00:23:07.020 --> 00:23:08.560 across the Hawaiian Archipelago. 00:23:09.980 --> 00:23:12.940 And so previous study that's been done looking at Kole 00:23:12.940 --> 00:23:16.040 collected samples from across the Hawaiian Archipelago. 00:23:16.040 --> 00:23:17.200 This is by Jeff (?) 00:23:17.920 --> 00:23:20.120 And it found widespread connectivity. 00:23:20.120 --> 00:23:25.460 So from the Big Island all the way to Kure for the most part it was one large population 00:23:26.060 --> 00:23:29.240 except for between Pearl and Hermes Maro Reef 00:23:29.240 --> 00:23:33.440 So for some reason that we still don't have an explanation for, 00:23:33.440 --> 00:23:38.760 the populations there are genetically distinct from everywhere else on Hawaii. 00:23:39.480 --> 00:23:44.440 So if you were to take an individual from Pearl and Hermes it's more closely related to Maro Reef. 00:23:44.980 --> 00:23:51.400 if you take a (?) Kure it's more closely related to individual on the Big Island of Hawaii 00:23:51.400 --> 00:23:53.220 than it is to s Pearl and Hermes. 00:23:53.780 --> 00:23:57.020 And again we don't have a really good explanation for that. 00:23:57.020 --> 00:23:59.780 But it is a pattern that we do see in other species. 00:24:00.600 --> 00:24:05.040 And remember this is taken off of just using the single genetic marker. 00:24:05.040 --> 00:24:08.600 So when we do a genomics framework 00:24:08.600 --> 00:24:13.620 what we find is that each different island is its own unique population. 00:24:14.420 --> 00:24:18.500 And what this means, in terms of conservation or management 00:24:18.640 --> 00:24:22.700 is that if you were to over-fish the Big Island, that's it. It's not coming back. 00:24:22.700 --> 00:24:26.340 if you]were to over-fish Maui, it's not going to be reseeded by anywhere else. 00:24:26.340 --> 00:24:29.600 and that's where every single island across Hawaiian Archipelago. 00:24:30.020 --> 00:24:37.240 So in this circumstance the Papahanaumokuakea is not reseeding the main Hawaiian Islands. 00:24:37.240 --> 00:24:44.320 And more so, each of the islands in the main Hawaiian Islands has to be able to manage it 00:24:44.320 --> 00:24:45.660 on their own. 00:24:47.280 --> 00:24:51.460 If they want to ensure a long term sustainability. 00:24:53.660 --> 00:24:55.520 Now moving forward with Manini. 00:24:55.520 --> 00:25:00.440 A previous study that was done only looked at the Big Island and Oahu 00:25:00.440 --> 00:25:04.380 but they found that those were distinct populations. 00:25:05.240 --> 00:25:09.040 And so I again incorporated genomics framework 00:25:09.040 --> 00:25:15.080 and what we found shows a really different pattern than what we see in Kole. 00:25:15.960 --> 00:25:17.100 So here in Manini, 00:25:17.100 --> 00:25:21.840 we found that in the main Hawaiian Islands, each of the islands are genetically distinct. 00:25:23.200 --> 00:25:27.720 And then there's this large area from Nihoa all the way up to Midway Atoll 00:25:27.720 --> 00:25:30.020 that is one large genetic population. 00:25:30.740 --> 00:25:35.520 And then you have this population at Kure that is genetically distinct. 00:25:35.980 --> 00:25:41.620 and we also have Johnston Atoll which we got samples for which is directly like 00:25:41.620 --> 00:25:44.300 five hundred miles south of Hawaii. 00:25:44.900 --> 00:25:48.280 And we that shows Laura 00:25:48.280 --> 00:25:53.280 that shows to be grouping with a large area in the northwest Line Islands. 00:25:55.280 --> 00:25:56.980 This group is grouping with that group. 00:25:57.940 --> 00:26:02.280 And so that might be a source of biodiversity from the rest of the Pacific. 00:26:02.280 --> 00:26:04.540 So the rest of the Pacific is extreme. 00:26:07.300 --> 00:26:08.760 Grouping with Johnston Atoll 00:26:08.760 --> 00:26:14.440 and at Johnson Atoll is grouping with the large area in the northwestern Hawaiian Islands. 00:26:15.140 --> 00:26:19.180 But again, in terms of management, what this means is that the main Hawaiian Islands are 00:26:19.180 --> 00:26:22.720 not being reseeded by the Papahanaumokuakea. 00:26:22.720 --> 00:26:28.520 And so for at least these species we need to look at it on the scale of an island 00:26:29.100 --> 00:26:33.580 in terms of identifying management strategies or conservation strategies for the 00:26:33.580 --> 00:26:36.700 long-term sustainability for both of these species. 00:26:38.200 --> 00:26:39.860 And so just to quickly conclude, 00:26:40.860 --> 00:26:45.720 genomics provides does provide a finer scale resolution for identifying connectivity patterns 00:26:45.720 --> 00:26:47.160 for both of these species. 00:26:48.420 --> 00:26:51.920 For Kole, we found Island by Island isolation. 00:26:51.920 --> 00:26:55.440 And this is the first account that we see this in a Hawaiian fish. 00:26:56.560 --> 00:27:00.120 And in Manini, we see widespread connectivity across the 00:27:00.130 --> 00:27:03.160 northwestern Hawaiian Islands. 00:27:03.160 --> 00:27:06.740 And island by Island isolation in the main Hawaiian Islands. 00:27:07.960 --> 00:27:10.500 And as I mentioned, so the the main Hawaiian Islands, 00:27:10.500 --> 00:27:13.940 both of these species are separate populations from the northwestern Hawaiian Islands. 00:27:13.940 --> 00:27:17.620 And so regardless of the protections of Papahanaumokuakea, 00:27:17.820 --> 00:27:20.540 they're not going to be reseeded by that protected region. 00:27:22.900 --> 00:27:24.880 So scaling down to the island, 00:27:26.460 --> 00:27:31.340 by going to a different Island scale, we require a much finer resolution. 00:27:31.800 --> 00:27:36.080 So we know based off of my research looking at it from an archipelago 00:27:36.080 --> 00:27:38.260 that each island is distinct. 00:27:38.260 --> 00:27:44.580 But you might even have more patterns or more connectivity 00:27:44.580 --> 00:27:47.980 finer resolution connectivity patterns at an island scale. 00:27:48.540 --> 00:27:52.740 And so the way we can do that is doing what we call a parentage analysis. 00:27:54.120 --> 00:27:58.620 And so a parentage analysis is it's really what it sort of sounds like. 00:27:58.620 --> 00:28:01.660 You're it's really similar to a paternity test. 00:28:01.660 --> 00:28:07.340 So if you guys are familiar with Maury Povich, this is a steady topic that he has on his show 00:28:08.100 --> 00:28:13.980 where so for a parentage analysis you have DNA from the father 00:28:13.980 --> 00:28:17.600 and if you have the child you can determine whether or not they're related to each other, 00:28:17.600 --> 00:28:20.420 And that's exactly what we're doing for fishes. 00:28:21.480 --> 00:28:26.560 And so the purpose of identifying this fine scale resolution or at least for the Hawaiian Islands 00:28:26.560 --> 00:28:30.900 is because there's a lot of fishing pressure, particularly on Oahu. 00:28:30.900 --> 00:28:34.140 and the fishing regulations are not really regulated. 00:28:36.540 --> 00:28:38.400 so having lack of regulations 00:28:39.020 --> 00:28:46.260 and that about a third of the populations of Hawaii participate in recreational fishing 00:28:48.560 --> 00:28:53.280 just provides a pretty steady source of pressure on marine resources. 00:28:53.280 --> 00:28:57.660 and so when and when I say recreational fishing what I'm referencing is 00:28:58.440 --> 00:29:04.550 fishing that's done for sport, fishing that's done for for leisure, and fishing that's done for sustenance. 00:29:04.550 --> 00:29:10.360 So people that use it to put dinner so for-- to put food on their plate 00:29:10.980 --> 00:29:15.440 and it's estimated at about a third of the total catch of fishes 00:29:15.440 --> 00:29:17.540 is attributed to recreational harvest. 00:29:17.540 --> 00:29:22.580 So as I mentioned, this isn't trivial pressure that's put on these on the marine 00:29:22.580 --> 00:29:24.160 these marine resources. 00:29:27.200 --> 00:29:33.080 And for Hawaii, particularly, because the fisheries aren't heavily regulated 00:29:33.080 --> 00:29:35.700 what could happen is that it could lead to over-fishing. 00:29:36.140 --> 00:29:42.700 and for a community that uses these sources, as a source of sustainability 00:29:42.700 --> 00:29:47.900 it could prohibit the long term viability for these resources. perfectly for future generations. 00:29:47.900 --> 00:29:52.560 And the build ultimately leads to the inability to maintain food security. 00:29:57.600 --> 00:30:01.720 So I've been working with a working group that the fished glow 00:30:01.720 --> 00:30:05.700 and that involves multiple scientists from the University of Hawaii 00:30:05.700 --> 00:30:08.160 where were identifying connectivity patterns 00:30:08.160 --> 00:30:11.740 across the windward side for the eastern side of Oahu. 00:30:11.740 --> 00:30:15.020 But we've taken it just beyond the genetic framework. 00:30:15.020 --> 00:30:17.660 So we've also incorporated oceanographic models 00:30:17.660 --> 00:30:19.020 we've looked at the ecology 00:30:19.020 --> 00:30:24.140 and we're also working with Conservation International 00:30:24.140 --> 00:30:29.760 to look at what's happened to the fish after they're taken. 00:30:29.760 --> 00:30:31.680 Where do they end up, who's eating them, 00:30:31.680 --> 00:30:34.920 are they being sold, are they being handed off to their friends and family. 00:30:35.480 --> 00:30:38.480 So we're trying to look at the where the fish are going 00:30:38.480 --> 00:30:40.880 from once they're spawned to once their consumed. 00:30:40.880 --> 00:30:46.140 And so for my portion of this as I'm interested in looking at the genetics aspect of it. 00:30:46.140 --> 00:30:49.620 and this is a project that was initiated by Native Hawaiian community leaders 00:30:49.620 --> 00:30:52.760 because they wanted to better understand how connectivity is 00:30:52.760 --> 00:30:55.080 on the at least the eastern side of Oahu. 00:30:55.080 --> 00:30:58.640 So they can begin implementing community based management. 00:30:58.640 --> 00:31:03.740 So rather than waiting for the state or other other laws to be passed 00:31:03.740 --> 00:31:10.020 or trying to do a ground-up effort where the community starts regulating fishing practices. 00:31:13.160 --> 00:31:17.740 And so for this we're look I used Manini as my study specimen. 00:31:17.740 --> 00:31:23.520 and so this species identified by community leaders as an important food fish. 00:31:24.460 --> 00:31:27.620 And it's abundant throughout Hawaii 00:31:27.620 --> 00:31:30.220 and it's heavily targeted by recreational fishers. 00:31:30.220 --> 00:31:34.140 So at any point you could go to a beach here on Oahu 00:31:34.140 --> 00:31:39.080 if you just wait around long enough you'll see somebody coming out with a bunch of Manini 00:31:39.080 --> 00:31:41.560 on that based speared. 00:31:44.660 --> 00:31:47.840 And so this is model based off of oceanographics. 00:31:48.900 --> 00:31:52.720 This was done by a Conor Roman and Margaret McManess. 00:31:52.720 --> 00:31:56.640 So each dot represents a single larvae. 00:31:56.640 --> 00:31:59.320 Each color represents where it originated from 00:31:59.860 --> 00:32:07.200 and you're seeing what's happening over a course of what's predicted to be their 00:32:07.200 --> 00:32:09.340 their time at the larval stage. 00:32:10.400 --> 00:32:16.980 So what you could quickly see is that a lot of the larvae, it really got swept offshore 00:32:16.980 --> 00:32:22.100 So those will likely die. To they're not going to contribute to the next generation. 00:32:22.760 --> 00:32:28.040 Although, there is a steady source steady amount of larvae that are hanging along the coastline. 00:32:28.960 --> 00:32:31.600 And if you look within Kaneoh Bay in particular, 00:32:31.600 --> 00:32:40.360 the yellow part or the southern part of the bay that's all being-- it's not moving. 00:32:40.360 --> 00:32:42.040 It's all staying within the bay. 00:32:42.040 --> 00:32:47.260 So a lot of the larvae that originates in the bay are staying within the bay. 00:32:47.980 --> 00:32:53.000 And then if I were to continue this long enough you'll see some of the larvae from Kailua 00:32:53.000 --> 00:32:55.140 sloshing into Kaneohe there. 00:32:55.140 --> 00:33:00.640 And and vice versa. A lot of the Kaneohe Bay starts wrapping around so the Kailua group. 00:33:01.580 --> 00:33:04.300 But looking at these type of models can give us a prediction 00:33:04.300 --> 00:33:08.540 of what we would see in terms of the genetics once we conduct our parentage analysis. 00:33:08.540 --> 00:33:12.120 And it helps us identify areas that we wanted to concentrate on in terms of 00:33:12.120 --> 00:33:16.780 trying to focus for our sampling efforts. 00:33:22.900 --> 00:33:25.820 Okay, and so the goal of looking at it from the eye on the scale 00:33:25.820 --> 00:33:28.020 is to identify a dispersal pathways 00:33:28.020 --> 00:33:30.420 and particularly the source-sink population. 00:33:30.700 --> 00:33:34.340 And also identify areas that are vulnerable for over-fishing. 00:33:35.900 --> 00:33:40.300 And so we collected from multiple areas around the island of Oahu. 00:33:40.300 --> 00:33:44.180 and also within different patch reefs of Kaneohe Bay. 00:33:44.180 --> 00:33:48.700 Sorry, the right side is an image of Kaneohe Bay. 00:33:48.700 --> 00:33:55.380 The blue is just shallow shallow water so something-- or less than 15 feet. 00:33:55.380 --> 00:34:01.780 Until you hit land. And yes so we collected from different patch reefs around that area. 00:34:03.640 --> 00:34:07.700 And so from the roughly 1200 samples that we collected from, 00:34:07.700 --> 00:34:12.440 we were able to assign 69 juveniles back to their parents. 00:34:12.440 --> 00:34:15.120 so about 11 percent success rate in doing so. 00:34:16.100 --> 00:34:21.500 and this is a general pattern of-- or this is what we've determined. 00:34:21.500 --> 00:34:26.640 So, there's a lot going on here so let me just quickly introduce to you what you're looking at. 00:34:26.640 --> 00:34:31.560 So each line represents a pathway of dispersal. 00:34:31.560 --> 00:34:36.580 So as I mentioned beginning, we know that the adults don't move that much 00:34:36.580 --> 00:34:42.880 and so the dispersal stage occurs at the lar-- the dispersal occurs at the larval stage. 00:34:42.880 --> 00:34:47.780 so if we know where the adults is from and we collect their child, 00:34:47.780 --> 00:34:53.540 we can draw a line saying that those two areas are absolutely, 100% connected. 00:34:53.540 --> 00:34:55.380 And that's what we're seeing here. 00:34:55.380 --> 00:35:00.820 So each dotted line or dashed line represents one event where we found one adult 00:35:00.820 --> 00:35:05.940 that if you follow the line where the aerial arrow finally ends at, 00:35:05.940 --> 00:35:09.540 that's where the lar-- their child settled at. 00:35:10.940 --> 00:35:15.400 The solid lines represent the defense all our lines are two to three events 00:35:15.400 --> 00:35:20.340 and the thicker lines are more than, greater than 14 individuals. 00:35:20.340 --> 00:35:24.720 Were shown to be to have that dispersal pathway. 00:35:25.500 --> 00:35:27.680 And then you also have these red boxes 00:35:28.560 --> 00:35:31.880 and these boxes indicate self recruitment. 00:35:31.880 --> 00:35:34.600 And what that means is that the adult was found in that area 00:35:34.600 --> 00:35:36.720 and the child was found in that same area. 00:35:36.720 --> 00:35:39.820 so the the child never left the area that it was spawned. 00:35:41.820 --> 00:35:44.940 And so you can see the overall pattern 00:35:44.940 --> 00:35:48.940 is that everything is ending up on the eastern side of Oahu. 00:35:50.080 --> 00:35:57.900 So the southern part of Oahu a lot of its wrapping around and ending up on the eastern side 00:35:58.940 --> 00:36:00.520 But if you follow a lot of the patterns 00:36:00.520 --> 00:36:03.960 you'll see the majority of individuals are actually entering Kaneohe Bay. 00:36:05.680 --> 00:36:10.840 Also you can see that despite the fact that we collected from the western and northern side, 00:36:10.840 --> 00:36:16.280 there wasn't a lot of recovery for parents or their juveniles 00:36:16.280 --> 00:36:19.020 or a lot of assignments of juveniles back to the parents. 00:36:19.020 --> 00:36:22.740 And there's a couple of reasons that we predict to be the cause for this. 00:36:23.480 --> 00:36:29.620 So we have two really strong currents that wrap around Oahu. 00:36:29.620 --> 00:36:34.920 So we have the Hawaii Lee Current which runs south to northwest 00:36:36.000 --> 00:36:38.220 across the western side of Oahu 00:36:38.220 --> 00:36:42.660 and the North Hawaiian Ridge Current which runs north of Oahu. 00:36:42.660 --> 00:36:46.700 And what we predict is that these currents are strong enough 00:36:46.700 --> 00:36:52.060 for that any individual that is spawned in the western or northern side 00:36:52.060 --> 00:36:54.420 is likely getting swept offshore. 00:36:55.520 --> 00:37:02.860 And this wasn't is not going to hold for every individual because we do see Manini 00:37:02.860 --> 00:37:04.800 on in those regions. 00:37:04.800 --> 00:37:09.900 But the overall pattern is likely anything that spawn is getting swept offshore. 00:37:09.900 --> 00:37:13.520 so for future research what we'd want to do is collect from Kauai 00:37:13.520 --> 00:37:18.520 and see whether or not we're able to recover juvenile and parents from Kauai. 00:37:21.180 --> 00:37:23.480 And so looking into Kaneohe Bay, 00:37:24.500 --> 00:37:28.500 again we found six instances of local retention within Kaneohe Bay. 00:37:28.500 --> 00:37:33.180 So the species or individuals that respond in the area stayed in Kaneohe Bay. 00:37:33.180 --> 00:37:38.020 and we had two instances of dispersal outside that originated from Kaneohe Bay 00:37:38.020 --> 00:37:39.340 that went northwards. 00:37:40.020 --> 00:37:46.460 But you can see that about 30-- 37 individuals that were originated outside of Kaneohe Bay 00:37:46.460 --> 00:37:48.060 entered Kaneohe Bay. 00:37:48.440 --> 00:37:50.760 and so what telling us is that 00:37:52.420 --> 00:37:59.560 for Kaneohe Bay, it's relying it's reliant on other populations outside of Kaneohe Bay 00:37:59.560 --> 00:38:05.820 for a sustainable population, particularly these two areas of Kailua, right here. 00:38:05.820 --> 00:38:09.780 And Laie so Laie from the north and Kailua from the South. 00:38:09.780 --> 00:38:16.640 Those are the two populations of at large or largest influences of recruitment in Kaneohe Bay. 00:38:17.240 --> 00:38:22.560 So if we went to inform proper- if my recommendation would for management would be 00:38:22.560 --> 00:38:26.320 if we want to preserve the population of Kaneohe Bay, 00:38:26.320 --> 00:38:29.980 we need to work with communities in Kailua and Laie 00:38:29.980 --> 00:38:35.700 because those are the source populations for the individuals that we see in Kaneohe Bay. 00:38:38.640 --> 00:38:41.380 And what we can also see from looking at an island scale 00:38:41.380 --> 00:38:44.960 is that the majority of larvae don't disperse far. 00:38:44.960 --> 00:38:48.760 So the despite the ability-- so despite the fact that they're in the water column 00:38:48.760 --> 00:38:51.720 or have a potential to be in the water column for up to 70 days, 00:38:51.720 --> 00:38:53.440 they're really not moving that far. 00:38:53.440 --> 00:38:57.200 And there's a lot of indication of local retention what just sort of 00:38:57.200 --> 00:39:00.060 provides further evidence that they're not moving. 00:39:00.740 --> 00:39:06.220 And on the eastern side of Oahu it's a source of recruitment for a large parts of the island itself. 00:39:06.220 --> 00:39:11.300 and and Kaneohe Bay in particular is dependent on a recruitment from outside of the bay. 00:39:12.900 --> 00:39:15.180 So by looking across different spatial scales, 00:39:15.180 --> 00:39:17.320 we're able to answer a couple of different questions. 00:39:17.320 --> 00:39:20.080 So looking at it from the archipelago scale, 00:39:20.080 --> 00:39:23.580 you can see that genomics provides a finer scale resolution. 00:39:23.580 --> 00:39:27.100 And we can also see that the main Hawaiian Islands are separate populations 00:39:27.100 --> 00:39:28.740 from the northwestern off Hawaiian Islands. 00:39:29.760 --> 00:39:32.400 And the particular Papahanaumokuakea. 00:39:32.980 --> 00:39:34.800 And then looking at it from the island scale, 00:39:34.800 --> 00:39:38.080 we can identify even finer scale pathways of dispersals 00:39:38.080 --> 00:39:42.220 and origins of recruitment and we can provide it on an island level 00:39:42.220 --> 00:39:46.320 what we would need to do in terms of management and conservation to protect this resource. 00:39:49.000 --> 00:39:53.120 And to sort of wanted to wrap that up or that aspect up and just say that 00:39:53.120 --> 00:39:56.340 there's a lot of threats that are currently happening in our world. 00:39:56.340 --> 00:40:01.240 so over-fishing, overpopulation, this is an example of Waikiki Beach, 00:40:01.240 --> 00:40:05.220 there's a lot of offshore pollutants that are entering our water systems, 00:40:05.220 --> 00:40:08.180 and the reefs themselves are just degradated all around. 00:40:08.180 --> 00:40:12.100 so the main Hawaiian Islands this is a very, in the lower left corner of the screen, 00:40:12.100 --> 00:40:13.940 it's just a good example of what you would see. 00:40:13.940 --> 00:40:15.780 so a lot of the reefs here are dead. 00:40:15.780 --> 00:40:21.140 And so it's important to with all the changes that are currently happening 00:40:21.140 --> 00:40:22.900 in addition to climate change 00:40:23.400 --> 00:40:30.080 to find areas or find appropriate methods to protect the resources that we have. 00:40:30.080 --> 00:40:35.280 And just to bring awareness to a lot of the threats that are occurring in our world. 00:40:37.560 --> 00:40:39.560 And so just on the lighter note, 00:40:40.440 --> 00:40:43.800 one of the fun things that I like to talk about are just general problems 00:40:43.800 --> 00:40:45.400 in the field that we encounter. 00:40:45.400 --> 00:40:48.840 So from a genetics perspective, 00:40:48.840 --> 00:40:51.480 there's a lot of things that we encounter that 00:40:51.480 --> 00:40:55.400 I don't know if a lot of people are inherently aware of 00:40:55.400 --> 00:40:58.860 when it comes to doing genetics research research. 00:40:58.860 --> 00:41:03.600 So one of the biggest issues that we find is that there's this mis-identifications in the field. 00:41:03.600 --> 00:41:09.140 So I presented two species that look pretty similar, 00:41:09.140 --> 00:41:13.300 but just because they look similar might not meet up with the same species. 00:41:13.940 --> 00:41:17.280 So I wanted to do a quick example and I have a poll question 00:41:17.280 --> 00:41:20.160 I presented so, Hannah, if you want to get that ready. 00:41:20.920 --> 00:41:24.460 what I want to propose to you is how many species do you see. 00:41:25.500 --> 00:41:28.960 and this is a group of cardinalfish is what we're looking at 00:41:32.180 --> 00:41:32.840 and 00:41:32.840 --> 00:41:38.420 - I'll give the audience time to see if they can figure out how many species before launching the poll. 00:41:41.200 --> 00:41:42.480 - So yeah, so 00:41:42.480 --> 00:41:45.240 when we're in the water and we're collecting 00:41:45.240 --> 00:41:50.360 we really don't we only have a very limited amount of time to collect the species that we're interested in. 00:41:50.360 --> 00:41:53.340 And if you're not familiar with your study organism 00:41:53.340 --> 00:41:56.440 or the intricacies that the differences between the different species, 00:41:56.440 --> 00:41:58.460 you might collect the wrong species. 00:41:58.460 --> 00:42:00.200 And since we're looking at genetics, 00:42:00.200 --> 00:42:06.140 if you introduce it that's the wrong species instead of genetics portion, 00:42:08.700 --> 00:42:11.160 it'll really mess up the patterns that you're gonna see. 00:42:11.160 --> 00:42:14.380 So I'll give you time to think about it. 00:42:16.540 --> 00:42:18.500 You tell me how many species you see. 00:42:19.940 --> 00:42:25.220 - We are at sixty percent of people voting. I'll give it a few more seconds. 00:42:31.780 --> 00:42:33.040 All right. 00:42:33.520 --> 00:42:36.520 We are at eighty percent of people voting. 00:42:37.240 --> 00:42:39.220 I'll share the results. 00:42:40.220 --> 00:42:41.760 Richard, how do you think they did? 00:42:42.180 --> 00:42:45.560 Most people think that there's three and four species. 00:42:45.560 --> 00:42:50.700 And I'm really curious. I wish we're in person because I'm curious what what it is about the 00:42:50.700 --> 00:42:52.900 those images that would make you think that. 00:42:52.900 --> 00:42:55.260 But, the answer is three species. 00:42:58.300 --> 00:43:00.480 So those are the three different species that exist. 00:43:01.160 --> 00:43:06.180 So I'll tell you what what it is that causes or where you go you can identify these differences. 00:43:06.180 --> 00:43:11.220 So in the upper left corner you in the blue you have one species. 00:43:11.220 --> 00:43:14.120 well looking at the top row there's three different species there. 00:43:14.880 --> 00:43:20.640 One of the bigger ones, at least for me, is a position of that dot that's on the tail. 00:43:21.060 --> 00:43:26.000 So in the leftmost one it's a dot that's right cent-- right in the center. 00:43:26.000 --> 00:43:29.940 And the middle one it's offset above the lateral line. 00:43:30.840 --> 00:43:35.280 And in the right one, it's actually kind of off-center if you look closely 00:43:35.280 --> 00:43:40.740 it's not as the fine of the circle. Sort of sort of like fades out as you move away from it. 00:43:40.740 --> 00:43:43.800 But addition to that circle you can look at the lateral line. 00:43:43.800 --> 00:43:49.120 So the lateral line on the left left left individual starts out thick and it tapers out. 00:43:49.120 --> 00:43:51.840 Same with the one in the middle, but the one on the right, 00:43:51.840 --> 00:43:55.140 it doesn't taper out. It's a solid line all the way through. 00:43:56.540 --> 00:43:59.800 So understanding or just being aware of the subtle differences 00:43:59.800 --> 00:44:02.940 it's important when you're collecting individuals for genetic analysis. 00:44:03.820 --> 00:44:09.260 But in addition to this group, this so this is a family called cardinalfish. 00:44:11.180 --> 00:44:12.660 They're nocturnal fishes. 00:44:12.660 --> 00:44:14.440 So you only really see them at night. 00:44:14.440 --> 00:44:16.900 And so the other problem I have with this group 00:44:16.900 --> 00:44:20.640 is that at night they all lose all those color powder those color patterns. 00:44:20.640 --> 00:44:23.060 So they're also known as iridescent fish. 00:44:23.060 --> 00:44:24.780 So they're iridescent at night. 00:44:24.780 --> 00:44:26.840 So if you go out at night, you wouldn't see those dots. 00:44:26.840 --> 00:44:28.780 You wouldn't really see the lateral line 00:44:28.780 --> 00:44:33.200 and so you have to look at some other differences. 00:44:33.200 --> 00:44:35.040 So in the lower right corner, 00:44:35.040 --> 00:44:41.240 there's if you look at the dorsal fin, before the first dorsal fin, 00:44:41.240 --> 00:44:45.740 the color is some can sometimes be like brown or yellow or or or orange. 00:44:45.740 --> 00:44:48.200 That's not apparent on the other two. 00:44:48.200 --> 00:44:51.240 Whereas if I looked at the other two at night, 00:44:51.240 --> 00:44:53.540 I wouldn't be able to know what species I'm looking at. 00:44:54.040 --> 00:44:56.580 But yeah, I thought that was a fun exercise. 00:44:57.220 --> 00:45:00.760 In addition, there could be incorrect labeling. 00:45:00.760 --> 00:45:08.240 So we collect-- it's rare where where we go into the field and we're looking at one species in particular. 00:45:08.240 --> 00:45:10.860 We're usually collecting hundreds of different species. 00:45:11.440 --> 00:45:13.960 And they all go into these little small tubes 00:45:13.960 --> 00:45:17.840 and we don't see the fish themselves. We only see a piece of their fin. 00:45:17.840 --> 00:45:23.420 So if you label the tube wrong and send us the wrong species, I'm not going to know that 00:45:23.420 --> 00:45:25.120 until way way way down 00:45:25.120 --> 00:45:27.920 and we're doing the analysis and I see that there's something wrong going on. 00:45:27.920 --> 00:45:33.820 For this individual is a different species from the rest of my samples that I'm working on. 00:45:35.460 --> 00:45:38.680 Another problem is doing is inadequate sample sizes. 00:45:38.680 --> 00:45:46.600 So you don't want-- you can't just use one individual to characterize the genetics for a population. 00:45:46.600 --> 00:45:50.500 So just as an example you can't take one individual from Germany 00:45:50.500 --> 00:45:53.720 and say that he's representative of all the individuals in Germany. 00:45:53.720 --> 00:45:59.600 See what you do is you would take multiple samples from across different areas of Germany. 00:45:59.600 --> 00:46:03.220 And that's how 23andMe works is you're taking 00:46:03.760 --> 00:46:08.640 a pool of a bunch of individuals in the region 00:46:08.640 --> 00:46:12.820 and getting an idea of the genetic diversity that exists in that area. 00:46:13.740 --> 00:46:16.480 And we also want to make sure that the DNA remains stable. 00:46:16.480 --> 00:46:21.660 so at times we're at sea for 12 hours a day, 00:46:21.660 --> 00:46:24.940 so the heat itself could degrade the DNA. 00:46:24.940 --> 00:46:29.800 so you have to have the proper preservation solution and avoid extreme heat. 00:46:29.800 --> 00:46:32.360 And there's also the prospect of contamination. 00:46:32.360 --> 00:46:36.180 There's been several times where I've amplified like an ant or a butterfly 00:46:36.180 --> 00:46:41.000 or even like my own my own or a human DNA. 00:46:41.000 --> 00:46:43.720 and when I when I've done my my lab experiments. 00:46:46.500 --> 00:46:51.160 So with just an addition to showing you how genetics could be used in terms of management, 00:46:51.160 --> 00:46:56.340 I, I wanted to highlight that genetics is that useful to for addressing a variety of questions. 00:46:56.340 --> 00:46:59.480 I didn't really go into the aspects of how it could be used for evolutionary 00:46:59.480 --> 00:47:01.980 or informing evolutionary theory 00:47:01.980 --> 00:47:04.740 or and identify mechanisms that promote evolution. 00:47:04.740 --> 00:47:08.660 But I hope I showed that it is a tool to properly 00:47:08.660 --> 00:47:13.440 to be able to inform management conservation at least for marine resources. 00:47:14.740 --> 00:47:19.280 And as technology increases our ability answer these questions and identify these patterns 00:47:19.280 --> 00:47:21.340 just to become more more robust. 00:47:23.900 --> 00:47:27.460 And that's kind of it. I do want to thank the the National Marine Sanctuaries 00:47:28.080 --> 00:47:30.320 for all of the support. So 00:47:31.100 --> 00:47:34.580 people interested in joining or entering grad school, 00:47:34.580 --> 00:47:39.060 the Dr. Nancy Foster Scholarship is an awesome program to be a part of. 00:47:40.280 --> 00:47:43.220 I've gained so many skills from it 00:47:43.220 --> 00:47:46.800 and it's a really great network of people to be working with. 00:47:47.620 --> 00:47:51.740 And and that's it. And I don't have time for questions. 00:47:52.260 --> 00:47:54.460 - Yeah, we have time for some questions. 00:47:54.460 --> 00:47:55.720 And thank you so much, Richard. 00:47:55.720 --> 00:47:58.920 We have a question coming in from Kim. 00:47:58.920 --> 00:48:04.840 and she is wondering how different, in terms of percentage, do the species have to be 00:48:04.840 --> 00:48:06.360 to consider them separate. 00:48:06.360 --> 00:48:08.280 And can they interbreed? 00:48:09.780 --> 00:48:14.640 So percentage is different depending on the gene that you're looking at. 00:48:14.640 --> 00:48:20.660 So a common gene that was used to to look at differences of species 00:48:20.660 --> 00:48:22.060 would be 00:48:23.120 --> 00:48:28.040 it's called C-O-1 set up from oxide-- 00:48:28.040 --> 00:48:33.760 The CO1. That's the common and historically is being used 00:48:35.020 --> 00:48:38.880 and it different for different groups too. It's a really complicated question to ask. 00:48:38.880 --> 00:48:42.560 To answer. So for fishes is about two percent difference 00:48:42.560 --> 00:48:44.580 would be enough to say that they're different species. 00:48:44.580 --> 00:48:47.520 But it wouldn't be simply looking at genetics, either. 00:48:48.440 --> 00:48:51.460 What you'd be you would also want to know sort of the ecology 00:48:51.460 --> 00:48:56.420 the individual. So do they overlap? Are they found in the same region? Can they breed? 00:48:58.260 --> 00:49:02.060 So species that are closely related can breed, sometimes. 00:49:02.800 --> 00:49:05.760 And that's where you get hybrids. And that's another area that I work in. 00:49:06.520 --> 00:49:07.420 and 00:49:08.520 --> 00:49:10.400 But it doesn't happen all of the time. 00:49:10.400 --> 00:49:15.100 So it's that's a really complex complex question to answer because 00:49:15.100 --> 00:49:17.320 it largely depends on the group that you're working with. 00:49:17.880 --> 00:49:21.880 So as another example is I've worked with a group of Angela fishes. 00:49:22.500 --> 00:49:23.240 and 00:49:24.700 --> 00:49:28.220 if you look at the genetics or less than one percent different, 00:49:28.220 --> 00:49:30.480 but physically they are completely different, 00:49:30.480 --> 00:49:31.960 they don't look anything alike. 00:49:31.960 --> 00:49:35.720 So then the question, you know, and depending on how you define a species 00:49:35.720 --> 00:49:37.080 comes into play as well. 00:49:37.080 --> 00:49:41.380 So I propose for that circumstance that they were subspecies. 00:49:41.380 --> 00:49:45.040 Rather than trying to say that they're actually two very different. 00:49:45.040 --> 00:49:46.960 Because they have the ability to interbreed, 00:49:46.960 --> 00:49:50.080 they just don't encounter each other in the wild. 00:49:50.080 --> 00:49:52.860 And they're very different physically. 00:49:52.860 --> 00:49:56.380 so you could look at them and say that they're different but the genetics wouldn't tell you that. 00:49:57.540 --> 00:49:59.480 - Thank you for that, Richard. 00:49:59.480 --> 00:50:02.740 And we have another question coming in from Renee 00:50:02.740 --> 00:50:07.240 and it's actually two parts so maybe you'll be able to bring up the research she's requesting. 00:50:07.240 --> 00:50:10.820 Now, but if not, we can try to send it out after the recording. 00:50:10.820 --> 00:50:16.260 She is wondering: Have you identified any common trait in fish species 00:50:16.260 --> 00:50:20.795 that are able to cross the barrier between the Big Island and Maui? 00:50:20.795 --> 00:50:27.660 And then the second question is: Is there, can you provide a list of the 24 species you looked at 00:50:27.660 --> 00:50:32.540 in determining that barrier and which crossed, versus which didn't. 00:50:34.160 --> 00:50:38.320 - Yeah, so I, I don't have the list on hand, but I'm happy to send that out. 00:50:38.320 --> 00:50:47.700 and that list has been updated quite a bit so I can maybe add that to the list as well. 00:50:47.700 --> 00:50:53.060 But in terms of common features for species that are able to cross, 00:50:53.060 --> 00:50:55.760 the biggest one is that they're pelagic fishes. 00:50:55.760 --> 00:50:58.500 or pelagic. It's not even fishes, but they're pelagic. So 00:50:58.920 --> 00:51:01.760 they have the ability to swim fast and strong. 00:51:01.760 --> 00:51:09.560 So an example would be like: dolphins, sharks, there's Jacks. 00:51:09.560 --> 00:51:16.880 So a lot of the larger organisms that aren't influenced by the the large currents 00:51:16.880 --> 00:51:18.120 that exist between the different regions. 00:51:18.120 --> 00:51:24.300 But those would be to the-- that would be probably the main main trait. 00:51:26.940 --> 00:51:32.540 - Great, thank you so much, Richard. That those are the questions that we have gotten so far. 00:51:32.540 --> 00:51:37.700 So with that I'm gonna hand it back over to Claire to wrap up the presentation and 00:51:37.700 --> 00:51:39.700 thank you again, Richard. That was great. 00:51:39.700 --> 00:51:41.700 - You're welcome. 00:51:41.960 --> 00:51:44.600 - Thanks so much, Richard. Very informative. 00:51:44.600 --> 00:51:47.440 And I just do a few wrap up things here. 00:51:47.440 --> 00:51:52.140 So if you have colleagues that perhaps registered or dismiss this completely 00:51:52.140 --> 00:51:55.680 or if you yourself wanna see the archive of today's presentation, 00:51:55.680 --> 00:52:00.430 you can worry about writing down that long government URL. 00:52:00.430 --> 00:52:02.940 It'll be sent to you in a follow-up email. 00:52:02.940 --> 00:52:07.700 And it usually takes us about a week to get the archive posted online. 00:52:07.700 --> 00:52:11.820 And most certainly if you have any specific questions for Richard 00:52:11.820 --> 00:52:14.280 that weren't put into today's presentation, 00:52:14.280 --> 00:52:18.800 you can contact us at the sanctuary.education@NOAA.gov 00:52:19.620 --> 00:52:25.460 I wanted to also let everyone know that you'll be receiving a certificate of attendance 00:52:25.460 --> 00:52:26.980 for today's session. 00:52:26.980 --> 00:52:32.800 And this is generally something that we provide all attendees. 00:52:32.800 --> 00:52:37.800 We've even for those if you also have friends or colleagues that plan to watch the archive, 00:52:37.800 --> 00:52:42.200 occasionally people will email us saying "hey, I watched the archive to the end. 00:52:42.200 --> 00:52:46.320 I would love to get my certificate of attendance." And we do allow for that, as well. 00:52:47.100 --> 00:52:52.300 And just to give a little promo for two of our upcoming presentations, 00:52:52.300 --> 00:52:57.080 we have Dr. James A. Morris jr. who works for NOAA. 00:52:57.080 --> 00:52:59.500 He'll be with us presenting ocean reports, 00:52:59.500 --> 00:53:03.440 which is this comprehensive web-based spatial assessment tool. 00:53:03.440 --> 00:53:08.280 And it's the first ever that's comprehensive for the United States, UEC. 00:53:08.280 --> 00:53:14.520 and so we are not only going to hear from James what this ocean reports tool is, 00:53:14.520 --> 00:53:17.280 but we're gonna be soliciting a lot of feedback 00:53:17.280 --> 00:53:21.640 from our participants, primarily formal and informal educators, 00:53:21.640 --> 00:53:26.400 on how they can envision utilizing this robust online product. 00:53:26.400 --> 00:53:31.060 Because we do potentially have funds to make educational tools 00:53:31.060 --> 00:53:32.940 using this ocean reports. 00:53:32.940 --> 00:53:35.960 So that'll be on October 8th, so just a few weeks. 00:53:36.500 --> 00:53:40.340 And we're still looking for presentation for November but 00:53:40.340 --> 00:53:44.880 in December we'll be targeting one of our sweet water sanctuaries. 00:53:44.880 --> 00:53:48.886 Our Thunder Bay National Marine Sanctuary. 00:53:48.886 --> 00:53:53.440 and we'll be exploring sinkholes with Mr. Biddanda 00:53:53.500 --> 00:53:59.960 and so he'll teach everybody about a decade of exploration of life and Lake Huron's sinkholes 00:53:59.960 --> 00:54:04.620 and how these five findings have relevance to the Earth's current 00:54:04.620 --> 00:54:08.340 biologic and physiologic diversity. 00:54:08.340 --> 00:54:12.460 So look forward to you potentially joining some of these future webinars. 00:54:12.460 --> 00:54:18.100 We do have a very short evaluation that follows today's presentation. 00:54:18.100 --> 00:54:20.300 So when you close out of GoToWebinar 00:54:20.300 --> 00:54:25.320 if you would please take three minutes to answer those questions. 00:54:25.320 --> 00:54:33.760 It is extremely helpful for us to get a sense of evaluating our distance learning programs. 00:54:33.760 --> 00:54:37.040 And there's an opportunity to provide feedback 00:54:37.040 --> 00:54:43.240 on the actual session as well as topics that you would like to see in the future presentation. 00:54:43.240 --> 00:54:46.940 So with that, I will be concluding today's webinar. 00:54:46.940 --> 00:54:50.620 Take those three minutes and do the evaluation. It'd be greatly appreciated. 00:54:50.620 --> 00:54:56.780 And thanks for Richard for a fantastic presentation on the connectivity 00:54:56.780 --> 00:54:59.900 genetic connectivity these species in Hawaii. 00:54:59.900 --> 00:55:01.700 You're welcome. 00:55:01.700 --> 00:55:05.900 This concludes today's webinar. Thanks for joining us.