1 00:00:01,440 --> 00:00:03,940 [Narrator] This conference will now be recorded. 2 00:00:06,120 --> 00:00:09,690 Alright, good morning, everybody. 3 00:00:09,690 --> 00:00:14,690 Welcome to the last of our 2020 EcoFOCI seminar series. 4 00:00:15,920 --> 00:00:18,520 I am Heather Tabisola, I am the co-lead 5 00:00:18,520 --> 00:00:21,150 with Jens Nielsen, who is also on video today 6 00:00:21,150 --> 00:00:23,320 and he's gonna be taking over today. 7 00:00:23,320 --> 00:00:26,010 This seminar is part of NOAA's EcoFOCI 8 00:00:26,010 --> 00:00:29,120 biannual seminar series, focused on the ecosystems 9 00:00:29,120 --> 00:00:31,470 of the North Pacific Ocean, Bering Sea, 10 00:00:31,470 --> 00:00:34,150 and U.S. Arctic to improve understanding 11 00:00:34,150 --> 00:00:37,520 of ecosystem dynamics and applications of that understanding 12 00:00:37,520 --> 00:00:40,750 to the management of living marine resources. 13 00:00:40,750 --> 00:00:43,150 Since October 21st 1986, 14 00:00:43,150 --> 00:00:45,370 the seminar has provided an opportunity 15 00:00:45,370 --> 00:00:48,320 for research scientists and practitioners to meet, 16 00:00:48,320 --> 00:00:51,640 present, develop their ideas and provoke conversations 17 00:00:51,640 --> 00:00:54,670 on subjects pertaining to fisheries oceanography 18 00:00:54,670 --> 00:00:57,783 or regional issues in Alaska's marine ecosystems. 19 00:00:58,900 --> 00:01:00,720 You can visit the EcoFOCI webpage 20 00:01:00,720 --> 00:01:05,613 for more information at www.ecofoci.noaa.gov. 21 00:01:06,620 --> 00:01:08,260 And again, we sincerely thank you guys 22 00:01:08,260 --> 00:01:11,270 for joining us today for the season 23 00:01:11,270 --> 00:01:14,430 as we continue kind of this all virtual seminar series. 24 00:01:14,430 --> 00:01:17,870 This is the last talk this season 25 00:01:19,070 --> 00:01:21,950 and our speaker lineup coming up can be found 26 00:01:21,950 --> 00:01:24,950 always by the OneNOAA science seminar series 27 00:01:24,950 --> 00:01:28,840 and also on the NOAA PMEL calendar of events. 28 00:01:28,840 --> 00:01:31,440 We are here Wednesdays at 10:00 AM 29 00:01:31,440 --> 00:01:34,840 and we should be beginning, probably March 3rd. 30 00:01:34,840 --> 00:01:37,793 So look for details forthcoming in the new year. 31 00:01:38,900 --> 00:01:41,140 Please double-check that your microphones are muted, 32 00:01:41,140 --> 00:01:43,920 that you are not on video, and then during the talk, 33 00:01:43,920 --> 00:01:46,600 please feel free to type your questions into the chat. 34 00:01:46,600 --> 00:01:48,500 Both Jens and I will be monitoring that 35 00:01:48,500 --> 00:01:50,900 and we'll address those at the end of the talks. 36 00:01:52,550 --> 00:01:55,280 And I'm going to hand this over to Jens 37 00:01:55,280 --> 00:01:59,100 and he's gonna introduce and lead seminar today. 38 00:01:59,100 --> 00:02:00,427 Jens, all you. 39 00:02:00,427 --> 00:02:01,773 Thank you Heather. 40 00:02:02,890 --> 00:02:04,880 Thank you, everyone for joining in. 41 00:02:04,880 --> 00:02:07,333 It's a pleasure to introduce Deana Crouser. 42 00:02:08,940 --> 00:02:12,700 Deana is a zooplankton ecologist in our EcoFOCI group. 43 00:02:12,700 --> 00:02:14,750 And she has a variety of projects focused 44 00:02:14,750 --> 00:02:18,380 on zooplankton identification quality assessment. 45 00:02:18,380 --> 00:02:20,090 She works on the chlorophyll analyses 46 00:02:20,090 --> 00:02:21,883 and data analysis of those data. 47 00:02:23,270 --> 00:02:24,820 She also knows three to four different 48 00:02:24,820 --> 00:02:26,193 programming languages, 49 00:02:27,410 --> 00:02:30,410 which is very helpful when you happen to be her officemate. 50 00:02:31,710 --> 00:02:35,320 And lately, she's been leading the group zooplankton 51 00:02:35,320 --> 00:02:37,560 imaging efforts which I think we'll hear a little bit 52 00:02:37,560 --> 00:02:40,870 more about today which is really, really cool work. 53 00:02:40,870 --> 00:02:42,610 Deana holds a degree in sonography 54 00:02:42,610 --> 00:02:46,230 from University of Washington, and she did her thesis work 55 00:02:46,230 --> 00:02:48,290 with Julie Keister and Daniel Grünbaum 56 00:02:49,790 --> 00:02:52,790 and researched effect of low oxygen levels 57 00:02:52,790 --> 00:02:57,193 and ocean acidification on copepod size distributions. 58 00:02:58,450 --> 00:03:01,170 I'm gonna swing it over to you Deana right now 59 00:03:01,170 --> 00:03:05,040 because I know you're gonna talk about that, so go ahead. 60 00:03:05,040 --> 00:03:06,690 The floor is yours. 61 00:03:06,690 --> 00:03:10,450 Thank you, Jens, hi, as Jens said, I'm Deana Crouser. 62 00:03:10,450 --> 00:03:12,783 I'm a zooplankton ecologist here at NOAA. 63 00:03:17,290 --> 00:03:20,120 Today, I'm going to talk to you about the effects 64 00:03:20,120 --> 00:03:22,740 of climate change on zooplankton communities 65 00:03:22,740 --> 00:03:24,410 from my undergraduate research 66 00:03:24,410 --> 00:03:27,410 at the University of Washington to my current work here 67 00:03:27,410 --> 00:03:29,633 at NOAA's Alaska Fisheries Science Center. 68 00:03:31,840 --> 00:03:35,560 So as Jens said, I graduated from University of Washington 69 00:03:35,560 --> 00:03:39,060 with my BS in oceanography in 2019. 70 00:03:39,060 --> 00:03:41,360 And for the first portion of this seminar, 71 00:03:41,360 --> 00:03:44,510 I'm going to present my undergraduate senior thesis 72 00:03:44,510 --> 00:03:47,410 which investigated the effects of low oxygen levels 73 00:03:47,410 --> 00:03:50,573 on copepod size distribution with depth in Hood Canal. 74 00:03:53,310 --> 00:03:56,830 Hypoxia, a condition in which there is a decrease 75 00:03:56,830 --> 00:03:59,560 in the concentration of oxygen availability 76 00:03:59,560 --> 00:04:02,865 below two milligrams per liter is, and we're narrow 77 00:04:02,865 --> 00:04:05,290 on that in marine life. 78 00:04:05,290 --> 00:04:08,390 Incidences of coastal hypoxia due to changes 79 00:04:08,390 --> 00:04:11,420 in nutrient loading have steadily been increasing 80 00:04:11,420 --> 00:04:12,860 over the years. 81 00:04:12,860 --> 00:04:17,500 According to recent studies, 415 oxygen-depleted 82 00:04:17,500 --> 00:04:20,323 coastal systems have been identified worldwide. 83 00:04:21,360 --> 00:04:25,630 This condition is exacerbated by global warming 84 00:04:25,630 --> 00:04:30,630 through increased stratification and warming waters. 85 00:04:30,700 --> 00:04:34,200 Many coastal areas experienced seasonal hypoxia 86 00:04:34,200 --> 00:04:37,650 due to high primary production, as well as stratification 87 00:04:37,650 --> 00:04:40,430 which naturally leads to oxygen depletion 88 00:04:40,430 --> 00:04:44,630 as algal blooms decay in late summer and autumn. 89 00:04:44,630 --> 00:04:48,410 However, the increase in the strength of stratification 90 00:04:48,410 --> 00:04:51,740 promoted through global warming is extending the length 91 00:04:51,740 --> 00:04:53,223 of seasonal hypoxia. 92 00:04:55,850 --> 00:04:58,880 It has been documented that hypoxic events 93 00:04:58,880 --> 00:05:02,620 can lead to mass fish kills and fishery closures. 94 00:05:02,620 --> 00:05:06,050 However, subtler effects of hypoxia are reduced 95 00:05:06,050 --> 00:05:10,120 egg production of copepods, altered community structure 96 00:05:10,120 --> 00:05:14,110 of mesoplankton in Hood Canal has also been documented. 97 00:05:14,110 --> 00:05:16,240 A change which will likely affect the amount 98 00:05:16,240 --> 00:05:19,903 of energy transfer between tropic levels in the marine web. 99 00:05:22,540 --> 00:05:26,500 Unfortunately, a lot is unknown about how hypoxia 100 00:05:26,500 --> 00:05:29,500 affects each aspect of the marine web. 101 00:05:29,500 --> 00:05:32,510 Predators like fish can deal with the extension 102 00:05:32,510 --> 00:05:35,910 of seasonal hypoxic conditions by simply changing 103 00:05:35,910 --> 00:05:39,580 migration habits and avoiding low oxygen areas. 104 00:05:39,580 --> 00:05:42,750 However, drifting organisms like copepods 105 00:05:42,750 --> 00:05:46,080 pay a higher cost to exploit adaptive mechanisms 106 00:05:46,080 --> 00:05:50,000 that allow them to better deal with low oxygen conditions 107 00:05:50,000 --> 00:05:53,783 because they're less able to move away from affected areas. 108 00:05:55,160 --> 00:05:58,200 One adaptation of copepods is to adjust 109 00:05:58,200 --> 00:06:01,100 their vertical position within the water column. 110 00:06:01,100 --> 00:06:04,630 An example of this adjustment is Diel Vertical Migration, 111 00:06:04,630 --> 00:06:08,210 when copepods occupy depths during the day 112 00:06:08,210 --> 00:06:10,573 and migrate to the surface to feed at night. 113 00:06:14,880 --> 00:06:18,860 Hypoxic conditions occur seasonally in Hood Canal, 114 00:06:18,860 --> 00:06:21,410 a sub-basin of Puget Sound, Washington 115 00:06:21,410 --> 00:06:25,640 making it a good place to study the effects of hypoxia. 116 00:06:25,640 --> 00:06:29,270 Hood Canal is a deep and narrow basin that makes up 117 00:06:29,270 --> 00:06:32,450 the entire Western branch of Puget Sound. 118 00:06:32,450 --> 00:06:36,950 At the northern end of Hood Canal, there is a long sill 119 00:06:36,950 --> 00:06:38,700 which limits ocean exchange 120 00:06:38,700 --> 00:06:41,113 and seasonally high effects of hypoxia. 121 00:06:42,000 --> 00:06:45,610 In the fall, tidal intrusions bring in dense waters 122 00:06:45,610 --> 00:06:48,860 that sweep over the sill and flow into the basin. 123 00:06:48,860 --> 00:06:53,100 Stratification occurs as this cold and dense water sinks 124 00:06:53,100 --> 00:06:56,420 to the bottom of the basin and then displaces the less dense 125 00:06:56,420 --> 00:06:59,133 bottom water towards the surface of the water column. 126 00:07:00,540 --> 00:07:04,830 This displaced water, which is low in oxygen concentration 127 00:07:04,830 --> 00:07:07,980 due to respiration, and a lack of mixing 128 00:07:07,980 --> 00:07:11,053 leads to a mid water layer of hypoxic conditions. 129 00:07:14,870 --> 00:07:19,370 So the question I explicitly asked for this study 130 00:07:19,370 --> 00:07:23,460 was, how do oxygen levels affect the community size 131 00:07:23,460 --> 00:07:26,993 distributions of copepods throughout the water column? 132 00:07:30,140 --> 00:07:34,690 And to answer this question, I need to determine two things. 133 00:07:34,690 --> 00:07:38,340 First, does oxygen affect the size distribution 134 00:07:38,340 --> 00:07:41,290 of where they are in the water column? 135 00:07:41,290 --> 00:07:45,340 Based on the theory that larger organisms require 136 00:07:45,340 --> 00:07:48,830 more oxygen than smaller organisms to function, 137 00:07:48,830 --> 00:07:53,830 I hypothesized that smaller organisms would well, 138 00:07:54,540 --> 00:07:58,140 I hypothesized that copepods size composition 139 00:07:58,140 --> 00:08:02,440 would increase with distance relative to the oxygen minimum. 140 00:08:02,440 --> 00:08:04,093 So something like this. 141 00:08:05,220 --> 00:08:09,380 And second, how does oxygen affect, 142 00:08:09,380 --> 00:08:13,440 how does oxygen effect the abundance of individuals? 143 00:08:13,440 --> 00:08:16,820 If oxygen concentrations were severely low, 144 00:08:16,820 --> 00:08:19,810 I hypothesized a decrease in the abundance 145 00:08:19,810 --> 00:08:21,143 of individuals present. 146 00:08:26,170 --> 00:08:30,500 Shifting into my method section, samples were collected 147 00:08:30,500 --> 00:08:34,910 at Twanoh Hood Canal on the 19th of August, 2017 148 00:08:34,910 --> 00:08:39,823 and the 24th of September, 2018 above the RV Rachel Carson. 149 00:08:40,770 --> 00:08:44,420 A CTD sensor fitted with an annually calibrated 150 00:08:44,420 --> 00:08:47,540 dissolved oxygen sensor was cast to determine 151 00:08:47,540 --> 00:08:49,820 target depths of interest. 152 00:08:49,820 --> 00:08:52,890 Profiles used in this study were selected 153 00:08:52,890 --> 00:08:55,730 based on oxygen concentrations. 154 00:08:55,730 --> 00:08:59,380 Levels of interests range from hypoxic conditions, 155 00:08:59,380 --> 00:09:03,540 less than two milligrams per liter to severe hypoxia 156 00:09:03,540 --> 00:09:06,030 which is less than one milligram per liter 157 00:09:06,030 --> 00:09:09,470 based on previous research conducted on copepod behavior 158 00:09:09,470 --> 00:09:11,283 under low oxygen levels. 159 00:09:16,770 --> 00:09:20,610 Once target depths were identified based on the obtained 160 00:09:20,610 --> 00:09:24,090 oxygen profile above, within and below 161 00:09:24,090 --> 00:09:27,480 the oxygen minimum zone, oblique multinet tows 162 00:09:27,480 --> 00:09:30,110 were conducted day and night to observe 163 00:09:30,110 --> 00:09:32,203 Diel Vertical Migration or DVM. 164 00:09:41,210 --> 00:09:45,420 Loading, zooplankton samples collected were preserved 165 00:09:45,420 --> 00:09:48,003 and returned to the laboratory for analysis. 166 00:09:52,410 --> 00:09:55,990 And in the laboratory, each zooplankton sample 167 00:09:55,990 --> 00:09:58,320 was quantitatively diluted and a sub sample 168 00:09:58,320 --> 00:10:00,240 was taken for analysis. 169 00:10:00,240 --> 00:10:03,170 Each species of copepod within a sub sample 170 00:10:03,170 --> 00:10:06,323 was counted and classified into five separate groups. 171 00:10:09,110 --> 00:10:14,110 Here is the CTD profile under normoxic conditions 172 00:10:14,270 --> 00:10:17,250 taken August 19th, 2017, 173 00:10:17,250 --> 00:10:20,330 at 3:30 AM at Twanoh Hood Canal, 174 00:10:20,330 --> 00:10:24,070 measuring oxygen concentration, temperature, 175 00:10:24,070 --> 00:10:26,340 chlorophyll, and salinity. 176 00:10:26,340 --> 00:10:30,990 The dash lines here represent the depths in meters. 177 00:10:30,990 --> 00:10:35,990 And the y-axis represents the well the dash line, sorry, 178 00:10:36,110 --> 00:10:39,060 the dash lines outlined multinet depth ranges 179 00:10:39,060 --> 00:10:42,240 and the y-axis represents the depth in meters 180 00:10:42,240 --> 00:10:45,240 and the x-axis represents the respective units 181 00:10:45,240 --> 00:10:47,180 of conditions measured. 182 00:10:47,180 --> 00:10:50,810 Now, focusing on the oxygen profile represented 183 00:10:50,810 --> 00:10:54,330 by this green line, we can see that conditions 184 00:10:54,330 --> 00:10:57,220 never fell below two milligrams per liter, 185 00:10:57,220 --> 00:11:00,423 which is why we'll use this profile as the control. 186 00:11:03,010 --> 00:11:07,848 And here we have the CTD profile under hypoxic conditions 187 00:11:07,848 --> 00:11:12,848 that was taken on September 24th, 2018 at 6:45 AM. 188 00:11:13,880 --> 00:11:17,910 Focusing on the oxygen profile, again, here in green, 189 00:11:17,910 --> 00:11:21,120 we can see concentrations fell well below 190 00:11:21,120 --> 00:11:24,726 the hypothermic threshold of two milligrams per liter. 191 00:11:24,726 --> 00:11:29,726 At 10 meters, we actually observed the oxygen minimum level 192 00:11:30,360 --> 00:11:34,767 where oxygen concentrations reach 0.25 milligrams per liter 193 00:11:36,510 --> 00:11:40,250 making it severely hypoxic conditions documented 194 00:11:40,250 --> 00:11:43,373 to significantly alter survival rates of copepods. 195 00:11:46,390 --> 00:11:50,360 Now, adjusting my first question, does oxygen affect 196 00:11:50,360 --> 00:11:53,850 the size distribution of where they are in the column? 197 00:11:53,850 --> 00:11:56,883 Note, this is my size slide indicator. 198 00:11:59,130 --> 00:12:02,600 First, we'll look at the distribution for the average size 199 00:12:02,600 --> 00:12:06,903 of the copepods under both normoxic and hypoxic conditions. 200 00:12:08,470 --> 00:12:11,440 At night, which is depicted with the green bars 201 00:12:11,440 --> 00:12:14,740 and during the day, which is depicted with the blue bars. 202 00:12:14,740 --> 00:12:18,460 The y-axis is the multinet depth ranges in meters 203 00:12:18,460 --> 00:12:21,480 and the x-axis is the prosome length 204 00:12:21,480 --> 00:12:23,513 of the copepods in millimeters. 205 00:12:25,060 --> 00:12:28,450 Copepods size distribution, significantly differed 206 00:12:28,450 --> 00:12:30,970 among depth's data under normoxic conditions. 207 00:12:30,970 --> 00:12:34,530 So this entire profile from 2017. 208 00:12:34,530 --> 00:12:38,170 As well as under hypoxic conditions, but only during 209 00:12:38,170 --> 00:12:41,113 the day, so just the blue bars in this graph. 210 00:12:43,100 --> 00:12:46,860 There were no statistically significant differences in size 211 00:12:46,860 --> 00:12:50,740 distribution among depths at night under hypoxic conditions, 212 00:12:50,740 --> 00:12:53,253 so that's the green bars in this graph. 213 00:12:55,380 --> 00:12:59,440 So we had a pretty even size distribution at night 214 00:12:59,440 --> 00:13:02,180 unlike our normoxic profile. 215 00:13:02,180 --> 00:13:06,140 Note, the green, yellow and red circles, 216 00:13:06,140 --> 00:13:08,720 these act as a reference and will be seen 217 00:13:08,720 --> 00:13:13,080 throughout my data to reflect normal, hypoxic, 218 00:13:13,080 --> 00:13:16,243 and severely hypoxic oxygen concentrations. 219 00:13:21,660 --> 00:13:26,640 The oxygen minimum of 0.25 milligrams per liter 220 00:13:26,640 --> 00:13:30,290 again, occurred at 10 meters below the sea surface. 221 00:13:30,290 --> 00:13:35,290 And on average, net two, the 10 to 15 meter range 222 00:13:36,610 --> 00:13:38,850 had the lowest oxygen concentration 223 00:13:38,850 --> 00:13:43,850 of 0.68 milligrams per liter, making it severely hypoxic. 224 00:13:44,060 --> 00:13:47,430 Recalling my original hypothesis that I would find 225 00:13:47,430 --> 00:13:51,560 the smallest copepods occupying the oxygen minimum zone 226 00:13:51,560 --> 00:13:54,850 and size would then increase with distance relative 227 00:13:54,850 --> 00:13:58,140 to the oxygen minimum zone, this is where I expected 228 00:13:58,140 --> 00:14:01,410 to observe the smallest copepods at night. 229 00:14:01,410 --> 00:14:03,620 This data did not support that hypothesis. 230 00:14:03,620 --> 00:14:07,640 I actually added these little copepods that are proportioned 231 00:14:07,640 --> 00:14:11,720 in size to kind of give you an idea of what 0.64, 0.62 232 00:14:11,720 --> 00:14:13,870 or 0.61 meters look like. 233 00:14:13,870 --> 00:14:16,420 So you can see these, it's really hard to distinguish 234 00:14:16,420 --> 00:14:19,770 the difference in size under hypoxic conditions. 235 00:14:19,770 --> 00:14:21,260 And it's a little bit easier to tell 236 00:14:21,260 --> 00:14:26,040 that this copepod right here under normoxic conditions 237 00:14:26,040 --> 00:14:28,610 is largest and it kind of differs in size. 238 00:14:28,610 --> 00:14:32,110 So that's my statistically significant difference 239 00:14:32,110 --> 00:14:34,810 in size when it comes to copepods in the water column. 240 00:14:37,350 --> 00:14:42,070 I use box plots to describe the size distribution 241 00:14:42,070 --> 00:14:44,560 of copepods throughout the water column 242 00:14:44,560 --> 00:14:47,500 and identify outliers within my data. 243 00:14:47,500 --> 00:14:52,500 Here, we have box plots under hypoxic conditions at night 244 00:14:52,920 --> 00:14:54,420 and during the day. 245 00:14:54,420 --> 00:14:58,090 The y-axis is the prosome length of the copepods 246 00:14:58,090 --> 00:15:00,220 in millimeters and the x-axis 247 00:15:00,220 --> 00:15:02,983 is the multinet depth ranges in meters. 248 00:15:04,090 --> 00:15:08,220 Under hypoxic conditions, numerous outliers 249 00:15:08,220 --> 00:15:11,530 shown as the blue circles, were scattered throughout 250 00:15:11,530 --> 00:15:14,283 the water column at night as you can see here. 251 00:15:16,810 --> 00:15:21,780 The mean value of length shown as the Xs was drawn up 252 00:15:21,780 --> 00:15:26,050 the most by outliers in the surface 0 to 5 meters 253 00:15:26,050 --> 00:15:27,613 at night, so right here. 254 00:15:28,820 --> 00:15:31,710 During the day, there was an overall decrease 255 00:15:31,710 --> 00:15:36,520 in the number of outliers observed with larger copepods 256 00:15:36,520 --> 00:15:40,710 shifting down and out of the surface 0 to 5 meters 257 00:15:40,710 --> 00:15:41,653 during the day. 258 00:15:45,160 --> 00:15:48,950 When compared to normoxic conditions, the length data 259 00:15:48,950 --> 00:15:52,230 was more normally distributed and most multinets 260 00:15:52,230 --> 00:15:54,540 during the night and day. 261 00:15:54,540 --> 00:15:57,560 So one of the things that was difficult here 262 00:15:57,560 --> 00:16:01,380 is the copepods have a very wide size distribution 263 00:16:01,380 --> 00:16:03,640 and there were a lot of outliers. 264 00:16:03,640 --> 00:16:06,420 And considering outliers do up the average value 265 00:16:06,420 --> 00:16:08,743 of length data in each depth's data, 266 00:16:10,380 --> 00:16:13,830 I switched to looking at the median to get a better picture 267 00:16:13,830 --> 00:16:16,160 of the copepods size distribution 268 00:16:16,160 --> 00:16:20,580 with the y-axis representing the copepods prosome length 269 00:16:20,580 --> 00:16:24,020 in millimeters and the x-axis, wait, no, sorry, 270 00:16:24,020 --> 00:16:26,410 the y-axis is the multinet depth ranges 271 00:16:26,410 --> 00:16:29,713 and the x-axis is the copepod prosome, apologies. 272 00:16:30,600 --> 00:16:33,760 Again, recalling my original hypothesis, 273 00:16:33,760 --> 00:16:37,360 this is where I expected to observe the smallest copepod 274 00:16:37,360 --> 00:16:40,180 at night in the oxygen minimum zone, 275 00:16:40,180 --> 00:16:43,590 the 10 to 15 meter range under hypoxic conditions. 276 00:16:43,590 --> 00:16:47,423 And this data still did not support that hypothesis. 277 00:16:50,530 --> 00:16:53,500 Now, moving on to my second question, 278 00:16:53,500 --> 00:16:57,090 how does oxygen affect the abundance of individuals? 279 00:16:57,090 --> 00:17:00,980 Note, this is my abundance slide indicator. 280 00:17:00,980 --> 00:17:04,510 We're no longer talking about size, so temporarily forget 281 00:17:04,510 --> 00:17:06,904 about those graphs I just showed you. 282 00:17:06,904 --> 00:17:09,800 Regarding Diel Vertical Migration, 283 00:17:09,800 --> 00:17:13,280 under normoxic conditions, we would expect to see copepods 284 00:17:13,280 --> 00:17:16,770 rise from the depths of the water column to the surface 285 00:17:16,770 --> 00:17:20,240 to feed and that correlates to an observed 286 00:17:20,240 --> 00:17:23,860 population density highest in the surface waters at night 287 00:17:23,860 --> 00:17:27,440 when light intensity has no influence on size distribution 288 00:17:27,440 --> 00:17:30,920 and lowest during the day as copepods migrate 289 00:17:30,920 --> 00:17:35,560 to deeper and darker depths to avoid predation. 290 00:17:35,560 --> 00:17:40,050 Here, we have two graphs 291 00:17:40,050 --> 00:17:43,990 of the estimated copepod abundance per cubic meter 292 00:17:43,990 --> 00:17:46,900 under normoxic and hypoxic conditions. 293 00:17:46,900 --> 00:17:49,290 Day, represented as the yellow bars 294 00:17:49,290 --> 00:17:51,860 and at night represented as the blue ones. 295 00:17:52,760 --> 00:17:57,020 The y-axis represents the depth in meters 296 00:17:57,020 --> 00:18:00,310 and the x-axis represents the density 297 00:18:00,310 --> 00:18:03,773 determined as population size per cubic meter. 298 00:18:04,780 --> 00:18:08,490 Note, the oxygen concentration indicators, 299 00:18:08,490 --> 00:18:10,850 my little, my dots, so again, right here 300 00:18:10,850 --> 00:18:15,850 in these two depth stratas, we had severely hypoxic conditions 301 00:18:16,720 --> 00:18:19,260 and we just had hypoxic here, so that's a little 302 00:18:19,260 --> 00:18:22,750 less than two milligrams per liter, and it was overall 303 00:18:23,710 --> 00:18:27,553 normal oxygen profile in the 0 to 5 meter range. 304 00:18:30,410 --> 00:18:34,720 Here, we have a similar graph with normoxic, the orange 305 00:18:34,720 --> 00:18:39,170 and blue, and the lower hypoxic, yellow and gray conditions 306 00:18:39,170 --> 00:18:42,240 overlapped to emphasize the difference. 307 00:18:42,240 --> 00:18:45,870 The y-axis depicts the multinet number with 5 308 00:18:45,870 --> 00:18:49,220 being at the surface and 1 being at depth. 309 00:18:49,220 --> 00:18:53,730 Now, remember the, I'm shifting to multinet numbers 310 00:18:53,730 --> 00:18:57,510 because the depth ranges are custom to each oxygen profile. 311 00:18:57,510 --> 00:19:00,520 So because I'm looking at normoxic compared to hypoxic, 312 00:19:00,520 --> 00:19:01,830 the depth range is a little different 313 00:19:01,830 --> 00:19:05,043 so I'm just gonna show you the multinet range. 314 00:19:06,520 --> 00:19:09,960 Under normoxic conditions, the orange and blue, 315 00:19:09,960 --> 00:19:12,380 we found evidence to support the theory 316 00:19:12,380 --> 00:19:15,730 of Diel Vertical Migration with the highest abundance 317 00:19:15,730 --> 00:19:19,440 in the surface 0 to 5 meters at night 318 00:19:19,440 --> 00:19:23,830 and shifting down in the water column 319 00:19:23,830 --> 00:19:26,730 as ambient light is introduced. 320 00:19:26,730 --> 00:19:30,480 Under hypoxic conditions, the yellow and gray, 321 00:19:30,480 --> 00:19:32,610 we did not observe the traditional 322 00:19:32,610 --> 00:19:35,250 Diel Vertical Migration behavior. 323 00:19:35,250 --> 00:19:37,790 Population density was actually highest 324 00:19:37,790 --> 00:19:39,460 in the hypoxic layers at night, 325 00:19:39,460 --> 00:19:44,053 so that's multinet 3 and 2, and lowest during the day. 326 00:19:45,600 --> 00:19:49,780 Meaning a large proportion of the population did not make it 327 00:19:49,780 --> 00:19:53,170 to the surface at night under hypoxic conditions. 328 00:19:53,170 --> 00:19:56,330 This observed copepod abundance may support 329 00:19:56,330 --> 00:20:00,810 two previous studies theorizing that copepods may exploit 330 00:20:00,810 --> 00:20:04,020 the oxygen minimum as predation refuge. 331 00:20:04,020 --> 00:20:07,760 However, it's very curious why we observed a drastic shift 332 00:20:07,760 --> 00:20:10,240 in abundance from night to day, 333 00:20:10,240 --> 00:20:13,853 just a--above the oxygen minimum in multinet 3. 334 00:20:14,720 --> 00:20:18,890 The hangup with our sampling method is we can't determine 335 00:20:18,890 --> 00:20:22,260 if the copepods are hanging out to avoid predation 336 00:20:22,260 --> 00:20:23,210 or if they're dead. 337 00:20:26,800 --> 00:20:31,610 So the null hypothesis that size does not vary 338 00:20:31,610 --> 00:20:34,890 with distance relative to the oxygen minimum there 339 00:20:34,890 --> 00:20:38,630 could not be rejected following the results of this study. 340 00:20:38,630 --> 00:20:42,160 However, we did find that the vertical distribution 341 00:20:42,160 --> 00:20:45,790 of copepods was significantly altered with an even 342 00:20:45,790 --> 00:20:50,780 distribution of size at night under hypoxic conditions 343 00:20:50,780 --> 00:20:53,120 compared to normoxic conditions. 344 00:20:53,120 --> 00:20:56,850 And relatively large pods occupying the oxygen minimum 345 00:20:56,850 --> 00:21:01,397 during both day and night, which is contradictory 346 00:21:02,330 --> 00:21:06,860 to a 2017 study on krill that observed the largest species 347 00:21:06,860 --> 00:21:10,113 and areas where oxygen concentrations were the highest. 348 00:21:11,910 --> 00:21:14,690 And regarding Diel Vertical Migration, 349 00:21:14,690 --> 00:21:18,090 our abundance data support the theory of DVM 350 00:21:18,090 --> 00:21:21,540 under both normoxic and hypoxic conditions. 351 00:21:21,540 --> 00:21:24,650 However, DVM was fundamentally altered 352 00:21:24,650 --> 00:21:27,530 under hypoxic conditions, which is contradictory 353 00:21:27,530 --> 00:21:31,160 of a 2009 study which concluded the oxygen minimum layer 354 00:21:31,160 --> 00:21:34,870 affected DVM but did not prevent it. 355 00:21:34,870 --> 00:21:37,670 My theory is under hypoxic conditions, 356 00:21:37,670 --> 00:21:42,330 copepods attempted DVM at night but majority got caught 357 00:21:42,330 --> 00:21:45,150 in the oxygen minimum there and either migrated back down 358 00:21:45,150 --> 00:21:47,530 to depths as ambient light was introduced 359 00:21:47,530 --> 00:21:49,963 or died and settled down the water column. 360 00:21:52,870 --> 00:21:56,270 If the vertical distribution of the size of copepods 361 00:21:56,270 --> 00:21:59,010 is significantly changed, this could affect predator 362 00:21:59,010 --> 00:22:01,910 and prey interactions and alter the transfer of energy 363 00:22:01,910 --> 00:22:04,300 throughout the marine food web. 364 00:22:04,300 --> 00:22:07,640 We observed a shift in copepod size with a more 365 00:22:07,640 --> 00:22:10,480 even distribution and size throughout the water column 366 00:22:10,480 --> 00:22:14,590 under hypoxic conditions, relative to normoxic conditions. 367 00:22:14,590 --> 00:22:19,020 These larger copepods here at the higher surface levels 368 00:22:19,020 --> 00:22:23,350 have a higher risk of being eaten by visual predators. 369 00:22:23,350 --> 00:22:26,250 If their distribution, if they're distributed differently, 370 00:22:27,109 --> 00:22:30,170 they can be exposed to different predators which will affect 371 00:22:30,170 --> 00:22:32,693 the, which will alter the food web dynamics. 372 00:22:33,710 --> 00:22:36,930 We also observed a decrease in abundance of copepods 373 00:22:36,930 --> 00:22:40,050 present in the surface layers under hypoxic conditions 374 00:22:40,050 --> 00:22:42,760 due to either mortality rates or predator avoidance, 375 00:22:42,760 --> 00:22:44,110 it's unclear at this point. 376 00:22:45,120 --> 00:22:47,610 If predators do not adjust their position 377 00:22:47,610 --> 00:22:49,840 in the water column, they need to migrate 378 00:22:49,840 --> 00:22:52,050 to a new location in search of prey, 379 00:22:52,050 --> 00:22:54,833 leaving a niche open for a new predator to occupy. 380 00:22:55,820 --> 00:22:57,890 But what if predators are unable to adapt 381 00:22:57,890 --> 00:22:59,483 to low oxygen availability? 382 00:23:00,410 --> 00:23:02,920 This will lead to a shift in predatory species 383 00:23:02,920 --> 00:23:06,210 in favor of those more tolerant to low oxygen levels 384 00:23:06,210 --> 00:23:07,920 who may have different predation behavior, 385 00:23:07,920 --> 00:23:10,463 like jellyfish or other gelatinous species. 386 00:23:15,870 --> 00:23:19,140 Since graduation, I've continued to work with Doctors 387 00:23:19,140 --> 00:23:22,680 Keister and Grünbaum to develop an imaging system 388 00:23:22,680 --> 00:23:25,500 to observe zooplankton swimming behavior in-situ 389 00:23:25,500 --> 00:23:28,053 with the hopes to add more data to this study. 390 00:23:28,990 --> 00:23:32,310 Net tows are just snapshots in time, but with imaging, 391 00:23:32,310 --> 00:23:34,500 we can look up and down the water column to get 392 00:23:34,500 --> 00:23:38,030 a better idea of what's happening beneath the surface. 393 00:23:38,030 --> 00:23:42,130 An imaging system was deployed on the Twanoh Orca Mooring 394 00:23:42,130 --> 00:23:46,310 from May through September from 2017 to 2018 395 00:23:46,310 --> 00:23:50,310 to monitor zooplankton abundance, vertical distribution, 396 00:23:50,310 --> 00:23:54,320 and small scale movement behaviors from prior to the onset 397 00:23:54,320 --> 00:23:57,273 of hypoxia through peak hypoxic conditions. 398 00:23:58,490 --> 00:24:02,060 This system profiled the water column several times a day 399 00:24:02,060 --> 00:24:04,690 to resolve changes in distributions on dial 400 00:24:04,690 --> 00:24:07,100 to seasonal timescales. 401 00:24:07,100 --> 00:24:10,130 We've also built and deployed an array of low cost 402 00:24:10,130 --> 00:24:13,710 video cameras on Lagrangian Drifters at four depths 403 00:24:13,710 --> 00:24:17,080 to concurrently study the behaviors and distributions 404 00:24:17,080 --> 00:24:19,320 of zooplankton throughout the water column, 405 00:24:19,320 --> 00:24:22,496 and the hours surrounding sunrise each day to capture 406 00:24:22,496 --> 00:24:25,870 zooplankton vertical migrations from surface to depth, 407 00:24:25,870 --> 00:24:28,430 when they're most likely to experience changes 408 00:24:28,430 --> 00:24:31,003 from favorable to unfavorable conditions. 409 00:24:36,040 --> 00:24:39,640 Dr. Grünbaum developed a low cost plankton imager 410 00:24:39,640 --> 00:24:42,620 to be mounted to the Lagrangian Drifters, which included 411 00:24:42,620 --> 00:24:46,020 a camera, an infrared illuminator and battery pack, 412 00:24:46,020 --> 00:24:48,470 which was constructed using only off-the-shelf 413 00:24:48,470 --> 00:24:50,490 and 3D printed parts. 414 00:24:50,490 --> 00:24:53,934 This imager can collect up to 60 gigabits of video 415 00:24:53,934 --> 00:24:56,963 during a 24-hour autonomous deployment. 416 00:25:01,210 --> 00:25:04,150 I personally work with an array of still images 417 00:25:04,150 --> 00:25:06,410 collected from the Orca Mooring. 418 00:25:06,410 --> 00:25:09,320 Particles that fit a predetermined size threshold 419 00:25:09,320 --> 00:25:12,993 are identified and isolated from manual classification. 420 00:25:15,530 --> 00:25:18,950 Here is a screenshot of the classification interface 421 00:25:18,950 --> 00:25:21,240 we're using which allows us to quickly sift 422 00:25:21,240 --> 00:25:24,060 through and identify species of interest observed 423 00:25:24,060 --> 00:25:25,060 in the water column. 424 00:25:27,410 --> 00:25:32,410 Like copepods and filter feeders, like larvaceans. 425 00:25:32,520 --> 00:25:35,850 Here, you can see one in its mucus house, 426 00:25:35,850 --> 00:25:39,310 medusae and ctenophores that can better withstand 427 00:25:39,310 --> 00:25:42,080 hypoxic conditions relative to other predators, 428 00:25:42,080 --> 00:25:43,127 and even euphausiids. 429 00:25:44,500 --> 00:25:48,000 All of the classified ROIs have their own corresponding 430 00:25:48,000 --> 00:25:52,670 timestamps, depths, and CTD profiles associated with them. 431 00:25:52,670 --> 00:25:55,330 So we're able to identify the specific conditions 432 00:25:55,330 --> 00:25:56,890 we're observing them under. 433 00:25:56,890 --> 00:25:59,270 It's truly some really exciting work that we're doing 434 00:25:59,270 --> 00:26:01,390 right now and getting beautiful images 435 00:26:01,390 --> 00:26:03,393 like these are the highlight of my job. 436 00:26:08,300 --> 00:26:11,410 The skills I gained working with Dr. Julie Keister 437 00:26:11,410 --> 00:26:14,180 and Dr. Danny Grünbaum, their graduate students, 438 00:26:14,180 --> 00:26:17,810 Sasha Seroy and Amy Wyeth, and the training I obtained 439 00:26:17,810 --> 00:26:20,890 from their research scientist and technician, Amanda Winans 440 00:26:20,890 --> 00:26:23,610 and BethElLee Herrmann have made the transition 441 00:26:23,610 --> 00:26:25,530 to my work here at NOAA seamless. 442 00:26:25,530 --> 00:26:27,970 So I owe a big thank you to them. 443 00:26:27,970 --> 00:26:30,490 And I know a few of them have tuned in in support of me 444 00:26:30,490 --> 00:26:32,683 here today, so thank you ladies. 445 00:26:35,740 --> 00:26:39,690 Now, for the second portion of this presentation, 446 00:26:39,690 --> 00:26:42,390 I'll share my current work here at NOAA, 447 00:26:42,390 --> 00:26:46,530 where I've been given the opportunity to further my skills 448 00:26:46,530 --> 00:26:50,260 in taxonomy and image analysis. 449 00:26:50,260 --> 00:26:52,650 I'm actually a contractor with Lynker, 450 00:26:52,650 --> 00:26:55,390 at NOAA's Alaska Fishery Science Center 451 00:26:55,390 --> 00:26:57,563 in the RACE Division here in Seattle. 452 00:26:59,890 --> 00:27:03,720 Now, I'm gonna cover a lot of topics and jump around a bit. 453 00:27:03,720 --> 00:27:07,780 So here's an outline so you know what to expect. 454 00:27:07,780 --> 00:27:11,020 I'll go over my contracted tasks at NOAA. 455 00:27:11,020 --> 00:27:13,960 Then I'll shift into my research focuses 456 00:27:13,960 --> 00:27:17,190 which are in relation to climate change, universal responses 457 00:27:17,190 --> 00:27:20,580 to global warming, and using ecosystem indicators. 458 00:27:20,580 --> 00:27:23,010 Then I'll go over research objectives 459 00:27:23,010 --> 00:27:26,420 using imaging analysis from my data collection 460 00:27:26,420 --> 00:27:30,150 to the software we're gonna use, as well as implications 461 00:27:30,150 --> 00:27:34,573 of our findings for this overall health of our ecosystems. 462 00:27:35,440 --> 00:27:36,590 Alright, so here we go. 463 00:27:38,470 --> 00:27:40,460 A large portion of my work here 464 00:27:40,460 --> 00:27:43,210 consists of zooplankton identification. 465 00:27:43,210 --> 00:27:45,160 NOAA has been in collaboration 466 00:27:45,160 --> 00:27:48,040 with the Plankton Sorting and Identification Center 467 00:27:48,040 --> 00:27:51,790 located in a Szczecin, Poland for 45 years. 468 00:27:51,790 --> 00:27:55,790 The department was founded in 1974, 469 00:27:55,790 --> 00:27:58,610 following the signing of an inter-governmental agreement 470 00:27:58,610 --> 00:28:02,420 between the Sea Fisheries Institute in Gardenia, Poland 471 00:28:02,420 --> 00:28:05,803 and the National Marine Fishery Service in Woods Hole, USA. 472 00:28:07,017 --> 00:28:12,017 This creation of this sorting center was actually stimulated 473 00:28:12,110 --> 00:28:16,000 by a 50% decrease of bottom and pelagic fish resource 474 00:28:16,000 --> 00:28:20,060 biomasses in the Northeast Atlantic in the '70s. 475 00:28:20,060 --> 00:28:23,340 Currently, there are about 10 highly skilled women 476 00:28:23,340 --> 00:28:27,000 that work here who processed over 7000 zooplankton samples 477 00:28:27,000 --> 00:28:28,780 a year from all over the world. 478 00:28:28,780 --> 00:28:32,140 So you can see the orange is all the different oceans 479 00:28:32,140 --> 00:28:34,343 that they process samples from. 480 00:28:36,560 --> 00:28:40,160 All of the zooplankton samples we collect here at ASFC 481 00:28:40,160 --> 00:28:43,800 are shipped to Poland where every specimen is identified, 482 00:28:43,800 --> 00:28:45,830 sorted and recorded. 483 00:28:45,830 --> 00:28:47,780 And then they're shipped back here to Seattle 484 00:28:47,780 --> 00:28:50,760 where Jessie Lim and I QA/QC this data 485 00:28:50,760 --> 00:28:53,290 through a verification process. 486 00:28:53,290 --> 00:28:56,300 The samples are then stored in our archive room 487 00:28:56,300 --> 00:28:58,980 where we have records of specimen dating 488 00:28:58,980 --> 00:29:01,620 all the way back to the late '80s. 489 00:29:01,620 --> 00:29:04,370 So this is what my normal day-to-day work 490 00:29:04,370 --> 00:29:05,683 looks like here at NOAA. 491 00:29:10,080 --> 00:29:12,880 Now, I'm going to shift to my research 492 00:29:12,880 --> 00:29:15,240 and what I'm really interested in which is looking 493 00:29:15,240 --> 00:29:18,233 at climate change and Alaska's ecosystems. 494 00:29:19,100 --> 00:29:21,960 The marine ecosystem off Alaska's coast 495 00:29:21,960 --> 00:29:25,160 sustain North America's richest fisheries. 496 00:29:25,160 --> 00:29:29,140 Alaska exports more than 1 million metric tons of seafood 497 00:29:29,140 --> 00:29:33,160 each year, bringing in over $3 billion worth of new money 498 00:29:33,160 --> 00:29:35,260 into the U.S. economy. 499 00:29:35,260 --> 00:29:40,180 Warming ocean temperatures which peaked in 2015 are believed 500 00:29:40,180 --> 00:29:43,593 to have had a major effect on the health of our fisheries. 501 00:29:46,040 --> 00:29:49,610 Now, there are many things that can affect plankton growth 502 00:29:49,610 --> 00:29:52,050 which is really important to our ecosystems 503 00:29:52,050 --> 00:29:55,520 because it sets up the base of the lower food web. 504 00:29:55,520 --> 00:29:57,700 One of the effects of warming waters 505 00:29:57,700 --> 00:30:00,770 is we're beginning to see an early sea ice retreat 506 00:30:00,770 --> 00:30:03,270 on the eastern Bering Sea shelf. 507 00:30:03,270 --> 00:30:06,040 And this early sea ice retreat is triggering 508 00:30:06,040 --> 00:30:08,510 an early spring algal bloom. 509 00:30:08,510 --> 00:30:12,440 Now, zooplankton vary their activities and reproduction 510 00:30:12,440 --> 00:30:15,990 according to the season, and we believe the timing 511 00:30:15,990 --> 00:30:18,730 of the spring bloom is critical to them. 512 00:30:18,730 --> 00:30:21,430 It's when and how they get the nutrition 513 00:30:21,430 --> 00:30:22,963 they need for egg production. 514 00:30:23,880 --> 00:30:28,070 The mismatch of when food is available and when zooplankton 515 00:30:28,070 --> 00:30:30,770 come out of diapause in search for that food 516 00:30:30,770 --> 00:30:32,883 could send ripples up the food chain. 517 00:30:33,930 --> 00:30:37,990 So this is just one of the things that I'm currently looking 518 00:30:37,990 --> 00:30:41,250 at, but I'm not gonna talk more about this today. 519 00:30:41,250 --> 00:30:44,900 Instead, I'm gonna focus on zooplankton and how they serve 520 00:30:44,900 --> 00:30:48,103 as an important link to the overall health of our ecosystems. 521 00:30:51,930 --> 00:30:55,950 One of the many things that I'm really interested 522 00:30:55,950 --> 00:31:00,233 in are the universal responses to global warming. 523 00:31:01,220 --> 00:31:05,690 Now, the first two bullet points are pretty well-documented 524 00:31:05,690 --> 00:31:08,190 that we'll see responses to phenology 525 00:31:08,190 --> 00:31:11,830 and range species shifts, but this third one 526 00:31:11,830 --> 00:31:16,320 is still hypothesized with only some evidence to support it. 527 00:31:16,320 --> 00:31:18,690 And when you consider the effects 528 00:31:18,690 --> 00:31:23,090 of a small zooplankton body size across ecosystems, 529 00:31:23,090 --> 00:31:25,500 there's even less evidence at hand. 530 00:31:25,500 --> 00:31:28,240 So it's really an interesting topic to focus 531 00:31:28,240 --> 00:31:31,130 on for our seas that we monitor here at AFSC. 532 00:31:34,610 --> 00:31:38,410 We researched some well-mixed nutrient rich seas. 533 00:31:38,410 --> 00:31:41,820 And an example of this is the Bering Sea shelf 534 00:31:41,820 --> 00:31:45,950 which tends to have this period of really strong mixing 535 00:31:45,950 --> 00:31:49,453 when you get phytoplankton blooms and then it stratifies. 536 00:31:50,490 --> 00:31:53,130 You'll see a division in the water column into layers 537 00:31:53,130 --> 00:31:56,810 with different densities, which inhibits nutrient flux. 538 00:31:56,810 --> 00:32:00,870 Now, if we begin to see an extension in the length 539 00:32:00,870 --> 00:32:04,120 of stratification or how long it lasts, 540 00:32:04,120 --> 00:32:08,830 then we're gonna see a shift toward this more stratified, 541 00:32:08,830 --> 00:32:12,900 nutrient-limited situation which in turn will affect 542 00:32:12,900 --> 00:32:16,693 the food web in terms of overall carbon and energy flow. 543 00:32:19,090 --> 00:32:22,950 Looking at global trends and phytoplankton size, 544 00:32:22,950 --> 00:32:26,020 we can see the smallest vital plankton are observed 545 00:32:26,020 --> 00:32:28,720 near the equator where sea surface temperatures 546 00:32:28,720 --> 00:32:32,220 are warmer and increasing in size as we move 547 00:32:32,220 --> 00:32:35,550 towards the poles and temperature decreases. 548 00:32:35,550 --> 00:32:38,650 This is where we expect to observe highly productive 549 00:32:38,650 --> 00:32:42,373 ecosystems at the poles in the Arctic and Antarctic. 550 00:32:45,730 --> 00:32:50,730 Now, focusing on zooplankton size in relation to warming, 551 00:32:50,810 --> 00:32:53,780 a study conducted in 2015 552 00:32:53,780 --> 00:32:58,000 tested temperature-induced shifts towards smaller body size 553 00:32:58,000 --> 00:33:01,730 and lower abundances under warming conditions by performing 554 00:33:01,730 --> 00:33:06,730 a mesocosm experiment using plankton from the Baltic Sea 555 00:33:07,170 --> 00:33:10,597 at three temperature levels, so 9.5, 13.5, 556 00:33:11,777 --> 00:33:14,533 and 17.5 degrees Celsius. 557 00:33:15,770 --> 00:33:17,550 Look at the average biomass 558 00:33:17,550 --> 00:33:21,100 of edible plankton, phytoplankton. 559 00:33:21,100 --> 00:33:25,940 There's an overall decrease in the biomass 560 00:33:25,940 --> 00:33:27,820 regardless of temperature. 561 00:33:27,820 --> 00:33:31,600 So we know over time in a mesocosm, 562 00:33:31,600 --> 00:33:34,173 so plankton are grazing on phytoplankton. 563 00:33:35,390 --> 00:33:39,610 Now, looking at the average biomass of adult Acartia, 564 00:33:39,610 --> 00:33:42,960 a species of copepod, over time, 565 00:33:42,960 --> 00:33:47,670 we see the coldest temperatures, the open circles, 566 00:33:47,670 --> 00:33:51,470 they have the most biomass, but in the warmest temperature, 567 00:33:51,470 --> 00:33:55,220 the boxes, they have the smallest biomass. 568 00:33:55,220 --> 00:33:59,470 So Acartia, growing under cold conditions are eating 569 00:33:59,470 --> 00:34:01,918 the same amount of food in relation to those growing 570 00:34:01,918 --> 00:34:05,270 under warming conditions but they're bigger. 571 00:34:05,270 --> 00:34:09,543 There's more biomass, roughly 50% more biomass. 572 00:34:10,470 --> 00:34:13,010 Now, again, this is just biomass. 573 00:34:13,010 --> 00:34:16,310 So to confirm they actually are getting smaller, 574 00:34:16,310 --> 00:34:19,510 we'll look at this third graph that describes the average 575 00:34:19,510 --> 00:34:23,023 prosome length, the length of the body excluding the tail. 576 00:34:24,350 --> 00:34:27,970 Looking at the 9.5 degrees Celsius 577 00:34:27,970 --> 00:34:32,160 and 13.5 degrees Celsius, we can see that the adults, 578 00:34:32,160 --> 00:34:36,380 these unfilled circles are the largest. 579 00:34:36,380 --> 00:34:39,260 But when we look at the warmer temperatures, 580 00:34:39,260 --> 00:34:41,610 17.5 degrees Celsius, 581 00:34:41,610 --> 00:34:46,610 we can see their prosome length is down about a 100 microns. 582 00:34:46,840 --> 00:34:51,380 So they become more compressed as temperatures warm. 583 00:34:51,380 --> 00:34:55,640 And it also gets less easy to distinguish between the C5, 584 00:34:55,640 --> 00:34:58,243 the field triangles in the adult stage. 585 00:34:59,080 --> 00:35:01,350 The warmth is reducing their size. 586 00:35:01,350 --> 00:35:03,610 Their higher metabolic rates are inhibiting 587 00:35:03,610 --> 00:35:06,223 their ability to accumulate biomass. 588 00:35:07,180 --> 00:35:11,400 So this is what happened in a mesocosm. 589 00:35:11,400 --> 00:35:14,560 Based on this, what's going to happen in the Bering Sea 590 00:35:14,560 --> 00:35:17,590 with Calanus, another species of copepod? 591 00:35:19,500 --> 00:35:23,550 If we extrapolate that data out to the Bering Sea, 592 00:35:23,550 --> 00:35:26,910 the individual cell size of phytoplankton may be smaller 593 00:35:26,910 --> 00:35:29,400 but overall there may not be major changes 594 00:35:29,400 --> 00:35:31,853 in relation to phytoplankton biomass. 595 00:35:33,660 --> 00:35:37,620 But if it gets warmer, we're going to have a population 596 00:35:37,620 --> 00:35:41,200 of Calanus that are typically rich in lipid content, 597 00:35:41,200 --> 00:35:43,050 decrease in size. 598 00:35:43,050 --> 00:35:47,700 And what we expect to see is size decreases as the amount 599 00:35:47,700 --> 00:35:51,660 of lipid, which is energy in the system could go down 600 00:35:51,660 --> 00:35:54,730 and the size of the individuals could go down as well. 601 00:35:54,730 --> 00:35:59,730 So this is the lipid sac inside of a Calanus. 602 00:35:59,930 --> 00:36:03,370 It's this little, little blob it's kind of hard to see 603 00:36:03,370 --> 00:36:05,470 but it's like a little, little water drop. 604 00:36:08,534 --> 00:36:11,540 If the size of the individuals go down, 605 00:36:11,540 --> 00:36:14,910 this is going to affect how much energy is available 606 00:36:14,910 --> 00:36:17,150 to higher trophic levels. 607 00:36:17,150 --> 00:36:19,910 So how can we get some indications 608 00:36:19,910 --> 00:36:22,140 of whether or not this is happening? 609 00:36:22,140 --> 00:36:23,710 Are they getting smaller? 610 00:36:23,710 --> 00:36:26,600 Are they less full of fat with less biomass? 611 00:36:26,600 --> 00:36:28,053 And how can we measure that? 612 00:36:30,450 --> 00:36:34,570 As it turns out, there is information that we can extract 613 00:36:34,570 --> 00:36:37,440 using specific size indicators. 614 00:36:37,440 --> 00:36:40,480 One is looking at the lipid sac 615 00:36:40,480 --> 00:36:41,900 and looking at the lipid size. 616 00:36:41,900 --> 00:36:44,930 So again, here, these are the little, this is a lipid sac. 617 00:36:44,930 --> 00:36:47,297 We've got one right here, right here. 618 00:36:47,297 --> 00:36:50,010 They're a little hard to see, but once you get it, 619 00:36:50,010 --> 00:36:53,003 you can point them out and, 620 00:36:55,340 --> 00:36:59,220 we can use the lipid size as a proxy for biomass 621 00:36:59,220 --> 00:37:02,120 because even if copepods are the same size, 622 00:37:02,120 --> 00:37:04,630 the amount of lipid content may vary. 623 00:37:04,630 --> 00:37:07,960 And lipid content is really important because it's applies 624 00:37:07,960 --> 00:37:10,660 overwintering provisioning for fish. 625 00:37:10,660 --> 00:37:14,420 We also have the ability to look at body sizes. 626 00:37:14,420 --> 00:37:17,130 If there's a change in body size measurement, 627 00:37:17,130 --> 00:37:20,200 we will observe a bottom up trophic effect 628 00:37:20,200 --> 00:37:22,500 and fish will need to eat more zooplankton to get the 629 00:37:22,500 --> 00:37:25,740 same amount of nutrients as they did in the past. 630 00:37:25,740 --> 00:37:27,740 With image software, we can actually measure 631 00:37:27,740 --> 00:37:30,170 both of these things the size of the lipid droplet 632 00:37:30,170 --> 00:37:31,563 and the body size. 633 00:37:36,330 --> 00:37:39,340 I'm now going to discuss my research objectives, 634 00:37:39,340 --> 00:37:42,720 which rely upon utilizing imaging analysis 635 00:37:42,720 --> 00:37:45,830 to identify trends and zooplankton size over time 636 00:37:45,830 --> 00:37:47,723 in Alaska's ecosystems. 637 00:37:48,600 --> 00:37:51,610 Keep in mind I'm just getting started with this research 638 00:37:51,610 --> 00:37:54,500 so this is just going to be a general overview 639 00:37:54,500 --> 00:37:56,883 and some early shots of what we're working on. 640 00:37:58,460 --> 00:38:00,890 First, we're working on the development 641 00:38:00,890 --> 00:38:05,030 of new ecosystem indicators based on zooplankton size. 642 00:38:05,030 --> 00:38:07,800 We'll generate images using ship board, 643 00:38:07,800 --> 00:38:09,623 as well as in-situ imaging. 644 00:38:11,060 --> 00:38:14,740 Next, in collaboration with Jan Olberger, 645 00:38:14,740 --> 00:38:18,660 our goal is to determine if zooplankton size has changed 646 00:38:18,660 --> 00:38:21,720 over time, and we'll do this by utilizing 647 00:38:21,720 --> 00:38:24,673 our archive samples that we have here on campus. 648 00:38:25,570 --> 00:38:29,790 And the most exciting is we're hoping to develop 649 00:38:29,790 --> 00:38:34,140 artificial algorithms to identify species automatically 650 00:38:34,140 --> 00:38:36,610 using a variety of deep learning networks 651 00:38:36,610 --> 00:38:38,933 and a potential collaboration with Google. 652 00:38:39,920 --> 00:38:42,320 Now, I'm gonna show you how we're gonna do this. 653 00:38:45,810 --> 00:38:50,310 In 2015, AFSC implemented a method 654 00:38:50,310 --> 00:38:54,640 for an at sea rapid zooplankton assessment, RZA, to provide 655 00:38:54,640 --> 00:38:57,960 leading indicator information on zooplankton composition 656 00:38:57,960 --> 00:39:00,710 in Alaska's large marine ecosystems. 657 00:39:00,710 --> 00:39:03,570 It was developed in order to provide information 658 00:39:03,570 --> 00:39:05,390 for our ecosystem status reports 659 00:39:05,390 --> 00:39:07,590 that I'm sure many of you are familiar with. 660 00:39:08,520 --> 00:39:12,240 This rapid assessment is a quick sort and identification 661 00:39:12,240 --> 00:39:15,840 of the zooplankton community and provides preliminary 662 00:39:15,840 --> 00:39:19,223 estimates of zooplankton abundance and community structure. 663 00:39:20,320 --> 00:39:24,330 Through RZA, we'll be able to generate a number of images 664 00:39:24,330 --> 00:39:27,570 just using a simple microscope cell phone adapter. 665 00:39:27,570 --> 00:39:30,990 And with these images collected at sea, we can analyze 666 00:39:30,990 --> 00:39:34,073 and extrapolate size data using imaging software. 667 00:39:37,630 --> 00:39:41,300 We're also working in collaboration with Calvin Mordy 668 00:39:41,300 --> 00:39:44,010 and NOAA PMEL's Innovative Technology 669 00:39:44,010 --> 00:39:49,010 for Arctic Exploration Team to use this really, really cool 670 00:39:49,060 --> 00:39:52,330 Continuous Particle Imaging Classification System 671 00:39:52,330 --> 00:39:54,540 which features embedded processing 672 00:39:54,540 --> 00:39:57,290 and reaching of interest extraction. 673 00:39:57,290 --> 00:40:01,180 It's capable of standalone deployment on CTD rosettes 674 00:40:01,180 --> 00:40:04,423 as well as via autonomous platforms of vehicles. 675 00:40:05,290 --> 00:40:08,940 The camera system is already trained in biodiversity 676 00:40:08,940 --> 00:40:12,770 of plankton and can automatically classify 22 species 677 00:40:12,770 --> 00:40:13,770 using deep learning. 678 00:40:18,120 --> 00:40:21,470 Once we've collected images at sea and in-situ, 679 00:40:21,470 --> 00:40:25,170 we can use the Image Pro 10 software to batch process 680 00:40:25,170 --> 00:40:28,770 and extrapolate a wide variety of size descriptors, 681 00:40:28,770 --> 00:40:31,593 as well as estimate lipid content volume. 682 00:40:32,500 --> 00:40:35,300 This software has the ability to identify 683 00:40:35,300 --> 00:40:39,040 regions of interest using a color contrast threshold, 684 00:40:39,040 --> 00:40:42,230 as well as the option to classify objects 685 00:40:42,230 --> 00:40:45,530 in order to train the program to become automated 686 00:40:45,530 --> 00:40:49,143 once a large library of images are loaded into the system. 687 00:40:53,770 --> 00:40:58,260 We've also recently received funding through the NPRB 688 00:40:58,260 --> 00:41:02,030 to research the effects of warming on copepod size 689 00:41:02,030 --> 00:41:05,760 and the Gulf of Alaska and Bering Sea, both areas 690 00:41:05,760 --> 00:41:09,750 support important commercial and subsistence fisheries. 691 00:41:09,750 --> 00:41:13,730 We'll utilize three decades of archive samples 692 00:41:13,730 --> 00:41:17,350 to generate images of selected species of copepods. 693 00:41:17,350 --> 00:41:20,690 And we'll be using the ImagePro software I just showed you 694 00:41:20,690 --> 00:41:24,440 to analyze these images in order to quantify the effects 695 00:41:24,440 --> 00:41:27,443 of long-term warming on copepod sizes. 696 00:41:32,310 --> 00:41:34,690 And finally, as I mentioned, 697 00:41:34,690 --> 00:41:37,423 we're hoping to develop artificial algorithms. 698 00:41:39,330 --> 00:41:42,390 We'll be able to take all of these images generated 699 00:41:42,390 --> 00:41:46,610 via the RZA, CPICS, the NPRB project, 700 00:41:46,610 --> 00:41:49,010 as well as a vast collection of images 701 00:41:49,010 --> 00:41:51,300 that are being generated in Poland 702 00:41:51,300 --> 00:41:54,360 in lieu of samples we were unable to collect this year 703 00:41:54,360 --> 00:41:58,200 and begin classifying them in order to identify species 704 00:41:58,200 --> 00:42:01,760 automatically using a variety of deep learning networks 705 00:42:01,760 --> 00:42:03,670 and potential collaboration with Google 706 00:42:03,670 --> 00:42:06,940 which is really exciting, and may also put me out of a job 707 00:42:06,940 --> 00:42:08,840 but I think that's a way down the way. 708 00:42:12,330 --> 00:42:15,210 This imaging work is just getting started. 709 00:42:15,210 --> 00:42:18,960 But if our hypotheses are confirmed and plankton 710 00:42:18,960 --> 00:42:22,450 are getting smaller across Alaska's ecosystems, 711 00:42:22,450 --> 00:42:26,260 it'll mean there's less biomass and lipid content available 712 00:42:26,260 --> 00:42:30,590 to fish and indicates a potential link to global warming. 713 00:42:30,590 --> 00:42:34,240 And if we know that, we'll know that under future 714 00:42:34,240 --> 00:42:37,630 warming conditions, the decrease in zooplankton size 715 00:42:37,630 --> 00:42:39,883 will only continue and get worse. 716 00:42:41,100 --> 00:42:45,350 And if our developed ecosystem indicators confirm 717 00:42:45,350 --> 00:42:50,290 copepod size or lipid content is in decline, implications 718 00:42:50,290 --> 00:42:54,020 for our ecosystem conditions that given year are not good 719 00:42:54,020 --> 00:42:57,113 for fish and in turn not good for our fisheries. 720 00:42:58,880 --> 00:43:03,420 Well, I hope over the past 38 minutes, I've been able 721 00:43:03,420 --> 00:43:06,543 to convince you about how important zooplankton are. 722 00:43:07,380 --> 00:43:09,940 As I mentioned, this work is just getting started 723 00:43:09,940 --> 00:43:12,730 but I truly look forward to sharing my findings with you 724 00:43:12,730 --> 00:43:13,563 in the future. 725 00:43:16,000 --> 00:43:19,070 I wanna give a special acknowledgement to Dave Kimmel, 726 00:43:19,070 --> 00:43:21,860 Jesse Lamb, Colleen Harpold, Kimberly Bahl, 727 00:43:21,860 --> 00:43:24,573 Adam Spear and Libby Logerwell for my help, 728 00:43:25,510 --> 00:43:27,560 training me and get me set up here at NOAA 729 00:43:27,560 --> 00:43:29,193 and helping me with this presentation. 730 00:43:33,270 --> 00:43:34,693 Thank you so much, Deana. 731 00:43:36,220 --> 00:43:39,760 You'll get the round of applause that Heather usually does. 732 00:43:39,760 --> 00:43:42,260 We have time for questions so please, if you have, 733 00:43:43,180 --> 00:43:47,430 all the 110 or I guess there are 99 left huge crowd. 734 00:43:47,430 --> 00:43:51,100 If anybody has questions, please put them in the chat box. 735 00:43:51,100 --> 00:43:53,213 We'll start with one from Ned Cokelet 736 00:43:53,213 --> 00:43:57,330 who is asking about your Twanoh work. 737 00:43:57,330 --> 00:43:59,740 I'm just wondering, he says, how deeply the light 738 00:43:59,740 --> 00:44:02,640 penetrated during the day and its relationship 739 00:44:02,640 --> 00:44:04,440 to the depths of your plankton tows? 740 00:44:06,020 --> 00:44:10,070 Well, the graphs of profile before 741 00:44:10,070 --> 00:44:13,830 mostly just looking at the oxygen concentration 742 00:44:13,830 --> 00:44:17,060 so I didn't spend time looking at the light penetration. 743 00:44:17,060 --> 00:44:21,400 I am assuming it's not, it's deep into Twanoh 744 00:44:21,400 --> 00:44:24,810 but it's deeper and other areas of Hood Canal. 745 00:44:24,810 --> 00:44:27,580 So I'm not exactly sure exactly how deep it is 746 00:44:27,580 --> 00:44:31,650 but I want to say it's probably within the top 20 meters 747 00:44:31,650 --> 00:44:32,760 probably a little bit less. 748 00:44:32,760 --> 00:44:34,890 There's a lot of productivity 749 00:44:34,890 --> 00:44:39,010 so the waters are really rich with phytoplankton 750 00:44:39,010 --> 00:44:41,440 so I'm not sure that the light concentration gets down 751 00:44:41,440 --> 00:44:44,315 really low but it's a really good question. 752 00:44:44,315 --> 00:44:47,210 I wanna keep adding onto my senior thesis with hopes 753 00:44:47,210 --> 00:44:49,830 of publishing it, so I'm gonna remember that question 754 00:44:49,830 --> 00:44:51,730 and make sure I answer it in my paper. 755 00:44:54,430 --> 00:44:56,470 So now the question from Jenna who says, 756 00:44:56,470 --> 00:44:59,090 thank you at what depths are you hoping 757 00:44:59,090 --> 00:45:01,995 to set up the autonomous surveys? 758 00:45:01,995 --> 00:45:06,329 Assuming that's the latest work you just talked about. 759 00:45:06,329 --> 00:45:10,600 Yeah, I'm a professional when it comes to identifying 760 00:45:10,600 --> 00:45:14,150 the plankton that we're seeing and I know creating 761 00:45:14,150 --> 00:45:15,740 the algorithms so I'm not exactly sure. 762 00:45:15,740 --> 00:45:18,220 I know that Calvin Mordy may be on the line 763 00:45:18,220 --> 00:45:21,590 as well as Dave Kimmel and they might have more details, 764 00:45:21,590 --> 00:45:24,810 but I'm hoping that, my assumption is that we're hoping 765 00:45:24,810 --> 00:45:29,810 to profile the entire water column as much as we can. 766 00:45:32,516 --> 00:45:35,453 This could be, alright, good. 767 00:45:41,790 --> 00:45:44,290 Alright, here's the question from Hassan. 768 00:45:44,290 --> 00:45:46,760 Deanna, could you elaborate about training data 769 00:45:46,760 --> 00:45:50,120 for machine learning algorithm that you mentioned? 770 00:45:50,120 --> 00:45:52,000 Yeah, absolutely. 771 00:45:52,000 --> 00:45:54,670 I'm just getting started when it comes 772 00:45:54,670 --> 00:45:57,750 to machine learning using the Image Pro 10 software, 773 00:45:57,750 --> 00:46:01,590 and because I'm just starting to load and generate 774 00:46:01,590 --> 00:46:05,920 image libraries, the systems, the software is not able 775 00:46:05,920 --> 00:46:09,540 to automatically identify yet but when I do load 776 00:46:09,540 --> 00:46:12,300 in my software, if you notice in the picture 777 00:46:12,300 --> 00:46:15,090 that had the copepods that were highlighted in blue, 778 00:46:15,090 --> 00:46:18,620 when I laid them, I have the option to set a classification 779 00:46:18,620 --> 00:46:21,640 and I tell the program if this is a copepod or I can even 780 00:46:21,640 --> 00:46:23,700 be more specific if I wanted to but in the beginning, 781 00:46:23,700 --> 00:46:25,203 I'm gonna be really general just to make sure 782 00:46:25,203 --> 00:46:26,690 that I can train system. 783 00:46:26,690 --> 00:46:29,840 And I'll say, this is a copepod or this is a euphausiids, 784 00:46:29,840 --> 00:46:32,120 and I set the class so when it goes through, 785 00:46:32,120 --> 00:46:34,490 I batch process, so I'll do a bunch of copepods 786 00:46:34,490 --> 00:46:37,130 all at one time and say, these are all copepods. 787 00:46:37,130 --> 00:46:40,500 And then all of the data that it spits out gets saved 788 00:46:40,500 --> 00:46:43,390 into a virtual learning file that the system software 789 00:46:43,390 --> 00:46:47,670 creates, and once we get enough images loaded into it, 790 00:46:47,670 --> 00:46:50,420 the system will be able to learn and identify and reference 791 00:46:50,420 --> 00:46:53,600 back to those images that I already provided to the system. 792 00:46:53,600 --> 00:46:58,140 So we need a lot of images to do this with the AI. 793 00:46:58,140 --> 00:47:01,650 And also even if we do collaborate with Google, 794 00:47:01,650 --> 00:47:05,040 the goal is we have to dump a lot of images in. 795 00:47:05,040 --> 00:47:08,330 So we're just in the beginning of generating these images. 796 00:47:08,330 --> 00:47:12,300 So I expect most of the next year to be making just getting 797 00:47:12,300 --> 00:47:14,803 as many images as we can to train all the systems. 798 00:47:20,840 --> 00:47:24,530 Alright, next question is from Patricia, I think, yeah. 799 00:47:24,530 --> 00:47:26,200 You state that the temperature is warm 800 00:47:26,200 --> 00:47:27,790 and zooplanktons may become smaller. 801 00:47:27,790 --> 00:47:30,100 However, do you think there'll be increased abundance 802 00:47:30,100 --> 00:47:33,003 as the zooplankton [indistinct] because of warming? 803 00:47:34,290 --> 00:47:36,500 There could be less lipids per individual 804 00:47:36,500 --> 00:47:38,660 because of the smaller size, but there may be an increase 805 00:47:38,660 --> 00:47:41,023 in numbers that could make up for that deficit. 806 00:47:44,308 --> 00:47:46,361 That's a good point, yeah. 807 00:47:46,361 --> 00:47:49,790 I'm just trying to shift between my, I think of warming 808 00:47:49,790 --> 00:47:54,130 and hypoxia that's undergrad, so warming in the Bering sea, 809 00:47:54,130 --> 00:47:56,300 there could be a change in abundance. 810 00:47:56,300 --> 00:47:59,463 It depends, let me think this one through, 811 00:48:00,420 --> 00:48:04,160 if it's warmer, metabolic rates, they spend more time 812 00:48:05,930 --> 00:48:07,430 in the lower sizes. 813 00:48:07,430 --> 00:48:11,500 I think the higher metabolic rates might prevent them 814 00:48:11,500 --> 00:48:14,310 from growing and it might limit egg production. 815 00:48:14,310 --> 00:48:17,570 So I'm not sure, Dave might have a little bit more insight 816 00:48:17,570 --> 00:48:22,270 about that, but, yeah, it's a good question. 817 00:48:22,270 --> 00:48:23,900 Yeah, that's a really good question. 818 00:48:23,900 --> 00:48:26,450 So there's a paper out there that does, 819 00:48:26,450 --> 00:48:28,690 I can put my camera on, sorry. 820 00:48:28,690 --> 00:48:31,360 Sorry, yeah, there's a paper out there that speculates 821 00:48:31,360 --> 00:48:34,210 just that that there's going to be a shift 822 00:48:34,210 --> 00:48:36,870 in species composition and you may get more zooplankton 823 00:48:36,870 --> 00:48:38,240 but they're smaller. 824 00:48:38,240 --> 00:48:41,750 My concern with that is that if there are different species, 825 00:48:41,750 --> 00:48:44,240 they don't accumulate as much lipid. 826 00:48:44,240 --> 00:48:46,970 So they might have smaller amounts of lipid 827 00:48:46,970 --> 00:48:48,180 and there might be more of them, 828 00:48:48,180 --> 00:48:51,170 but they might be of different sizes which affects 829 00:48:51,170 --> 00:48:54,500 the predator-prey mass ratio and the trophic transfer. 830 00:48:54,500 --> 00:48:57,010 So yeah, there's some competing hypotheses out there 831 00:48:57,010 --> 00:48:59,120 that there will be sort of increases 832 00:48:59,120 --> 00:49:00,900 in the overall abundance as it warms. 833 00:49:00,900 --> 00:49:04,460 But the, I think for this ecosystem, the type of species 834 00:49:04,460 --> 00:49:08,000 does matter because the fish have evolved to use 835 00:49:08,000 --> 00:49:10,510 that as a particular food source. 836 00:49:10,510 --> 00:49:13,289 So, yeah, great, great question. 837 00:49:13,289 --> 00:49:16,488 -I'll shut up, sorry. -No, thank you. 838 00:49:16,488 --> 00:49:17,660 Thank you, Dave. 839 00:49:17,660 --> 00:49:20,360 Next one is from Jim Overland who says, how strong 840 00:49:20,360 --> 00:49:23,427 are your bearing implications based on 2018 and 19. 841 00:49:23,427 --> 00:49:26,460 And I'm thinking that he really, he thinking 842 00:49:26,460 --> 00:49:28,020 about this very warm years 843 00:49:28,020 --> 00:49:30,710 and how that may have impacted the zooplankton. 844 00:49:30,710 --> 00:49:33,360 Do you know this yet or you're still looking at data? 845 00:49:36,150 --> 00:49:37,760 When it comes to imaging, I don't know this yet. 846 00:49:37,760 --> 00:49:39,950 I haven't looked at data, but in regards of this, 847 00:49:39,950 --> 00:49:42,600 I haven't, I need to read more papers and Dave might know 848 00:49:42,600 --> 00:49:44,243 a little bit more about that too. 849 00:49:45,450 --> 00:49:49,060 So we don't know yet but we have zooplankton 850 00:49:49,060 --> 00:49:50,900 in hand that she's gonna image and measure. 851 00:49:50,900 --> 00:49:53,030 So she just got started on that so we hope 852 00:49:53,030 --> 00:49:55,490 to have an answer on that relatively soon. 853 00:49:55,490 --> 00:49:59,560 I'm expecting to see some pretty dramatic size shifts 854 00:50:00,761 --> 00:50:03,780 with a [indistinct], which is not a very big copepod, 855 00:50:03,780 --> 00:50:06,740 they saw about a 100 micro meter change. 856 00:50:06,740 --> 00:50:10,393 And that copepod is only about a millimeter in size. 857 00:50:11,567 --> 00:50:15,870 The Calanus are triple to four times that so I expect to see 858 00:50:15,870 --> 00:50:19,020 some of those changes, at least that's the hypothesis. 859 00:50:19,020 --> 00:50:20,777 So we'll see, we'll be, yeah. 860 00:50:20,777 --> 00:50:23,120 And Deana just started measuring those. 861 00:50:23,120 --> 00:50:27,350 Yeah, I started looking at 2012, 2015 data, 862 00:50:29,150 --> 00:50:30,430 I want to say last week. 863 00:50:30,430 --> 00:50:34,747 And my first initial response was we had a lot [indistinct]. 864 00:50:37,960 --> 00:50:40,470 In sex, we had a lot more females than we saw males 865 00:50:40,470 --> 00:50:42,310 relative to most of the years that we saw. 866 00:50:42,310 --> 00:50:47,100 And there were less adult C5 copepods that were present 867 00:50:47,100 --> 00:50:49,460 which was really, really surprising. 868 00:50:49,460 --> 00:50:51,670 And yeah, it kind of threw us for a loop 869 00:50:51,670 --> 00:50:53,900 so I had to kind of recalibrate a little bit 870 00:50:53,900 --> 00:50:56,860 and figure out what to do when we have a really big shift 871 00:50:56,860 --> 00:50:58,960 and what we expected to see when it came 872 00:50:58,960 --> 00:51:01,203 to who we measure and who we image. 873 00:51:04,840 --> 00:51:06,700 Alright, we're gonna keep moving here. 874 00:51:06,700 --> 00:51:10,070 So Melanie says in the sailor sea study you showed 875 00:51:10,070 --> 00:51:13,600 an 02 reading of several 0.25 milligrams per liter. 876 00:51:13,600 --> 00:51:16,150 Did you ever hit anything that low? 877 00:51:16,150 --> 00:51:19,240 Also, did you approach that study with any info 878 00:51:19,240 --> 00:51:21,980 about metabolic differences between younger, 879 00:51:21,980 --> 00:51:23,813 smaller copepods and larger ones? 880 00:51:25,240 --> 00:51:29,933 So did I, the question, did I find any 881 00:51:31,300 --> 00:51:34,040 oxygen concentrations that low 882 00:51:34,040 --> 00:51:37,470 in relation to other conditions or another profile, 883 00:51:37,470 --> 00:51:39,610 I think I missed that part right there. 884 00:51:39,610 --> 00:51:42,350 I didn't expect oxygen concentrations to get that low 885 00:51:42,350 --> 00:51:43,500 but it's great that it did. 886 00:51:43,500 --> 00:51:45,860 There weren't really a lot of studies 887 00:51:45,860 --> 00:51:49,950 that kind of went that low in oxygen concentration 888 00:51:49,950 --> 00:51:52,540 so it was really nice to kind of see that out. 889 00:51:52,540 --> 00:51:55,410 I did do research into the metabolic difference 890 00:51:55,410 --> 00:51:58,620 and I also did kind of look into species availability. 891 00:51:58,620 --> 00:52:01,220 I looked at five different species of copepod that we found 892 00:52:01,220 --> 00:52:03,580 in that area, and they all do have significantly 893 00:52:03,580 --> 00:52:07,820 different behavioral differences, size differences, 894 00:52:07,820 --> 00:52:10,780 and there is gonna be implications in regards 895 00:52:10,780 --> 00:52:15,150 to the metabolic process, with warmer waters and hypoxia. 896 00:52:15,150 --> 00:52:18,370 I expect there to be different behavior. 897 00:52:18,370 --> 00:52:21,350 That's something didn't apply with them in my initial run 898 00:52:21,350 --> 00:52:24,020 on my senior thesis, but that is something that I plan 899 00:52:24,020 --> 00:52:27,560 on spending time on making sure that I have a large 900 00:52:27,560 --> 00:52:29,740 kind of section because it's really important. 901 00:52:29,740 --> 00:52:34,050 Because the implications on my data weren't very conclusive 902 00:52:34,050 --> 00:52:36,690 and it was kind of surprising and I say, 903 00:52:36,690 --> 00:52:38,510 that's probably what I'm gonna have to rely on next. 904 00:52:38,510 --> 00:52:40,000 I looked at body size and abundance, 905 00:52:40,000 --> 00:52:42,320 I need to now shift towards species abundance. 906 00:52:42,320 --> 00:52:44,710 Exactly who's there and what the differences 907 00:52:44,710 --> 00:52:47,663 between the metabolic rates, so that's a great question. 908 00:52:49,330 --> 00:52:52,070 Next, a good question here from Al Hermann. 909 00:52:52,070 --> 00:52:54,680 Under warming, both zooplankton and fish may experience 910 00:52:54,680 --> 00:52:56,080 heightened metabolism. 911 00:52:56,080 --> 00:52:57,630 Has reduced zooplankton and could be due 912 00:52:57,630 --> 00:53:00,220 to both their own increased respiration 913 00:53:01,190 --> 00:53:03,860 and heightened predation from fish, hungry fish. 914 00:53:03,860 --> 00:53:06,233 Any thoughts on which will be more important? 915 00:53:09,498 --> 00:53:10,533 Not an easy one. 916 00:53:16,910 --> 00:53:18,560 No, I don't have any [laughs]. 917 00:53:20,190 --> 00:53:23,260 I mean, I'm a little biased, I think what happens 918 00:53:23,260 --> 00:53:26,490 with the zooplankton are the most important. 919 00:53:26,490 --> 00:53:29,510 I mean the trophic energy transfer is only 10% 920 00:53:29,510 --> 00:53:30,890 from each trophic level. 921 00:53:30,890 --> 00:53:35,010 So if the metabolic rates and the size of the copepods 922 00:53:35,010 --> 00:53:38,200 are different phytoplankton biomass is a little different, 923 00:53:38,200 --> 00:53:42,580 I expect it to be the, just the main driver of what happens 924 00:53:42,580 --> 00:53:45,180 to the rest of, so even if the fish are affected by it, 925 00:53:45,180 --> 00:53:47,983 I think that they may be affected by it 926 00:53:47,983 --> 00:53:50,820 but I think their biggest effect 927 00:53:50,820 --> 00:53:52,640 will be because of their food availability. 928 00:53:52,640 --> 00:53:54,150 I think the, I'm a little biased, 929 00:53:54,150 --> 00:53:56,033 the zooplankton they're most important. 930 00:53:58,430 --> 00:54:01,840 And then let's see here, we have Patricia saying, 931 00:54:01,840 --> 00:54:04,150 production increases with temperature, I think that relates 932 00:54:04,150 --> 00:54:09,130 to the earlier discussion about lower sizes but potentially 933 00:54:09,130 --> 00:54:11,093 higher production per individual. 934 00:54:12,560 --> 00:54:14,160 If you want to add anything to that, 935 00:54:14,160 --> 00:54:19,160 I think we kind of covered it, let's see. 936 00:54:19,470 --> 00:54:21,520 Another question from Jenna. 937 00:54:21,520 --> 00:54:24,560 What kind of tech is on deck for the surveys? 938 00:54:24,560 --> 00:54:27,430 Does the unit collect or take microscopic imagery? 939 00:54:27,430 --> 00:54:29,860 If it is capturing images to get finite details, 940 00:54:29,860 --> 00:54:34,480 what onboard camera technology does it have? 941 00:54:34,480 --> 00:54:36,290 I'm interested in learning more about the abilities 942 00:54:36,290 --> 00:54:38,573 capturing data in a highly productive area. 943 00:54:39,610 --> 00:54:43,810 Yeah, well, the CPICS is something that 944 00:54:43,810 --> 00:54:46,270 is with Calvin Mordy's group and PMEL. 945 00:54:47,580 --> 00:54:49,430 They're able to collect images, I'm not sure 946 00:54:49,430 --> 00:54:51,870 that they're microscopic I haven't seen it yet. 947 00:54:51,870 --> 00:54:53,810 I've just been able to kind of stalk 948 00:54:53,810 --> 00:54:56,673 around the website's specs of the camera. 949 00:54:57,530 --> 00:54:59,640 But right now, when it comes to in-situ work, 950 00:54:59,640 --> 00:55:02,380 that the camera that we have working on, and then we plan 951 00:55:02,380 --> 00:55:05,730 to pull on board and take microscopic pictures onwards. 952 00:55:05,730 --> 00:55:07,750 So whatever we catch in our nets 953 00:55:07,750 --> 00:55:10,690 and look at under the microscope while we're on board, 954 00:55:10,690 --> 00:55:12,283 we'll use this one for that. 955 00:55:13,970 --> 00:55:16,250 Yeah, it's really exciting, I'm really excited about it too. 956 00:55:16,250 --> 00:55:18,810 Definitely reach out to Dave and Calvin 957 00:55:18,810 --> 00:55:21,223 and they can probably talk to you about it more. 958 00:55:22,070 --> 00:55:26,280 Yeah, I can add to that that NOAA also recently got, 959 00:55:26,280 --> 00:55:29,010 I always get the name wrong, but basically similar type 960 00:55:29,010 --> 00:55:31,340 of approach to take phytoplankton images 961 00:55:31,340 --> 00:55:34,380 which I think will also be tested next year. 962 00:55:34,380 --> 00:55:37,530 A Fido something bought or something like that. 963 00:55:37,530 --> 00:55:39,430 Jeanette Gann, I don't know if she's on this, 964 00:55:39,430 --> 00:55:40,930 is sort of the expert on that. 965 00:55:42,770 --> 00:55:44,710 Let's see, moving on, Patricia says, 966 00:55:44,710 --> 00:55:46,840 thanks for sending interesting work. 967 00:55:46,840 --> 00:55:48,500 I do zooplankton work in the Great Lakes. 968 00:55:48,500 --> 00:55:52,100 So I appreciate all the fellow zooplankton nerds. 969 00:55:52,100 --> 00:55:53,480 Thank you. 970 00:55:53,480 --> 00:55:56,030 Yeah, I'm super excited for the zooplankton work. 971 00:55:57,250 --> 00:56:00,210 I was just gonna mention that the camera 972 00:56:00,210 --> 00:56:03,650 should be in here in time for the spring mooring cruise. 973 00:56:03,650 --> 00:56:08,010 So we'll put it on the CTD and have it collect images 974 00:56:08,010 --> 00:56:10,480 while we're doing the net samples. 975 00:56:10,480 --> 00:56:13,530 And hopefully that all works and then we will have it 976 00:56:13,530 --> 00:56:15,730 on the fall cruise through the Bering Sea 977 00:56:15,730 --> 00:56:19,270 up through the Arctic doing the same thing on the CTD. 978 00:56:19,270 --> 00:56:21,040 If everything looks good, we'll be thinking 979 00:56:21,040 --> 00:56:24,380 about other applications on moorings. 980 00:56:24,380 --> 00:56:25,660 So we'll see how that goes. 981 00:56:25,660 --> 00:56:28,369 Dave, you have anything else to add to that? 982 00:56:28,369 --> 00:56:30,790 No, just that, yeah, it's coming. 983 00:56:30,790 --> 00:56:32,770 It should be here in January and we're gonna put it 984 00:56:32,770 --> 00:56:34,700 through its paces in Puget Sound. 985 00:56:34,700 --> 00:56:39,360 And the other thing is we're really relying 986 00:56:39,360 --> 00:56:41,950 on Poland to produce annotated images. 987 00:56:41,950 --> 00:56:45,160 We had a question earlier about artificial intelligence. 988 00:56:45,160 --> 00:56:47,080 One of the main rate limiting steps 989 00:56:47,080 --> 00:56:49,650 there is getting training data sets. 990 00:56:49,650 --> 00:56:51,990 So what we're doing with our colleagues is Poland 991 00:56:51,990 --> 00:56:54,520 is they're producing the images that are already sorted 992 00:56:54,520 --> 00:56:57,860 and annotated, and hopefully building up that library 993 00:56:57,860 --> 00:57:01,010 so we can produce artificial intelligence algorithms 994 00:57:01,010 --> 00:57:04,960 that are a little bit more annotated because many 995 00:57:04,960 --> 00:57:08,440 of these software limitations are due to the fact 996 00:57:08,440 --> 00:57:12,120 that training sets are just really laborious to produce. 997 00:57:12,120 --> 00:57:14,817 So we're hoping to get by that by helping 998 00:57:14,817 --> 00:57:17,740 with our colleagues in Poland. 999 00:57:17,740 --> 00:57:19,860 So we're really excited to converge 1000 00:57:19,860 --> 00:57:22,790 all of this different activities and technology 1001 00:57:22,790 --> 00:57:26,490 that Deana is sitting at the nexus of to help us 1002 00:57:26,490 --> 00:57:27,640 advance this science. 1003 00:57:27,640 --> 00:57:30,310 So we're very excited to get out in the field next year 1004 00:57:30,310 --> 00:57:31,180 and do some more of this. 1005 00:57:31,180 --> 00:57:33,190 And will you be on, will either of you 1006 00:57:33,190 --> 00:57:35,140 be on the spring mooring cruise, do you have plans 1007 00:57:35,140 --> 00:57:38,473 for that or is there still COVID concerns? 1008 00:57:39,550 --> 00:57:42,370 So I have indicated that I would like to go 1009 00:57:42,370 --> 00:57:45,220 on the cruises where the camera is deployed 1010 00:57:45,220 --> 00:57:47,557 as the main dude to help with that. 1011 00:57:47,557 --> 00:57:49,540 Okay, well, you'll see how that works. 1012 00:57:49,540 --> 00:57:52,560 Yeah, we're gonna try it, Calvin to get it working 1013 00:57:52,560 --> 00:57:55,990 with Jeff and I are over the winter and then Deanna 1014 00:57:55,990 --> 00:57:59,180 is gonna be involved with that with us and make sure 1015 00:57:59,180 --> 00:58:02,930 that we have two folks that know inside and out how to work 1016 00:58:02,930 --> 00:58:05,460 with the camera and the software in the field. 1017 00:58:05,460 --> 00:58:08,360 Meaning just some dock deployments? 1018 00:58:08,360 --> 00:58:11,240 Yep, dock deployments, maybe a little 1019 00:58:12,480 --> 00:58:16,210 small boat action if we can try it to get this thing 1020 00:58:16,210 --> 00:58:18,206 in the water and try to take some images. 1021 00:58:18,206 --> 00:58:20,810 And Julie Keister is on the line somewhere 1022 00:58:20,810 --> 00:58:23,840 and she's got some cool images of Puget Sound plankton. 1023 00:58:23,840 --> 00:58:26,240 So if we get some cool pictures there, we might be able 1024 00:58:26,240 --> 00:58:28,570 to compare to some of their work that she's done. 1025 00:58:28,570 --> 00:58:31,650 So we're, yeah, we're very excited about this 1026 00:58:31,650 --> 00:58:34,760 and hoping to have folks in the field that are gonna 1027 00:58:34,760 --> 00:58:37,210 be working with this stuff closely with you, Cal. 1028 00:58:37,210 --> 00:58:39,450 And then we also had the Google team actually come 1029 00:58:39,450 --> 00:58:44,450 to PMEL and give us a show and tell of their AI system, 1030 00:58:44,950 --> 00:58:46,050 it's quite impressive. 1031 00:58:48,081 --> 00:58:50,300 Yeah, and we need, we just need images. 1032 00:58:50,300 --> 00:58:52,810 We need images that are annotated to begin training 1033 00:58:52,810 --> 00:58:55,440 the system and that's really the, yeah, 1034 00:58:55,440 --> 00:58:58,074 that's really the part we're looking forward to. 1035 00:58:58,074 --> 00:59:00,830 So Deana has been really instrumental in moving 1036 00:59:00,830 --> 00:59:02,730 this needle on this. 1037 00:59:02,730 --> 00:59:05,130 We've been sorta talking about this for a while 1038 00:59:05,130 --> 00:59:06,767 but we haven't had people in here to work on it 1039 00:59:06,767 --> 00:59:08,880 and Deana has just really jumped right in 1040 00:59:08,880 --> 00:59:12,523 and grabbed the bull by the horns, so looking forward to it. 1041 00:59:13,450 --> 00:59:17,350 Perfect, I'm gonna say thank you to everybody 1042 00:59:17,350 --> 00:59:19,363 for tuning in, it's past 11. 1043 00:59:20,260 --> 00:59:22,760 Thank you so much again, Deanna, this was awesome. 1044 00:59:24,940 --> 00:59:28,070 We will see you in the EcoFOCI seminars 1045 00:59:28,070 --> 00:59:29,983 hopefully in the beginning of March. 1046 00:59:31,650 --> 00:59:32,900 Thank you everyone. 1047 00:59:32,900 --> 00:59:35,000 Thanks everyone for coming. 1048 00:59:37,593 --> 00:59:38,426 Bye.