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1 1 Skaala-2005 Dr. Øystein Skaala and Dr. Kevin A. Glover Institute of Marine Research GENIMPACT, Bergen 2-4 July 2007 Tracing escaped salmon to its sea.

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Presentasjon om: "1 1 Skaala-2005 Dr. Øystein Skaala and Dr. Kevin A. Glover Institute of Marine Research GENIMPACT, Bergen 2-4 July 2007 Tracing escaped salmon to its sea."— Utskrift av presentasjonen:

1 1 1 Skaala-2005 Dr. Øystein Skaala and Dr. Kevin A. Glover Institute of Marine Research GENIMPACT, Bergen 2-4 July 2007 Tracing escaped salmon to its sea cage of origin

2 2 2 Skaala-2005 Why do we want to trace the origin of escapees? 1)Are all escapements reported? 2)There is some uncertainty with regard to the relative amount of reported and unreported escapees 3)If we can detect unreported escapements, we will also get better statistics on number of escapees 4)We can also learn more about what was causing the escapement 5)White Paper no. 12 (2001 - 2002) “Clean, Rich Seas” –Recommendation from the Energy and Environment Committee, no 134, (2002 - 2003) regarding the establishment of national salmon rivers and salmon fjords –Tagging of farmed salmon suggested by Norwegian Parliament in 2003 6)The National Tagging Committee: –management authorities, aquaculture industry, research community –with the mandate to review a number of tagging/tracing systems that could be implemented in salmon farming

3 3 3 Skaala-2005 The National Tagging Committee (2004): Coded wire tags vs. The Stand by method Coded Wire Tags: Informative also when small numbers escape and are recaptured Informative even a long time after the escape incident Tagging must be conducted at the sea farm to reduce noise in results In conflict with animal welfare and also with market Requires also tagging of large numbers of fish that do not escape High cost, NOK 1,00–1,30 per fish plus development of methods Yearly cost in Norway: NOK 150–200 mill.

4 4 4 Skaala-2005 The National Tagging Committee (2004): Coded wire tags vs. The Stand by method The Stand by method: Only natural characteristics used: DNA profiles, lipid profiles, trace elements No adding of artificial tags of any kind Compares characteristics of escapees with salmon in farms within a geographical area Useful when large numbers escape Not useful to identify releases of small numbers (drip leakage), or origin of salmon which have been moving about for some time No data banks required Activity and funding only required in specific episodes

5 5 5 Skaala-2005 Breeding cores Broodstocks Smoltproducers (hundreds) Sea farms (hundreds) To reduce noise due to logistics, samples must be collected from the sea farms C an genetic data banks with information from the breeding stations help us identify escapees? The production chain in Norwegian salmon farming:

6 6 6 Skaala-2005 SampleNStrain Farm 1a Farm 1b Farm 1c Farm 2a Farm 2b Farm 3 Farm 4 59 53 54 58 60 59 69 Mowi/Bolaks NLA Mowi Bolaks Total412 Individual assignment of salmon from four farms by DNA microsatellite markers Wennevik et al 2005

7 7 7 Skaala-2005 Genetic distances between samples from sea farms

8 8 8 Skaala-2005 Genetic assignment (~%) Wennevik et al 2005 Farm 1aFarm 1bFarm 1cFarm 2aFarm 2bFarm 3Farm 4 Farm 1a 61 20102221 Farm 1b0 60 130000 Farm 1c030 87 0000 Farm 2a1520 59 3253 Farm 2b156028 43 00 Farm 300022 76 20 Farm 48002217 75

9 9 9 Skaala-2005 TRACES: Tracing escaped farmed salmon by means of naturally occurring DNA markers, fatty acid profiles, trace elements and stable isotopes Research partners: IMR (project leader) NINA VESO NGU SINTEF NVH DIFRES Univ. of Bergen Hardanger fish health network Rådgivende Biologer Steering committee: The Fisheries directorate The Directorate for nature management FHL (industry) Mattilsynet Norwegian Research Council County Governor of Hordaland Hardanger Anglers association

10 10 Skaala-2005 TRACES : aims Determine the accuracy of identifying the origin of escaped salmon by means of DNA microsatellite markers Determine the accuracy of identifying escapees by means of single nucleotide polymorphisms (SNP’s) Compare precision, costs and time efficiency of DNA microsatellites and SNP’s analysis Determine whether it is possible to distinguish between salmon from the same line of smolt that have been fed different feeds in different ongrowing facilities by means of fatty acid profiles, trace elements or stable isotopes Determine whether it is possible to distinguish between salmon from different hatcheries by trace elements

11 11 Skaala-2005 Work packages and responsible persons WP-1: DNA microsatellite genotyping and identification Dr. Øystein Skaala and Dr. Kevin Glover (IMR) WP-2: SNP genotyping and identification: Dr. Bjørn Høyheim (NVH) and prof. Michael Møller Hansen (DIFR) WP-3: Identification by lipid acid profiles and stable isotopes Dr. Bengt Finstad (NINA) and Dr. Marit Aursand (SINTEF) WP-4: Identification of farmed fish stocks based on traceelement analysis Dr. Vidar Moen, VESO and Dr. Belinda Flem, NGU WP-5: Sampling and assessment of escaped salmon Dr. Bjørn Barlaup (U.iB) and Dr. Ove Skilbrei (IMR) WP-6: Statistical support on research design and analysis of DNA microsatellites and SNP’s Prof. Michael M. Hansen, (DIFR) WP-7: Administration, meetings, reporting and publicity Project leader, Dr. Øystein Skaala

12 12 Skaala-2005 Genetic assignment identifies farm of origin for recaptured Atlantic salmon escapees in a Norwegian fjord. Glover, K. A., Skilbrei, O. T., Skaala, Ø. Submitted 2007

13 13 Skaala-2005 The Romsdalsfjord: 7 farms with 16 cages

14 14 Skaala-2005 Numbers of sea farms and smolt groups reared per farm Sea farmSmolt producers ABCDEFGHIJKL 1xxx 2x 3xx 4xx 5xxxxx 6xx 7x

15 15 Skaala-2005 Results of self-assignment. Overall assignment= 62.5% Farm 1A1B1C2D3E3F4G4H5I5J5G5K6E6F5B7L 1A2419-------------- 1B2127-------1------ 1C--48-----------11- 2D---2525---1-76--5 3E1--3264-----43--12 3F---7223------10--- 4G------17211271---- 4H1-----339--5----- 5I------2246-1---1- 5J--------138------ 5G------263--13---2- 5K---831---4-213--4 6E---249---1-1123--1 6F-------------471- 5B----------1---33- 7L---2114---1-32--25 % Co.As. 5159100535450358596812845491006953

16 16 Skaala-2005 Direct assignment of the 29 escapees to 16 baseline samples and exclusion TestFarm and cage 1A1B1C2D3E3F4G4H5B5I5J5G5K6E6F7LRej Direct assignment to farm Bayesian % 1313 2727 1313 0002727 1313 021 72 00001313 0- Nei % 1313 2727 1313 0001313 01313 19 66 02727 002727 0- Exclusion from farm 0.0125 2729 2025197262129 283 0.0526 2829 2225209282229 4 ”More than enough to initiate a police investigation”

17 17 Skaala-2005 Can lipid profiles provide additional information when genetic similarity between salmon farms is high? FarmForGj.snitt Vekt (g) SDVekt forpartikkel Farm1Lingalaks, BergdalenSkretting32008000.64 g Farm2Eide Fjordbruk, HisdalEwos370016001.29 g Farm3Tombre, Dysvik-48001100- farm4Hesvik, Søreid smoltEwos440015001.29 g

18 18 Skaala-2005 Lipid profiles in feed particles (%) ”Feed 2” og ”Feed 4” (Ewos) are similar, while ”Feed 1” (Skretting), has a somewhat different composition

19 19 Skaala-2005 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 201 202 203 206 207 208 209 210 211 212 213 214 215 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 401 402 403 404 405 406 407 408 409 410 412 413 414 415 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 201 202 203 206 207 208 209 210 211 212 213 214 301 301* 302 303 304 305 306 307 308 309 310 311 312 313 314 315 401 403 404 405 406 407 408 409 410 411 412 413 414 415 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 201 202 206 207 208 209 210 211 212 213 214 215 301 302 303 304 305 306 308 309 310 311 312 313 314 315 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 202* 203* Heart Gut fat Adipose fin Separation of four smolt groups by lipid profiles Skretting: 100 and 300 EWOS: 200 and 400 Each numer represents one fish

20 20 Skaala-2005 311 314 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 301 301* 302 303 304 305 306 307 308 309 310 312 313 315 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 302 303 304 305 306 308 309 310311 312 313 314 315 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 301 302 303304 305 306 307 308 309 310 311 312 313 314 315 Heart Gut fat Adipose fin And smolts from two farms Preliminary conclusion: there are additional signals in lipid profiles.

21 21 Skaala-2005 Conclusions: The Stand By Method using DNA markers etc. can in some cases identify the origin of escapees with the precision required to initiate inspection and investigation Quick sampling response is of fundamental importance, otherwise escapees may spread and mix with escapees from other sources (drip leakage) which will weaken the “signal” in the results Laboratory facilities: equipment, technician and scientist must be ”stand by” Sampling escapees: local fishermen or a mobile recapture unit in the Fisheries directorate must be in stand by position. Within 24 hours a sampling design for the geographic area must be selected and nets must out. Ideally 100 samples required. Location, date, weight, length, sex and maturation, external characteristics, fin clips (2ml eppendorf tubes, alcohol), scale samples (in envelopes), stomac contents Collecting baseline samples from salmon farms: Within 24 hours a sampling plan must be ready and representatives from the Fishery directorate collecting samples 50 finclips from each smolt group in 100ml jar with alcohol

22 22 Skaala-2005

23 23 Skaala-2005 WP-5 Sampling wild and farmed salmon

24 24 Skaala-2005 Undersøkte vassdrag i Hardangerfjordbassenget Bjoreio (4,6 km) Eio (1,7 km) Opo (1,5 km) Granvinselva (6,7 km) Øysteseelva (0,9 km) Steinsdalselva (5,1 km) Jondalselva (1 km) Øyreselva (1,1 km) Bondhuselva (2,5 km) Hattebergsvassdraget (2 km) Guddalselva nedstr. Seimfoss (0,2 km) Omvikselva (4,4 km) Etneelva - Storelva + Sørelva (12,5 km) Noen flere er kommet med fra 2005 eller 2006; Sima, Uskedalselva, Osa/Nordøla

25 25 Skaala-2005 Resultater fra tellingene av villaks i perioden 2004-2006

26 26 Skaala-2005 Resultater fra tellingene i perioden 2004-2006

27 27 Skaala-2005 Antall laks er gjennomgående svært lave sammenliknet med høyeste innrapporterte fangst i perioden 1970-2000. Dette tilsier at lakebestandene, med unntak av Etneelva, er i en kritisk situasjon. Beregningene av gytebestandsmålene, dvs. antall gytte egg per m 2, gir samme konklusjon.

28 28 Skaala-2005 Rød linje indikerer at størrelsen på gytebestanden er stor nok til at det blir gytt ca 3 egg/m 2 (stjerne indikerer at laksebestanden er fredet for fiske)

29 29 Skaala-2005

30 30 Skaala-2005 Atlantic salmon (Salmo salar L.) of farm, wild and hybrid parentage; migration and natural selection at EST- and MHC linked microsatellites (SALMARINE-08) The main objectives of the project are: To compare the mortality and migration patterns of salmon of farm, hybrid and wild parentage, through a fjord system To study natural selection at EST- and MHC-linked microsatellites within salmon of farmed, hybrid and wild parentage, reared in a natural environment To search for genetic markers, either DNA microsatellites or SNP’s that can distinguish between farmed and wild salmon To compare overall mortality, recapture rates, straying, growth and fecudity of naturally produced salmon of farm, hybrid and wild parentage from the smolt stage to maturation

31 31 Skaala-2005

32 32 Skaala-2005 The Hardangerfjord Heavy salmon farming region: Up to 50 farms Some years with high numbers of escapees Very good collaboration with salmon farmers

33 33 Skaala-2005 Heart Gut fat Adipose fin Only smolt from three farms

34 34 Skaala-2005 WP-4 Sporstoffanalysar (VESO / NGU) DESIGN: A. Innen og mellom individ innenfor en kohort og ett kar. Variasjon innen ett individ: 1. Mellom rastre innen eller langs en eller flere skleritter i ett skjell: - Langs en skleritt innenfor smoltsonen av skjellet - Mellom skleritter 2. Mellom skjell fra en og samme fisk: - Tre prøver langs en bestemt skleritt fra 5 skjell 3. Mellom otolitt og skjell fra en og samme fisk: - Nytter ”fargemerket” fisk med et distinkt merke i otolitt og i skjell. B.Variasjon mellom individer mellom kohorter: - Det nyttes ett godkjent skjell fra hver av 10 individer fra to kohorter fra ett kommersielt settefiskanlegg. Fra hver fisk taes det ut tre rastre fra et område på en-to skleritter (20 individer og 60 analysepunkter) Status: kjemiske analysar utført, resultata klare før 15.07.2007

35 35 Skaala-2005

36 36 Skaala-2005 A: Beinsetholmen merd 9 B: ” ” 10 C: Kråkenes ” 5 D: ” ” 10 E: Skarbukta ” 5,5 F: ” ” 54 G: ” ” 55 H: Gangstadbukta ” 2 I: ” ” 4 J: Juvika ” 4 K: Setervika ” 4 L: Furnes ” 1 M ” ” 3: N ” ” 4: O: ” ” 9 P: ” ” 13 X: rømt laks

37 37 Skaala-2005 1.Collecting baseline samples from salmon farms: Within 24 hours a sampling plan must be ready and representatives from the Fishery directorate collecting samples 50 finclips from each smolt group in 100ml jar with alcohol 2.Labelling: farm no 1, 2, 3, 4 etc, smoltgroup within farms: a, b, c, d etc. 3.Further information from each farm: smolt producer

38 38 Skaala-2005

39 39 Skaala-2005

40 40 Skaala-2005

41 41 Skaala-2005 Identifisering av matfisk i Hardangerfjorden (%) Wennevik et al 2005 Farm 1aFarm 1bFarm 1cFarm 2aFarm 2bFarm 3Farm 4 Farm 1a 61 20102221 Farm 1b0 60 130000 Farm 1c030 87 0000 Farm 2a1520 59 3253 Farm 2b156028 43 00 Farm 300022 76 20 Farm 48002217 75

42 42 Skaala-2005 WP-1 DNA microsatellite genotyping and identification Hardangerfjordmaterialet (2005 årsklassen): –Genotypa alle anlegg (19 markørar) –Genotypa ca 100 rømlingar frå 2005 årsklassen (innsamla 2006) Romsdalsfjorden –Genotypa alle aktuelle anlegg (7stk, til 16 merdar) –Genotypa rømlingar –Rapport til Fiskeridir. Ferdig –Manus sendt til internasjonal publisering

43 43 Skaala-2005 TRACES; Finansiering: søkt og innvilga (2006-2007) WPSøktInnvilga WP-1 DNA microsatellite3.6811.000 WP-2 SNP4.3501.000 WP-3 Lipids and stable isotopes Lipid acid profiles Stable isotopes 1.9501.000 WP-4 Trace element analysis3.2280.600 WP-5 Sampling farmed and escaped salmon1.8000.700 WP-6 Statistical support, research design0.7500.500 WP-7 Adm., meetings, trav., rep. & publicity (HI)1.0500.200 SUM16.8095.000

44 44 Skaala-2005 Kapasitet for sporing ved DNA mikrosatellittar Kapasitet på genotyping: ABI 3100> 3130> 3730: 48 kapillær, –3x så fort som tidlegare. 1000 ind. pr dag pr markør, 15 markørar: 5 markørar pr run: ca 300 ind pr dag. Opparbeiding, isolering, PCR oppformeiring: 2 veker Totalt 3 veker Potensiale: 1000 prøvar på 10 dagar ferdig resultat. –Føresetnadar: PCR robot, 2 teknikarar

45 45 Skaala-2005 WP-2: SNP genotyping and identification: Oppgaven Identifisering av SNP’s Designa primers Sekvensiering av SNP Gode SNP’ar til vidare identifisering Tusenvis 100-200 Ca 100 Ca 25 Forventing: Totalt 50 SNP klar for bruk i det vidare identifiseringsarbeid i løpet av 2007

46 46 Skaala-2005 WP-3:Lipid acid profiles and stable isotopes NINA (Bengt Finstad) SINTEF (Marit Aursand) Univ. I Bergen (Otto Grahl Nilsen)

47 47 Skaala-2005 Stabile isotopar (N og C) Kjemiske analysar så smått igang Forventa ferdigstilt pr 1.09.2007

48 48 Skaala-2005 Vidare framdrift juni - desember 2007 Ferdigstilling DNA mikrosat. data frå 2005 årsklassen: 15.07.2007 Ferdigstilling sporstoffanalysar VESO/NGU: 15.07.2007 Ferdigstilling fettsyreanalysar SINTEF: 15.07.2007 Ferdigstilling stabile isotopar: 15.07.2007 Utvalg av panel med 25 SNP’s plukka frå biblioteket: 15.07.2007 Utvalg av panel med 50 SNP’s plukka frå biblioteket: 1.12.2007 Materiale samla inn av 2007 vår og haust smoltutsetta (juli / sept.) Telling av rømt og vill laks i 12 vassdrag i Hardangerfjorden: 15.11.2007 Innsamling av rømlingar Hardangerfjordbassenget: 1.12.2007

49 49 Skaala-2005 A: Beinsetholmen merd 9 B: ” ” 10 C: Kråkenes ” 5 D: ” ” 10 E: Skarbukta ” 5,5 F: ” ” 54 G: ” ” 55 H: Gangstadbukta ” 2 I: ” ” 4 J: Juvika ” 4 K: Setervika ” 4 L: Furnes ” 1 M ” ” 3: N ” ” 4: O: ” ” 9 P: ” ” 13 X: rømt laks


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