Spatial-Temporal Distribution Characteristics of Bigeye and Yellowfin Tunas in Kiribati waters

Transcription

1 National Taiwan Ocean University Aketa Mature Taanga, Yi-Hui Cai, Hsueh-Jung Lu, I-Hsun Ni *, Spatial-Temporal Distribution Characteristics of Bigeye and Yellowfin Tunas in Kiribati waters Aketa Mature Taanga, Yi-Hui Cai, Hsueh-Jung Lu & I-Hsun Ni*,

2 起網作業 Tsai TP 00

3 Longliner Chen CL 00

4 Sustainably managing renewable resources - to reduce fishery pressure FAO () adopted IPOA-Fishing capacity calling upon 0-0% reduction for longliners Longlining accounts for % global tuna production There are large-scale tuna longline vessels in 00 Taiwan has longliners (% of the world) Taiwan has spent >US$00 m dollars (% from Tuna association and % from Government) and scratched (0% of total) active longliners (>m, 00t) since 00 (, & longliners in yrs) During , Fishing vessels has been down sized from,000 to,000 (0% reduction, use as vessel reefs) and tonnage from,000 to 0,000 (%)

5 International Workshop/Conference on Sustainable Fisheries Management in Tropical/Subtropical Regions

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7 participants from countries with international ti well-respected td speakers from additional countries fisheries mangers (Directors, Associate/Assistant Directors, Senior Mangers, etc.) professors ( Dean Head professors ( Dean, Head, Professors)

8 遠發中心 國際會議廳

9 Workshop (contents) A. World Fisheries and Aquatic Technology Overview B. Fisheries Management and Policy around the World: all participant s national fishery management & policy C. Fisheries Management: Taiwanese experiences D. Other essential topics in Fisheries science (Fishing technology, Aquaculture, Seafood science, etc.) E. Field studies: TFRI fishery research vessels, Fishing port, fisher s associations, fish wholesale market, seafood tourism market, seafood manufactory, whale watching, marine biology museum, aquariums, coastal cage culture, sea ranching, and fish fry nursery, etc.

10 七月二十四日邵所長廣昭講授 生物多樣性及魚類資料庫 及各國漁情報告 ( 海洋大學延平技術大樓演講廳 )

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12 Taiwan Fisheries Research Institute 七月二十五日 參觀拜會農委會水產試驗所 -- 水試一號研究船 ( 基隆市和平島 )

13 DongGang Fishing Harbor & Fishers Organization

14 NanFangO Fishers 九十一年度課程剪影 Organization 七月二十六日拜會蘇澳區漁會及參觀漁港設施 ( 宜蘭縣南方澳漁港 )

15 參訪漁業網具製造工廠 Fishing Gears

16 參訪漁業網具製造工廠 Fishing Gears

17 National Museum of Marine Biology

18 Marine Biology Museum 九十一年度課程剪影 七月二十八日 參訪國立海洋生物博物館 ( 屏東縣車城鄉 )

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20 National Traditional Art Center 九十一年度課程剪影 七月二十六日 文化之旅參觀活動 國立傳統藝術中心 ( 宜蘭縣五結鄉 )

21 TaiLuGe National Park 九十一年度課程剪影 七月二十七日 戶外參觀活動 太魯閣國家公園中橫公路九曲洞段 ( 花蓮縣新城鄉 )

22 Aboriginal Cultural Festival

23 Taiwanese Fishers Festival in Sun Moon 九十一年度課程剪影 Lake 七月二十日 參加漁民節活動合影於光華島 ( 日月潭 )

24 Taiwanese Fishers Festival in Sun Moon Lake With 九十一年度課程剪影 Director General FAN ZZ of Council of Agriculture 七月二十日參加漁民節表揚大會與我國漁業政要合影 ( 日月潭 )

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27 From Dir JA Al-Qaseer, Directorate of Fisheries & Marine Resource, Kingdom of Bahrain, thank you so much for your successful of the organizing the workshop, I am sure that, all, our colleagues share with me in this point, that we gain a lot of information and idea to adding on our knowledge. Truly, we appreciate your high effort, and for your hospitality and help during the workshop ; From Wilson Celestin, Professor of Agronomy and Veterinary Medicine of Haiti The coordinator Dr. Ni and Dr. Lu were just perfect serious and be able to make the program enjoyable need to continue this program in the future ; From Arthur Hosten, General Fisheries Manger of Grenada, Dr. Ni and Dr. Lu did excellent job in putting this workshop together. I believe it should be held on a yearly y basis since the knowledge gain could benefit small and developing state with their fisheries programme ; From Jaime Villanueva, Fisheries officer of Belize, Dr. Ni has vast knowledge on most of the topics discussed in the workshop, he...had had done a wonderful job in organizing this successful workshop ; From Ben Saqata, Marine Biologist from Fiji Island, I must not forget to thank the hard working Professor Ni & Dr. Lu arranging courses, trips and taking care of students like good sheppard concerning his sheep. Thank you so much ; From Marissa Albaladejo, Chief of Fishing Policy & Economics of Philippines Dr. Ni and Dr. Lu good administrator...highly experience this workshop should be pushed thru coming gy years ; From Alfred Lebehn of Federal States of Micronesia Great help from Dr. Ni I really recommend this workshop be continued in future and to have Dr. Ni and Dr. Lu to be the same host highly academic, well outlined and introduced ; From Elena Borisova, Head of Reforming and Forecasting Development of Agriculture Division of Russia, The exchange of views and information was very interesting and useful for our professional activities Sincerely thank to professor Ni for his hard working such a important program I hope this program will be continued.

28 National Taiwan Ocean University Spatial-Temporal Distribution Characteristics of Bigeye and Yellowfin Tunas in Kiribati waters Aketa Mature Taanga, Yi-Hui Cai, Hsueh-Jung Lu, and I-Hsun Ni*

29 Where is Kiribati? Size of Taiwan:,.0 km² Kiribati EEZ: ~.million km² area of Taiwan Kiribati EEZ in regions Western Central Pacific Ocean. Source: CES SPC, Noumea Gilbert region Zone,0, km² Phoenix region Zone,00 km² Line region Zone,,000 km²

30 Introduction Kiribati government major revenues

31 Spatial-Temporal Distribution Characteristics of Bigeye and Yellowfin Tuna in Kiribati EEZ, during the period of -00. Zone : Gilbert Region (KI) (Western Region) Zone : Phoenix Region (PX) (Central Region) Zone : Line Region (LN) (Eastern Region) Longline Catch Statistic BET & YFT Korean Catch & Effort Data Distribution & Concentration of Yellowfin and Bigeye Tuna Environmental Data ONI SST anomaly Depth of 0 C isotherm SST Inter-relationship of Yellowfin and Bigeye Tuna with the Environment Effect of El Nino Southern Oscillation (ENSO) on the distribution of Yellowfin and Bigeye Tuna

32 Yellowfin Tuna (YFT) Thunnus albacares (Bonnaterre, ) Max. size: cm FL Max. published weight: 00.0 kg Importance: fisheries - highly commercial; gamefish Environment: migratory pelagic

33 Bigeye tuna (BET) Thunnus obesus (Lowe, ) Max. size: 0 cm TL Max. published weight:.0 kg Importance:fisheries - highly commercial; gamefish Environment: marine pelagic; oceanodromous

34 No. of Fish 0000 Total Number of Tuna species Catch for Longlines in Kiribati EEZ (-00) 0000 YFT BET ALB Time Periods Fig. Time Series (-00) showing average monthly catch (No. of fish) of the major tuna species for Longline in Kiribati.

35 Materials and Methods Materials Monthly x grid Catch and Effort Data from -00 of Korea Longliners in zones (Secretariat of Pacific Community). Monthly Average x Sea Surface Temperature (SST), from -00. ( Monthly. x grid Depth of 0 C isotherm (proxy for thermocline depth, TD) from -00. ( NCEP/ EMC/ CMB/ P acific/.monthly) Oceanic El Nino Index (ONI), from -00. ( Sea Surface Anomalies of the study area (Climb000 software)

36 Major El Nino and La Nina periods. El Nino periods. ONI ( 0. C) 0. 0 DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ ONI ( C) La Nina Periods

37 El Nino Southern Oscillation (ENSO) effect on the BET & YFT, within each zone. Table. El Nino and La Nina Periods. La Nina Periods Neutral Periods El Nino Periods May-Dec Jan-Apr Apr-Dec- Jan-May Jun-Dec Jan-May Apr-Dec Jan-Mar Apr-Dec Jan-Apr Jun-Dec-000 Jan-July May-Dec Jan-May CPUE of BET/YFT in x grid was average for El Nino and La Nina periods for each zones. For each zone, a paired t-test is used to compare between El Nino and La Nina, the CPUE of BET/YFT in the x grid. The same procedure was done for Sea surface temperature and Depth of 0 C Isotherm MapInfo software later utilize to overlay the difference in CPUE of BET/YFT with depth 0 C Isotherm during El Nino and La Nina.

38 Methods I. Tuna catches among the three areas Only chose Korean dataset. An average monthly time-series of total catches for Yellowfin and Bigeye Tuna was plotted (-00). II. Comparison of BET & YFT distribution among the zones CPUE = No. of Fish Caught /0 Hooks (Index of abundance) A paired t-test used to compare the annual average of CPUE in each x grid between BET & YFT within zones. Annual average of CPUE for each species for each zones was also plotted Annual average of CPUE for each species for each zones was also plotted for comparison among zones.

39 Fig. a Proportion of Catch Data for Different fishing Nations in Kiribati EEZ.%.0% 0.%.% CN JP KI KR TW VU Korean 0.% Fig. b.0% Zone Zone Zone Fig. a) Time Series (-00) showing average monthly catch (N) of the major tuna species for Longline. Fig. b) Percentage of Catch data for Different fishing nation in zone, zone and zone in Kiribati EEZ

40 III. Comparison of Sea surface temperature and Depth of thermocline among the zones Time-longitudinal (plot of the average Sea Surface Temperature (SST) and Depth 0 C isotherm within a N- S was uploaded from TAO Data Display web for comparison of the environmental conditions of three zones Time-series of average depth of thermocline among zones was also plotted IV. Inter-relationship ti between Oceanographic and CPUE data Correlation of CPUE with Sea Surface Temperature (SST) and Depth of 0 C Isotherm (proxy for thermocline depth). V. Effect of ENSO on the distribution of yellowfin and bigeye tuna and environmental conditions of the zones Defining the reliability of ONI Correlation of ONI with CPUE of BET YFT Define El Nino and La Nina periods using ONI

41 RESULTS AND DISCUSSION Comparison of BET & YFT distribution among the zones

42 Total catch of both Yellowfin and Bigeye tuna in zone 0000 BET YFT )Total catch of both Yellowfin and Bigeye gy tuna in zone Catch (No. of fish) Catch (No. of fish) Total catch of both Yellowfin and Bigeye tuna in zone Time period Catch (No. of f fish)

43 Result of Paired t-test Comparison between average CPUE of BET and YFT in x grid of each zones. Year Zone Zone Zone t-value DF t-value DF t-value DF -.0*** -.***.*** -..0.***.0.***.00*** -.0*** -.***.*** *** -.***.*** ** ***.*** 00 -.* -0.0.* 00 -.** -.0***.* t-value indicates significant level at 0.0*, 0.0**, 0.00***

44 Comparison of the average CPUE of Bigeye and yellowfin tuna among the three zones CP PUE (No./HHo ooks) BET YFT Zone ) (No./HHooks) Zone 00 CPUE CPUE (No./H HHooks) Zone Years Fig. Comparison of averaged CPUE of Bigeye Tuna (BET) and Yellowfin Tuna (YBT) among the three zones: (a) Zone (KI), (b) Zone (PX) (c) Zone (LN)

45 Comparison among periods Periods BET CPUE YFT CPUE SST TD SST TD La Niña 0.() -0.() 0.() ns Normal ns -0.() -0.() ns El Niño ns -0.() ns ns SST: Sea Surface Temperature; TD: Thermocline Depth; ns: no significance; (no.): no. means sample size Correlation: r = -0. BET TD % confidence

46 .. BET ONI La Niña El Niño 0. CPUE(no/HHooks) ONI BET YFT ONI La Niña El Niño 0 (no/hhooks) ONI - YFT CPUE(

47 ANOVA to discuss the zones and ENSO periods Factor DF BET YFT F-value p-value F-value p-value Model. <.000***. <.000*** Zone 0. <.000***. <.000*** ENSO.0 <.000***. 0. Zone*ENSO. <.000***. <.000*** DF= degree of freedom p<0.00*** is significant difference. Use two-way ANOVA to compared with BET and YFT (BET is significant between factors; YFT is significant in Zone and interaction factor; no significant in ENSO factor.)

48 -0. ENSO; LS Means Wilks lambda=., F(, )=., p= Effective hypothesis decomposition Vertical bars denote 0. confidence intervals -0. Zone*ENSO; LS Means Wilks lambda=., F(, )=., p= Effective hypothesis decomposition Vertical bars denote 0. confidence intervals lnbet T YFT-not significant El nino La nina Normal ENSO lnbet lnyft BET catch more in El nino Zone El nino La nina normal Zone; LS Means Wilks lambda=., F(, )=0.00, p= Effective hypothesis decomposition Vertical bars denote 0. confidence intervals BET in zone more than & YFT in zone & more than Zone*ENSO; LS Means Wilks lambda=., F(, )=., p= Effective hypothesis decomposition Vertical bars denote 0. confidence intervals lnyft T Zone lnbet lnyft -. YFT catch more in El nino in zone Zone & catch more in normal. -. Zone El nino La nina normal

49 Zone and ENSO interaction () La Nina Normal El Nino La Nina Normal El Nino ln BET ln YFT La Nina Normal El Nino Zone Zone Zone

50 Zone and ENSO interaction () Zone Zone Zone ln BET ln YFT Zone Zone Zone Zone Zone Zone La Nina Normal El Nino

51 Zone Zone Zone Zone Zone Zone

52 A. Taanga

53 Table. Coefficient Correlation of Monthly Average Catch rate with Environmental Data Zone (KI) SST 0 C Isotherm CPUE_BET CPUE_YFT SST C Isotherm -0.*** CPUE_BET C Isotherm Zone (PX) CPUE_BET 0.*** -0.0*** CPUE_YFT Zone (LN) SST *** C Isotherm -0.0*** 0.00 CPUE_BET 0.00**

54 Average Depth of 0 degree cel. Isotherm in the three Zones Years zone Zone Zone Fig. Time Series of the Depth with 0 C Isotherm (a proxy of thermocline depth) in Zone (KI), Zone (PX) and Zone (LN) Depth (m)

55 Relationship of BET_CPUE with Thermocline depth in Zone (KI) BET_CPUE Themocline depth. 0 CPUE (No/H HHooks) zone (KI) r = -0.*** m) Depth ( Relationship of BET_CPUE with Thermocline depth in Zone (PX) BET_CPUE Themocline depth. 0 (No/HHooks) CPUE zone (PX) r = -0.*** De epth (m) Relationship of BET_CPUE with Thermocline depth in Zone (LN) BET_CPUE Themocline depth. 0 CPUE (No/HHo ooks) zone (LN) r= -0.* Depth (m) Fig. Time-series of the Quarterly average BET CPUE and Depth of 0 C Isotherm a) zone (KI) b) zone (PX) and c) zone (LN)

56 RESULTS AND DISCUSSION El Nino Southern Oscillation (ENSO) effect on the BET & YFT tuna species within each zone.

57 The warm pool and Sea Surface Temperature in Pacific Ocean for to Huang et al. 00 Data source: IGOSS/NOAA

58 Strong wind La Niña Strong Upwelling - Warm pool > El Niño Weak wind Weak Upwelling

59 Impact of Global Climate Change LU et al 000 BET

60 Zone Zone Zone Lu et al. 00 Fig. Changes in habitat of BET in the Tropical Pacific Ocean.

61 Lu et al. 00 Fig. Changes in habitat of YFT in the Tropical Pacific Ocean.

62 Conclusion Yll Yellowfin and dbigeye Tuna are the major target t species of Distant Longline fleets in Kiribati EEZ Korea is the major foreign Country with composed nearly % of all total tuna harvested from Kiribati EEZ. BET and YFT as well as environmental conditions were different among the three zones.

63 Conclusion Zone is relatively colder compared to west Gilbert and central Phoenix Thermocline depth (depth of 0 C Isotherm) is relatively shallower in the east Line region (Zone ) compared to central Phoenix region (Zone ) and west gilbert region (zone ) YFT Distribution of Bigeye and Yellowfin Tuna YFT BET Prefer warmer water Prefer colder water

64 Conclusion Comparison of tuna species & environmental data (SST & TD) within zones During El Nino During La Nina Warm water Higher SST BET YFT Lower SST YFT Higher SST Higher SST BET Lower SST BET BET BET Lower SST Warm water Warm water BET Thermocline depth become shallower Thermocline depth become deeper

65 Conclusion BET increased directly with the increase in SSTA of Nino. region in zone. month time lagged in zone and month in zone. YFT took longer time responding in all three zones. BET:El Nino enhance, and La Nina reduce in all three zones. YFT:La nina increase, especially in zone. For Kiribati basic management.

66 Thanks for your attention