本研究中，使用時間及空間維度用來了解環境因子與阿根廷魷資源量波動的 關係。 本研究的結果表明，區域性大氣驅動力對阿根廷魷資源量之變動有顯著相 關，因此在評估環境因子與阿根廷魷資源量的關係時，除常用的 SST 外，亦應將 大氣驅動力納入考量，此一結果符合本研究之假設。 而本研究發展的包含 4 參數 的 GLM 經驗模型可解釋 83%的阿根廷魷豐度波動，相較於過去的研究，解釋率提 高許多，而模擬預測的結果與實際觀測值有相同趨勢，亦增加模式的可性度。 另 一方面，目前學者提出的預測模式，可提前 8 個月預測阿根廷魷的資源趨勢，本 研究提出以 AAO 為環境因子進行預測，最早或可在漁季前 2 年即預測阿根廷魷資 源變化趨勢，對於阿根廷魷漁業的管理，可為一有效應用。 此外，本研究也首次 嘗試採用地學統計分析技術進行阿根廷魷的族群資源量評估。 在西南大西洋漁場 的阿根廷魷，其分布既非均勻也非隨機模式，而具有不同程度的空間差遲自相關。
因為克利金法所得之估值與來自漁場的實際觀察高度相關，故使用空間分析所獲 得的參數以克利金法量化年度空間結構有其價值。 所研究年度的魷魚總生物質量 估計至少為年度漁獲量的兩倍以上，顯示在目前的漁場開發與管理機制下，阿根 廷魷仍處於健康狀態。
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TABLES
Table 1. The coefficient of correlation (r) between the log-transformed catch per unit of effort (logU) of the Argentine shortfin squid (Illex argentinus) in the southwest Altantic during 1986–2010 and the environmental variables in the same fishing season and with time lags of the previous 1 and 2 years. Variables with P-values <0.05 are shown, including subsurface seawater temperature on the northern and southern Patagonian shelf and Antarctic Oscillation (AAO) indices. Note that N_5m and S_5m stand for the subsurface seawater temperature at a depth of 5 m at the northern (36°S, 53°W) and southern (50°S, 60°W) Patagonian shelf, respectively. The fishing season lasts from the November of the previous year to the June of the current fishing season.
Environmental factors
Previous 2 years Previous 1 year Fishing season
Month r P Month r P Month r P
N_5m – – – March −0.452 0.030 – – –
S_5m – – – March −0.477 0.021 February −0.573 0.005
– – – – – – March −0.596 0.003
– – – – – – April −0.573 0.005
AAO November −0.478 0.016 – – – December 0.401 0.047 December −0.564 0.003 – – – – – –
March 0.565 0.003 – – – – – –
May 0.436 0.030 – – – – – –
Table 2. List of coefficients of generalized linear models used for analyses of the influence of environmental factors on catch per unit of effort (CPUE) for the Argentine shortfin squid (Illex argentinus) in the southwest Atlantic, P-values, coefficients of multiple determination (R2), and Akaike’s information criteria (AIC) that resulted from backward stepwise procedure for predicting the squid's abundance. The dependent variable is the log-transformed annual CPUE (logU) from 1986 through 2007. Note that PPANov, PPAMar, and PPADec represent 2-year-lagged Antarctic Oscillation indices in November, March, and December, respectively, before the fishing season; S_5m_Mar and PS_5m_Mar stand for subsurface seawater temperatures at a depth of 5 m at the southern Patagonian shelf in the concurrent and previous March, respectively; and PN_5m_Mar represents the subsurface seawater temperatures at a depth of 5 m at the northern Patagonian shelf in the previous March.
Model Intercept
Environmental variables
Table 3. List of environmental variables in the predictive generalized linear models used in analyses of the influence of environmental factors on catch per unit of effort for the Argentine shortfin squid (Illex argentinus) in the southwest Atlantic, when progressive analysis was performed with data from 1998 through 2007. The shading in a cell denotes that a variable was included in the model with either positive (+) or negative (−) loading. All coefficients in models were significant at P-value <0.05. Note that PPANov, PPADec, and PPAMar represent 2-year-lagged Antarctic Oscillation indices in November, December, and March, respectively, before the fishing season; PS_5m_Mar and S_5m_Mar represent subsurface seawater temperatures at a depth of 5 m at the southern Patagonian shelf in the previous and concurrent March, respectively; and R2 represents the coefficient of multiple determination.
Year Environmental variables
R2 (%) PPANov PPADec PPAMar PS_5m_Mar S_5m_Mar
1998 − + − 91.3
1999 − + − 93.9
2000 − + − 90.2
2001 − + − 90.6
2002 − + − 90
2003 − + − 87
2004 − + − 84.5
2005 − + − 78.7
2006 − + + − 82.2
2007 − + − − 83
Table 4. Parameters and goodness of fit for variogram model estimation for the Argentine shortfin squid (Illex argentines) in the Southwest Atlantic.
Year Model Parameters Goodness of fit
Nugget Scale Length % R2
1999 Linear + 0.05 (Slope)
Spherical 0.240 0.176 1.716 96.2 0.969
2004 Spherical 0.130 0.297 2.075 68.1 0.690
2007 Spherical 0.16 1.766 2.805 87.6 0.879
2010 Exponential 0.036 0.304 2.703 84.3 0.844
2011 Spherical 0.059 0.739 1.930 88.0 0.881
2012 Spherical 0.154 0.220 2.993 53.5 0.707
Table 5. Cross-validation of Kriging estimate, average CPUE (U) and integrated annual biomass of the Argentine shortfin squid (Illex argentinus) in the Southwest Atlantic squid jigging ground.
Year Model Cross-validation
Avg. U Std Biomass Catch Exp. rate
a b R2
1999 Linear + Spherical
0.861 0.657 0.817 4.768 2.605 780,474 263,434 33.7%
2004 Spherical 0.319 0.664 0.885 0.928 0.399 109,815 9,767 8.89%
2007 Spherical 0.292 0.887 0.981 6.387 3.860 1,093,013 284,562 26.0%
2010 Exponential 0.307 0.764 0.952 1.352 0.426 225,776 30,543 13.5%
2011 Spherical 0.195 0.901 0.990 2.040 1.248 258,932 69,577 26.9%
2012 Spherical 1.065 0.511 0.811 2.887 0.416 400,697 83,949 2.10%
FIGURES
Fig. 1: The world fishery catch of non-cephalopods and cephalopods since 1950.
1940 1960 1980 2000 2020
Year 0
20,000,000 40,000,000 60,000,000 80,000,000 100,000,000
tons (non-cephalopods catch)
0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000
tons (Cephalopods catch)
Non-cepholopods catch Cepholopods catch
Fig. 2: The world fishery catch of cephalopods with different orders since 1950.
The ommastrephid is the biggest catch of cephalopods around the world, included more than 40% of cephalopods catch since 1991.
1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year
0 500,000 1,000,000 1,500,000 2,000,000 2,500,000
tons
Shortfin squid Longfin squid Octopus Others
Fig. 3: The world fishery catch of shortfin squid since 1950.
1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year
0 400,000 800,000 1,200,000
tons
Illex argentinus Todarodes sagittatus Todarodes pacificus Dosidicus gigas
Ommastrephes bartramii Illex illecebrosus Nototodarus sloanii
Fig. 4: Annual catch (in metric tons [t]) of the Argentine shortfin squid (Illex argentinus), based on data from catch logs of Taiwanese jiggers that operated in the southwest Atlantic from 1986 through 2013.
Taiwan catch World catch
1990 2000 2010
Year 0
400,000 800,000 1,200,000
Catch (t)
Fig. 5: Map of the study area (34–55°S, 50–70°W) and spatial distribution of annual mean catch per unit of effort (CPUE), measured in metric tons (t) per vessel per day, of Argentine shortfin squid (Illex argentinus) from 1986 through 2010 in the southwest Atlantic. The dashed line indicates the 200-m isobath, and the 2 black stars indicate the 2 locations on the Patagonian shelf where subsurface seawater temperatures were taken:
36°S, 53°W (north) and 50°S, 60°W (south). The area of the Antarctic Oscillation (AAO) is shown between 40°S and 65°S.
200 m
Southwest Altantic
Falkland Interim Conservation Zone
CPUE (t vessel-day-1)
<1
AAO area between 40o S and 65o S
45oW
The subsurface water temperatures download from IRI website with 0.5o resolution
36oS 35.5oS
36.5oS
43oW 42.542.5oo 43.5o
The reference locations seawater temperatures were calculated by averaging the water temperatures at 4 surrounding data points.
Fig. 6: Monthly subsurface water temperatures at the 2 reference locations were calculated by averaging the water temperatures, from the IRI database,
Fig. 6: Monthly subsurface water temperatures at the 2 reference locations were calculated by averaging the water temperatures, from the IRI database,