The skipjack tuna fishery in the west-central Pacific Ocean: applying neural networks to detect habitat preferences

Spatial models for habitat selection were developed using neural networks. The model specifications were elucidated from model construction, training, validating, testing, and interpretation, and applied to skipjack tuna in the west-central Pacific Ocean. The model was created using commercial data from the Oceanic Fisheries Programme of the South Pacific Fisheries Commission and oceanic environmental data include sea surface temperature, horizontal gradient of sea surface temperature calculated from sea surface temperature, sea surface height, and chlorophyll-a. Local abundance indices for skipjack tuna were compiled using catch per unit effort, catch or effort. The optimal neural network models for each abundance index were selected by mean square errors and average relative variances. The predictive ability for optimal neural network models was evaluated by the R ² value using a cross-validation approach. The accuracy and stability of the optimal models, the contribution of independent variables, and the distribution of spatial sensitivity analyses were shown to vary with the abundance index chosen as the response variable. Chlorophyll-a was the most significant oceanographic factor in habitat selection. These results improve our understanding of how best to apply neural networks for modeling habitat selection by skipjack tuna.     

Operational oceanography applied to skipjack tuna (Katsuwonus pelamis) habitat monitoring and fishing in south‐western Atlantic

Skipjack tuna (Katsuwonus pelamis) ranks third among marine resources that sustain global fisheries. This study delimits the spatiotemporal habitat of the species in the south‐western Atlantic Ocean, based on operational oceanography. We used generalized additive models (GAMs) and catch data from six pole‐and‐line fishing vessels operating during 2014 and 2015 fishing seasons to assess the effect of environmental variables on catch. We also analysed Modis sensor images of sea surface temperature (SST) and surface chlorophyll‐α concentration (SCC) to describe fishing ground characteristics in time and space. Catch was positively related to thermocline depth (24–45 m), SST (22–24.5°C), SCC (0.08–0.14 mg/m³) and salinity (34.9–35.8). Through SST images, we identified that thermal fronts were the main surface feature associated with a higher probability to find skipjack. Also, we state that skipjack fishery is tightly related to shelf break because bottom topography drives the position of fronts in this area. Ocean colour fronts and plankton enrichment were important proxies, accessible through SCC, used to delineate skipjack fishing grounds. Catch per unit effort (CPUE) was higher towards summer (median 14 t/fishing day) due to the oceanographic characteristics of the southern region. High productivity in this sector of the Brazilian coast defines the main skipjack feeding areas and, as a consequence, the greatest abundance and availability for fishing.     

Environmental associations of Pacific bluefin tuna (Thunnus orientalis) catch in the California Current system

We investigate the impact of oceanographic variability on Pacific bluefin tuna (Thunnus orientalis: PBF) distributions in the California Current system using remotely sensed environmental data, and fishery‐dependent data from multiple fisheries in a habitat‐modeling framework. We examined the effects of local oceanic conditions (sea surface temperature, surface chlorophyll, sea surface height, eddy kinetic energy), as well as large‐scale oceanographic phenomena, such as El Niño, on PBF availability to commercial and recreational fishing fleets. Results from generalized additive models showed that warmer temperatures of around 17–21°C with low surface chlorophyll concentrations (<0.5 mg/m³) increased probability of occurrence of PBF in the Commercial Passenger Fishing Vessel and purse seine fisheries. These associations were particularly evident during a recent marine heatwave (the “Blob”). In contrast, PBF were most likely to be encountered on drift gillnet gear in somewhat cooler waters (13–18°C), with moderate chlorophyll concentrations (0.5–1.0 mg/m³). This discrepancy was likely a result of differing spatiotemporal distribution of fishing effort among fleets, as well as the different vertical depths fished by each gear, demonstrating the importance of understanding selectivity when building correlative habitat models. In the future, monitoring and understanding environmentally driven changes in the availability of PBF to commercial and recreational fisheries can contribute to the implementation of ecosystem approaches to fishery management.     

中西太平洋鲣鱼丰度的时空分布及其与表温的关系

中西太平洋是全球金枪鱼围网的主要海域,鲣鱼(Katsuwonus pelamis)是金枪鱼围网的主要作业对象。本研究利用1983～2007年中西太平洋金枪鱼围网渔获物数据,结合海洋表层温度(SST)数据,分析中西太平洋鲣鱼资源丰度在时间序列和空间位置上的分布规律。研究表明,1983～2002年,各年平均CPUE在时间序列上呈一定的上升趋势,1983～2002年,平均SST在一定范围内上下波动,平均CPUE和平均SST无显著相关性;2003～2007年,平均CPUE和平均SST均呈较大幅度上升,两者呈显著相关。从空间位置分析,鲣鱼资源量集中出现在SST为28～30℃之间的海域,在5°N和10°S附近海域CPUE反映的总体资源量较高,而在0°和5°S的资源量较低。鲣鱼资源量较大区域分布在冷暖水团交汇处。     

Association between the interannual variation in the oceanic environment and catch rates of bigeye tuna (Thunnus obesus) in the Atlantic Ocean

The environmental processes associated with variability in the catch rates of bigeye tuna in the Atlantic Ocean are largely unexplored. This study used generalized additive models (GAMs) fitted to Taiwanese longline fishery data from 1990 to 2009 and investigated the association between environmental variables and catch rates to identify the processes influencing bigeye tuna distribution in the Atlantic Ocean. The present findings reveal that the year (temporal factor), latitude and longitude (spatial factors), and major regular longline target species of albacore catches are significant for the standardization of bigeye tuna catch rates in the Atlantic Ocean. The standardized catch rates and distribution of bigeye tuna were found to be related to environmental and climatic variation. The model selection processes showed that the selected GAMs explained 70% of the cumulative deviance in the entire Atlantic Ocean. Regarding environmental factors, the depth of the 20 degree isotherm (D20) substantially contributed to the explained deviance; other important factors were sea surface temperature (SST) and sea surface height deviation (SSHD). The potential fishing grounds were observed with SSTs of 22–28°C, a D20 shallower than 150 m and negative SSHDs in the Atlantic Ocean. The higher predicted catch rates were increased in the positive northern tropical Atlantic and negative North Atlantic Oscillation events with a higher SST and shallow D20, suggesting that climatic oscillations affect the population abundance and distribution of bigeye tuna.     

Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data

Satellite‐based oceanographic data of sea surface temperature (SST), sea surface chlorophyll‐a concentration (SSC), and sea surface height anomaly (SSHA) together with catch data were used to investigate the relationship between albacore fishing ground and oceanographic conditions and also to predict potential habitats for albacore in the western North Pacific Ocean. Empirical cumulative distribution function and high catch data analyses were used to calculate preferred ranges of the three oceanographic conditions. Results indicate that highest catch per unit efforts (CPUEs) corresponded with areas of SST 18.5–21.5°C, SSC 0.2–0.4 mg m^?3, and SSHA ?5.0 to 32.2 cm during the winter in the period 1998–2000. We used these ranges to generate a simple prediction map for detecting potential fishing grounds. Statistically, to predict spatial patterns of potential albacore habitats, we applied a combined generalized additive model (GAM) / generalized linear model (GLM). To build our model, we first constructed a GAM as an exploratory tool to identify the functional relationships between the environmental variables and CPUE; we then made parameters out of these relationships using the GLM to generate a robust prediction tool. The areas of highest CPUEs predicted by the models were consistent with the potential habitats on the simple prediction map and observation data, suggesting that the dynamics of ocean eddies (November 1998 and 2000) and fronts (November 1999) may account for the spatial patterns of highest albacore catch rates predicted in the study area. The results also suggest that multispectrum satellite data can provide useful information to characterize and predict potential tuna habitats.     

基于不同模型研究环境因子对中西太平洋鲣资源丰度的影响

根据1998—2013年中西太平洋鲣(Katsuwonus pelamis)生产数据,选取时空因子(年、月、经纬度)和环境因子[海表面温度(SST)、海表面高度(SSH)、尼诺指数(ONI)和叶绿素a浓度]Chl-a)],通过两种不同的模型(广义加性模型GAM和提升回归树模型BRT)研究各因子对鲣资源丰度(以CPUE表示)的影响。研究结果认为,GAM模型中,经度对CPUE的影响最大,累计解释偏差超过50%,其次为纬度、年和月;在环境因子中,SSH最为重要,其次为ONI,而SST和Chl-a的影响相对较低。BRT模型分析结果与GAM分析结果类似,时空因子相对占据了重要的地位,其中经度的影响最大,其次为年、纬度和月;而在环境因子中,ONI的重要性相对更高,其次为SSH,SST和Chl-a同样影响较低。研究认为,两种模型均能较好地反映出因子对CPUE的影响。由于厄尔尼诺/拉尼娜现象引起的海洋环境变化会使鲣资源分布产生差异,因此在后续的渔情预报研究中,应该更多地考虑将ONI因子纳入渔情预报模型中,以提高预测精度。     

Impacts of changing spatial scales on CPUE–factor relationships of Ommastrephes bartramii in the northwest Pacific

The selection of spatial scales is of particular importance in modeling relationships between fishery abundance and its influencing factors, because these relationships are significantly affected by spatial scale. Here, we explore the spatial scale effects of catch per unit effort (CPUE)–factor relationships for Ommastrephes bartramii in the northwest Pacific. The original commercial fishery data and oceanographic factors were tessellated to 12 spatial scales from 5′ to 60′ with an interval of 5′. Under the original scale and 12 tessellated scales, we constructed the generalized additive models (GAMs) to model the relationships between the O. bartramii CPUE and the influencing factors, including Year, Month, Latitude (Lat), Longitude (Lon), sea surface salinity (SSS), sea surface temperature (SST), sea surface chlorophyll‐a (Chl‐a) concentration, and sea surface height (SSH). Our multi‐scale analysis showed that the relationships are sensitive to spatial scales. Among the factors, Year, Month, and SSS share quadratic polynomial scaling relations; Lat, SST, and Chl‐a illustrate power law scaling relations; Lon has a linear scaling relation; and SSH presents an exponential scaling relation. Considering the scale sensitivity of the factor sort‐order and the accumulation of explained residual deviance in GAM, we suggest 30′–45′ as the optimal range of spatial scales for analyzing the CPUE–factor relationships for O. bartramii. Our research improves understanding of the impacts of changing scales in fisheries and provides a potential method for the selection of a suitable spatial scale for fisheries analysis and resource surveying.     

Relationships between CPUE fluctuation of southern bluefin tuna and ocean temperature variability in the Central Indian Ocean

In this study, catch and effort data of southern bluefin tuna (SBT) from Taiwan longliners operating in the Central Indian Ocean (CIO) during 1982 to 2003 were compiled and their catch per unit effort (CPUE) was standardized using the generalized linear model (GLM). The GLM includes factors such as year, season, by-catch, latitude, sea surface temperature (SST) and the interactive effects among factors. The standardized CPUE and its relationship with SST fluctuation were then analyzed to understand the effects of fishing ground SST variations on CPUE of SBT, as well as their connection to El Niño-Southern Oscillation (ENSO) events. The standardized CPUE in the CIO seemed to oscillate with the sea surface temperature anomalies (SSTA) between 30 and 50°S where SSTA fluctuations were prolonged and slower than the ENSO cycle. It is then very likely that fishing conditions at the CIO fishing ground were influenced by the expansion of the cold water mass from the Southern Ocean, and the colder SST is beneficial to increasing SBT catch rate.     

Characterizing environmental and spatial variables associated with the incidental catch of olive ridley (Lepidochelys olivacea) in the Eastern Tropical Pacific purse‐seine fishery

In the Eastern Tropical Pacific (ETP), a region of high fishing activity, olive ridley (Lepidochelis olivacea) and other sea turtles are accidentally caught in fishing nets with tuna and other animals. To date, the interaction between fishing activity, ocean conditions and sea turtle incidental catch in the ETP has been described and quantified, but the factors leading to the interaction of olive ridleys and fishing activity are not well understood. This information is essential for the development of future management strategies that avoid bycatch and incidental captures of sea turtles. We used Generalized additive models (GAM) to analyze the relationship between olive ridley incidental catch per unit effort (iCPUE) in the ETP purse‐seine fisheries and environmental conditions, geographic extent and fishing set type (associated with dolphins, floating objects or in free‐swimming tuna schools). Our results suggest that water temperature, set type and geographic location (latitude, longitude and distance to nesting beaches) are the most important predictor variables to describe the probability of a capture event, with the highest iCPUE observed in sets made over floating objects. With the environmental predictors used, sea surface temperatures (SST) of 26–30°C and chlorophyll‐a (chl‐a) concentrations <0.36 mg m^?3 were associated with the highest probability of an incidental catch. Temporally, the highest probability of an incidental catch was observed in the second half of the year (June to December). Four regions were observed as high incidental catch hotspots: North and south of the equator between 0–10°N; 0–10°S and from 120 to 140°W; and along the Colombian coast and surrounding regions.     

Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data

The physical environment directly influences the distribution, abundance, physiology and phenology of marine species. Relating species presence to physical ocean characteristics to determine habitat associations is fundamental to the management of marine species. However, direct observation of highly mobile animals in the open ocean, such as tunas and billfish, is challenging and expensive. As a result, detailed data on habitat preferences using electronic tags have only been collected for the large iconic, valuable or endangered species. An alternative is to use commercial fishery catch data matched with historical ocean data to infer habitat associations. Using catch information from an Australian longline fishery and Bayesian hierarchical models, we investigate the influence of environmental variables on the catch distribution of yellowfin tuna (Thunnus albacares). The focus was to understand the relative importance of space, time and ocean conditions on the catch of this pelagic predator. We found that pelagic regions with elevated eddy kinetic energy, a shallow surface mixed layer and relatively high concentrations of chlorophyll a are all associated with high yellowfin tuna catch in the Tasman Sea. The time and space information incorporated in the analysis, while important, were less informative than oceanic variables in explaining catch. An inspection of model prediction errors identified clumping of errors at margins of ocean features, such as eddies and frontal features, which indicate that these models could be improved by including representations of dynamic ocean processes which affect the catch of yellowfin tuna.     

A spatial population dynamics simulation model of tropical tunas using a habitat index based on environmental parameters

We are developing a spatial, multigear, multispecies population dynamics simulation model for tropical tunas in the Pacific Ocean. The model is age-structured to account for growth and gear selectivity. It includes a tuna movement model based on a diffusion–advection equation in which the advective term is proportional to the gradient of a habitat index. The monthly geographical distribution of recruitment is defined by assuming that spawning occurs in areas where sea surface temperature is above 25°C. During the first 3 months of their life, simulated tunas are transported by oceanic currents, after which movement is conditioned by gradients in the habitat index. Independent estimates of natural mortality rates and population size from large-scale tagging experiments carried out by the Secretariat of the Pacific Community are used in the simulations. The habitat index consists of components due to forage density and sea surface temperature, both of which are suspected to play major roles in determining tuna distribution. Because direct observations of forage are not available on a basin scale, we developed a submodel to simulate the surface tuna forage production (Lehodey et al ., 1998). At present, only skipjack ( Katsuwonus pelamis ; a surface tuna species caught by purse seine and by pole-and-line) is considered, at a 1°-square resolution and on a monthly climatological time series. Despite the simplicity of the model and the limitations of the data used, the simulation model is able to predict a distribution of skipjack catch rates, of the different fleets involved in the fishery, that is fairly consistent with observations.     

The relationship between the skipjack tuna (Katsuwonus pelamis) fishery and seasonal temperature variability in the south-western Atlantic

The spatio‐temporal distribution of tuna fishing effort has been related to oceanographic circulation and features in several seas of the world. Understanding the relationship between environmental variables and fishery resource dynamics is important for management decisions and to improve fishery yields. The relationship between sea temperature variability and the pole‐and‐line skipjack tuna (Katsuwonus pelamis) fishery in the south‐western Atlantic Ocean was investigated in this work. Data from logbooks, satellite images (sea surface temperature), and oceanographic surveys were used in the analyses. Skipjack are caught in warm tropical waters of the Brazil Current (BC). The north–south displacement of fishing effort was strongly associated to seasonal variation of the surface temperature, which was coupled to the tropical BC flow. Oceanographic fronts from autumn to spring and a shallow thermocline in summer probably induces the aggregation of skipjack schools over the shelfbreak, favouring fishing operations. Hypotheses are proposed to explain the relationship between peaks of fishing events and the presence of topographic peculiarities of the shelfbreak.     

Skipjack tuna fishery in relation to sea surface temperature off the southern Brazilian coast

Several oceanographic studies have associated tuna fisheries to sea surface temperature (SST) fields, although catch per unit of effort (CPUE) has not shown a clear relationship with SST. However, most results concerned species that occur deep in the water column. In this paper, we present a study on the relationship between SST and CPUE for the skipjack tuna fisheries off the southern Brazilian coast, which take place at the sea surface. We use historical data from the Japanese fleet, which operated in the area from July 1982 to June 1992. Fishing sets occurred only in areas where SST ranged from 17°C to 30°C. Frequency of occurrence vs. SST showed a Gaussian distribution, with highest CPUEs in waters of SST 22°-26.5°C. The relationship between CPUE (or fishing set occurrence) and SST varied seasonally. Largest CPUEs occurred in summer, independently of SST. Therefore, temperature alone could not be used as a determinant of CPUE, suggesting that seasonal variability of other environmental parameters has a stronger effect on the CPUE than does SST. However, when the seasonal cycle was excluded from the data sets, a relationship between the interannual variability of SST and CPUE became apparent. Cross-correlation analysis between CPUE and SST has shown that oscillations in CPUE anomalies precede oscillations in SST anomalies by a month, but the mechanism relating them in this way is unknown.     

Application of a habitat-based model to estimate effective longline fishing effort and relative abundance of Pacific bigeye tuna (Thunnus obesus)

A new habitat‐based model is developed to improve estimates of relative abundance of Pacific bigeye tuna (Thunnus obesus). The model provides estimates of `effective' longline effort and therefore better estimates of catch‐per‐unit‐of‐effort (CPUE) by incorporating information on the variation in longline fishing depth and depth of bigeye tuna preferred habitat. The essential elements in the model are: (1) estimation of the depth distribution of the longline gear, using information on gear configuration and ocean currents; (2) estimation of the depth distribution of bigeye tuna, based on habitat preference and oceanographic data; (3) estimation of effective longline effort, using fine‐scale Japanese longline fishery data; and (4) aggregation of catch and effective effort over appropriate spatial zones to produce revised time series of CPUE. Model results indicate that effective effort has increased in both the western and central Pacific Ocean (WCPO) and eastern Pacific Ocean (EPO). In the WCPO, effective effort increased by 43% from the late 1960s to the late 1980s due primarily to the increased effectiveness of effort (deeper longline sets) rather than to increased nominal effort. Over the same period, effective effort increased 250% in the EPO due primarily to increased nominal effort. Nominal and standardized CPUE indices in the EPO show similar trends – a decline during the 1960s, a period of stability in the 1970s, high values during 1985–1986 and a decline thereafter. In the WCPO, nominal CPUE is stable over the time‐series; however, standardized CPUE has declined by ～50%. If estimates of standardized CPUE accurately reflect relative abundance, then we have documented substantial reductions of bigeye tuna abundance for some regions in the Pacific Ocean. A decline in standardized CPUE in the subtropical gyres concurrent with stability in equatorial areas may represent a contraction in the range of the population resulting from a decline in population abundance. The sensitivity of the results to the habitat (temperature and oxygen) assumptions was tested using Monte Carlo simulations.     

Tuna and swordfish catch in the U.S. northwest Atlantic longline fishery in relation to mesoscale eddies

To analyze the effects of mesoscale eddies, sea surface temperature (SST), and gear configuration on the catch of Atlantic bluefin (Thunnus thynnus), yellowfin (Thunnus albacares), and bigeye tuna (Thunnus obesus) and swordfish (Xiphias gladius) in the U.S. northwest Atlantic longline fishery, we constructed multivariate statistical models relating these variables to the catch of the four species in 62 121 longline hauls made between 1993 and 2005. During the same 13‐year period, 103 anticyclonic eddies and 269 cyclonic eddies were detected by our algorithm in the region 30–55°N, 30–80°W. Our results show that tuna and swordfish catches were associated with different eddy structures. Bluefin tuna catch was highest in anticyclonic eddies whereas yellowfin and bigeye tuna catches were highest in cyclonic eddies. Swordfish catch was found preferentially in regions outside of eddies. Our study confirms that the common practice of targeting tuna with day sets and swordfish with night sets is effective. In addition, bluefin tuna and swordfish catches responded to most of the variables we tested in the opposite directions. Bluefin tuna catch was negatively correlated with longitude and the number of light sticks used whereas swordfish catch was positively correlated with these two variables. We argue that overfishing of bluefin tuna can be alleviated and that swordfish can be targeted more efficiently by avoiding fishing in anticyclonic eddies and in near‐shore waters and using more light sticks and fishing at night in our study area, although further studies are needed to propose a solid oceanography‐based management plan for catch selection.     

Using historical fisheries data to predict tuna distribution within the British Indian Ocean Territory Marine Protected Area,and implications for its management

相似文献   

Predicting skipjack tuna forage distributions in the equatorial Pacific using a coupled dynamical bio-geochemical model

Skipjack tuna ( Katsuwonus pelamis ) contributes ≈70% of the total tuna catch in the Pacific Ocean. This species occurs in the upper mixed-layer throughout the equatorial region, but the largest catches are taken from the warmpool in the western equatorial Pacific. Analysis of catch and effort data for US purse seine fisheries in the western Pacific has demonstrated that one of the most successful fishing grounds is located in the vicinity of a convergence zone between the warm (>28–29°C) low-salinity water of the warmpool and the cold saline water of equatorial upwelling in the central Pacific (Lehodey et al ., 1997). This zone of convergence, identified by a well-marked salinity front and approximated by the 28.5°C isotherm, oscillates zonally over several thousands of km in correlation with the El Niño–Southern Oscillation. The present study focuses on the prediction of skipjack tuna forage that is expected to be a major factor in explaining the basin-scale distribution of the stock. It could also explain the close relation between displacements of skipjack tuna and the convergence zone on the eastern edge of the warmpool. A simple bio-geochemical model was coupled with a general circulation model, allowing reasonable predictions of new primary production in the equatorial Pacific from mid-1992 to mid-1995. The biological transfer of this production toward tuna forage was simply parameterized according to the food chain length and redistributed by the currents using the circulation model. Tuna forage accumulated in the convergence zone of the horizontal currents, which corresponds to the warmpool/equatorial upwelling boundary. Predicted forage maxima corresponded well with high catch rates.     

A study on the variability of albacore (Thunnus alalunga) longline catch rates in the southwest Pacific Ocean

Relationships between albacore tuna (Thunnus alalunga) longline catch per unit effort (CPUE) and environmental variables from model outputs in New Caledonia’s Exclusive Economic Zone (EEZ) were examined through generalized linear models at a 1° spatial resolution and 10‐day temporal resolution. At a regional (EEZ) scale, the study demonstrated that a large part of albacore CPUE variability can be explained by seasonal, interannual and spatial variation of the habitat. Results of the generalized linear models indicated that catch rates are higher than average in the northwestern part of the EEZ at the beginning of the year (January) and during the second half of the year (July–December). In the northwestern region of the EEZ, high CPUEs are associated with waters <20.5° in the intermediate layer and with moderate values of primary production. Longline CPUE also appeared to be dependent on prey densities, as predicted from a micronekton model. Albacore CPUE was highest at moderate densities of prey in the epipelagic layer during the night and for relatively low prey densities in the mesopelagic layer during the day. We also demonstrated that the highest CPUEs were recorded from 1986 to 1998, which corresponds to a period with frequent El Niño events.     

1999—2011年东、黄海鲐资源丰度年间变化分析

根据1999—2011年我国鲐大型灯光围网渔业数据,使用广义线性模型(generalized linear model,GLM)和广义加性模型(generalized additive model,GAM)估算了影响CPUE的时间(年、月)、空间(经度、纬度)、捕捞性能和环境效应[海表面温度(sea surface temperature,SST)、海表面高度、海表面叶绿素浓度],并以年效应作为资源丰度指数,分析了东、黄海鲐资源丰度的年间变化,东、黄海鲐资源丰度指数的年间变化与产卵场海表面温度以及捕捞强度间的关系。GAM结果表明,时间、空间、捕捞和环境变量对CPUE偏差的解释率为11.69%,其中变量年的解释率最大,占总解释率的38%。结果显示,1999—2011年东、黄海鲐鱼资源丰度指数(abundance index,AI)总体上呈下降趋势,2008年以来更是持续下降,丰度指数由2008年的1.22降至2011年的0.82。东、黄海鲐资源丰度指数年间与产卵场呈正相关,关系式为AI=-3.51+0.23SST(P0.05),这表明较高的产卵场SST对鲐资源量增加有利。过高的渔获量以及我国群众围网渔业渔船数量的快速增长是导致近年来鲐鱼资源下降的重要原因。