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.     

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.     

Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective

Skipjack tuna habitat in the western North Pacific was studied from satellite remotely sensed environment and catch data, using generalized additive models and geographic information systems. Weekly resolved remotely sensed sea surface temperature, surface chlorophyll, sea surface height anomalies and eddy kinetic energy data were used for the year 2004. Fifteen generalized additive models were constructed with skipjack catch per unit effort as a response variable, and sea surface temperature, sea surface height anomalies and eddy kinetic energy as model covariates to assess the effect of environment on catch per unit effort (skipjack tuna abundance). Model selection was based on significance of model terms, reduction in Akaike’s Information Criterion, and increase in cumulative deviance explained. The model selected was used to predict skipjack tuna catch per unit effort using monthly resolved environmental data for assessing model performance and to visualize the basin scale distribution of skipjack tuna habitat. Predicted values were validated using a linear model. Based on the four‐parameter model, skipjack tuna habitat selection was significantly (P < 0.01) influenced by sea surface temperatures ranging from 20.5 to 26°C, relatively oligotrophic waters (surface chlorophyll 0.08–0.18, 0.22–0.27 and 0.3–0.37 mg m^?3), zero to positive anomalies (surface height anomalies 0–50 cm), and low to moderate eddy kinetic energy (0–200 and 700–2500 cm² s^–2). Predicted catch per unit effort showed a trend consistent with the north–south migration of skipjack tuna. Validation of predicted catch per unit effort with that observed, pooled monthly, was significant (P < 0.01, r² = 0.64). Sea surface temperature explained the highest deviance in generalized additive models and was therefore considered the best habitat predictor.     

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。     

Distribution of albacore (Thunnus alalunga) in the Indian Ocean and its relation to environmental factors

The distribution pattern of albacore, Thunnus alalunga, in the Indian Ocean was analyzed based on catch data from the Taiwanese tuna longline fishery during the period 1979–85. The Taiwanese tuna fishery began operating in the Indian Ocean in 1967. We used a geographic information system to compile a fishery and environmental database and statistically explored the catch per unit effort (CPUE) distribution of albacore. Our results indicated that immature albacore were mainly distributed in areas south of 30°S although some displayed a north–south seasonal migration. Mature albacore, which were mainly concentrated between 10°S and 25°S, also showed a north–south migration. Within 10°S and 30°S, the separation of mature, spawning, and immature albacore life history stages roughly coincided with the boundaries of the three oceanic current systems in the Indian Ocean. The optimal environmental variables for CPUE prediction by stepwise discriminant analysis differed among life history stages. For immature albacore, the sea surface variables sea surface temperature (SST), chlorophyll concentration and surface salinity were significant. For mature albacore, SST was significant, while for spawning albacore, the sub‐surface variables temperature at 100 m and oxygen at 200 m were significant. Spawning albacore evidently prefer deep oceanographic conditions. Our results on the oceanographic conditions preferred by different developmental stages of albacore in the Indian Ocean were compatible with previous studies found in the Pacific Ocean.     

Near real-time spatial management based on habitat predictions for a longline bycatch species

Abstract Southern bluefin tuna (SBT), Thunnus maccoyii (Castelnau), is a quota‐managed species that makes annual winter migrations to the Tasman Sea off south‐eastern Australia. During this period it interacts with a year‐round tropical tuna longline fishery (Eastern Tuna and Billfish Fishery, ETBF). ETBF managers seek to minimise the bycatch of SBT by commercial ETBF longline fishers with limited or no SBT quota through spatial restrictions. Access to areas where SBT are believed to be present is restricted to fishers holding SBT quota. A temperature‐based SBT habitat model was developed to provide managers with an estimate of tuna distribution upon which to base their decisions about placement of management boundaries. Adult SBT temperature preferences were determined using pop‐up satellite archival tags. The near real‐time predicted location of SBT was determined by matching temperature preferences to satellite sea surface temperature data and vertical temperature data from an oceanographic model. Regular reports detailing the location of temperature‐based SBT habitat were produced during the period of the ETBF fishing season when interactions with SBT occur. The SBT habitat model included: (i) predictions based on the current vertical structure of the ocean; (ii) seasonally adjusted temperature preference data for the 60 calendar days centred on the prediction date; and (iii) development of a temperature‐based SBT habitat climatology that allowed visualisation of the expected change in the distribution of the SBT habitat zones throughout the season. At the conclusion of the fishing season an automated method for placing management boundaries was compared with the subjective approach used by managers. Applying this automated procedure to the habitat predictions enabled an investigation of the effects of setting management boundaries using old data and updating management boundaries infrequently. Direct comparison with the management boundaries allowed an evaluation of the efficiency and biases produced by this aspect of the fishery management process. Near real‐time fishery management continues to be a realistic prospect that new scientific approaches using novel tools can support and advance.     

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.     

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.     

海洋环境对中西太平洋金枪鱼围网渔场影响的GIS时空分析

根据2008年～2012年中西太平洋金枪鱼（Thynnus）围网的渔获生产数据,并结合利用遥感信息技术手段同期获取的海表温度、次表层和温跃层温度、叶绿素等海洋环境数据,分析了围网主要捕获品种渔获量、资源丰度与渔场重心的时空变化及其与主要环境因子之间的关系。结果显示,目前中西太平洋金枪鱼围网渔获量分布在10°N～10°S、140°E-180°E,中心渔场经度重心集中在150°E～165°E,大体走向是由西向东;纬度重心在1°N～3°S,呈现先南后北的走向。渔场主要适温在28～32℃,最适海表温度为29～31℃,次表层50m,适温为26．84～29．47℃,100m适温为24．71～28．57℃,温跃层上界深度在54．09～121．49m,对应的海水温度为27．10—29．18℃;主要渔获产量集中在叶绿素质量浓度0．02～0．35mg·m-3内,叶绿素质量浓度处于0．04—0．18mg·m-3时渔获产量出现频次最高,为渔场的最适叶绿素质量浓度范围。     

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.     

Enhancing the TurtleWatch product for leatherback sea turtles,a dynamic habitat model for ecosystem‐based management

Fishery management measures to reduce interactions between fisheries and endangered or threatened species have typically relied on static time‐area closures. While these efforts have reduced interactions, they can be costly and inefficient for managing highly migratory species such as sea turtles. The NOAA TurtleWatch product was created in 2006 as a tool to reduce the rates of interactions of loggerhead sea turtles with shallow‐set longline gear deployed by the Hawaii‐based pelagic longline fishery targeting swordfish. TurtleWatch provides information on loggerhead habitat and can be used by managers and industry to make dynamic management decisions to potentially reduce incidentally capturing turtles during fishing operations. TurtleWatch is expanded here to include information on endangered leatherback turtles to help reduce incidental capture rates in the central North Pacific. Fishery‐dependent data were combined with fishing effort, bycatch and satellite tracking data of leatherbacks to characterize sea surface temperature (SST) relationships that identify habitat or interaction ‘hotspots’. Analysis of SST identified two zones, centered at 17.2° and 22.9°C, occupied by leatherbacks on fishing grounds of the Hawaii‐based swordfish fishery. This new information was used to expand the TurtleWatch product to provide managers and industry near real‐time habitat information for both loggerheads and leatherbacks. The updated TurtleWatch product provides a tool for dynamic management of the Hawaii‐based shallow‐set fishery to aid in the bycatch reduction of both species. Updating the management strategy to dynamically adapt to shifts in multi‐species habitat use through time is a step towards an ecosystem‐based approach to fisheries management in pelagic ecosystems.     

中西太平洋金枪鱼围网鲣鱼渔获量时空分布分析

中西太平洋的金枪鱼围网渔业目前的年产量约在100×104t左右,其中鲣鱼占有很重要的地位。本文通过对20世纪70年代以来围网捕获的鲣鱼渔获数据进行时间序列以及空间位置变化等时空分析,试图找出其变化规律以及趋势。结果表明,从20世纪70年代以来,随着渔船数的增加,中西太平洋的围网捕获的鲣鱼渔获量分布,从太平洋岛屿近海逐渐向太平洋热带中部海域扩展。渔获量经度重心随着中西太平洋金枪鱼围网渔业的发展有向东移动的趋势,70年代在128°E附近变化,80年代在144°E左右,90年代在153°E左右,近年在158°E左右变化。而鲣鱼渔获量纬度重心位于赤道区域,70年代在2°N附近,80年代在1°30′S左右,90年代在2°50′S左右,近年在2°55′S左右变化。经纬度5°×5°单个小区范围内10年内的最高总产量则从70年代的11×104t,增加到90年代超过了69×104t。渔获量空间分布除了随着渔业发展向外海向赤道以南扩展以外,还受南方涛动(ENSO)现象的明显影响,一般来说在相邻的数年中渔获量经度中心在厄尔尼诺年比较偏东,在拉尼娜年比较偏西。     

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.     

利用水温垂直结构研究中西太平洋鲣鱼栖息地指数

根据1990~2001年中西太平洋海域(20°N~25°S、175°W以西)金枪鱼围网鲣鱼作业产量和作业次数,结合不同水层的水温及其温差数据(海表温度SST,12.5 m、237.5 m和287.5 m温度,137.5 m与287.5 m温差),以高产频次的相对比值分别建立各因素的栖息地指数SI,建立单因素一元非线形回归模型。采用连乘法、最小值法、最大值法、算术平均法和几何平均法建立综合栖息地指数HSI,并对1990~2001年各月HSI值与实际作业产量进行验证。结果表明,采用连乘法和最小值法时,主要产量分布在HSI<0.5以下的区域;采用算术平均法和几何平均法时,主要产量分布在0.30.7的区域,其产量占总产量的87%。五种模型结果比较,认为最大值法能更好地反映中心渔场分布和符合鲣鱼的分布特征。采用最大值法推算2003年各月HSI值,并与实际产量分布进行实证分析,发现其各月产量主要分布在HSI>0.8的区域,说明利用HSI模型来预测中心渔场是可行的。     

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。     

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.     

南海及临近海域黄鳍金枪鱼渔场时空分布与海表温度的关系

为得到南海及临近海域黄鳍金枪鱼(Thunnus albacores)渔场最适宜栖息海表温度(SST)范围,基于美国国家海洋大气局(NOAA)气候预测中心月平均海表温度(SST)资料,结合中西太平洋渔业委员会(WCPFC)发布的南海及临近海域金枪鱼延绳钓渔业数据,绘制了月平均SST和月平均单位捕捞努力量渔获量(CPUE)的空间叠加图,用于分析南海及临近海域黄鳍金枪鱼渔场CPUE时空分布和SST的关系。结果表明,南海及临近海域黄鳍金枪鱼CPUE在16℃~31℃均有分布。在春季和夏季(3~8月),位于10°~20°N的大部分渔区CPUE较高,其南北侧CPUE较低;而到了秋季和冬季(9月到次年2月),高产渔场区域会向南拓宽。CPUE在各SST区间的散点图呈现出明显的负偏态分布,高CPUE主要集中在26℃~30℃,最高值出现在29℃附近;在22℃~26℃范围内CPUE散点分布较为零散,但在这个范围也会出现相当数量的高CPUE;在22℃以下的CPUE几乎属于低CPUE和零CPUE;零CPUE的平均SST为26.7℃(±3.2℃),低CPUE的平均SST为27.8℃(±2.1℃),高CPUE的平均SST为28.4℃(±1.5℃),高CPUE在各SST区间的分布要比零CPUE和低CPUE更为集中。采用频次分析和经验累积分布函数计算其最适SST范围,得到南海及临近海域黄鳍金枪鱼最适SST为26.9℃~29.4℃。本研究初步得到南海及临近海域黄鳍金枪鱼中心渔场时空分布特征及SST适宜分布区间,可为开展南海及临近海域金枪鱼渔情预报工作提供理论依据和参考。     

越南开发南海金枪鱼资源的历史和趋势

近十年来,越南将南海的金枪鱼资源作为其＂外向型＂渔业的重要支撑,不断增加捕捞强度,产量逐年升高。本文总结了越南发展南海金枪鱼渔业的过程,分析了南海金枪鱼资源的开发趋势。越南现代化的金枪鱼捕捞技术主要来自日本,使用的渔具主要有金枪鱼延绳钓、手钓、刺网和小型围网,捕捞的种类主要为鲣鱼、黄鳍金枪鱼和大眼金枪鱼,主要作业区域在西沙群岛南部海域和南沙群岛海域。越南2009年金枪鱼的产量已达到5.9×104t,计划2015年达到30×104t。根据越南海洋渔业研究所（RIMF）的评估,南海中西部的金枪鱼资源量为66~67×104t,可捕量23.3×104t,其中鲣鱼的可捕量21.6×104t,黄鳍金枪鱼和大眼金枪鱼的可捕量1.7×104t。随着全球金枪鱼捕捞配额的缩减和越南＂外向型＂渔业经济的发展,越南将继续加强对南海金枪鱼资源的开发。     

基于贝叶斯原理的大洋金枪鱼渔场速预报模型研究

高度洄游的大洋性金枪鱼类（Scombridae）是世界远洋渔业的重要捕捞对象,中国金枪鱼生产仍处于初期发展阶段,因此研究和预测金枪鱼渔场具有重要的现实意义.本研究利用美国NASA提供的卫星遥感反演海表温度（SST）三级数据产品和太平洋共同体秘书处（SPC）提供的有关国际金枪鱼历史捕捞产量资料,分析金枪鱼同SST等海洋渔场环境要素之间的统计关系,建立了金枪鱼渔场的贝叶斯概率预报模型.通过对历史数据进行模型回报试验,结果表明太平洋鲣鱼渔场综合预报的准确性达到70%以上,对渔业捕捞生产具有一定的指导意义.[中国水产科学,2006,13（3）：426-431]     

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.