Do habitat models accurately predict the depth distribution of pelagic fishes?

Habitat models are used to correct estimates of fish abundance derived from pelagic longline fishing gear. They combine information on hook depth with the species’ preferences for ambient environmental conditions to adjust the gear's catchability. We compare depth distributions of bigeye tuna (Thunnus obesus) catch predicted by a habitat model with distributions derived from data collected by observers on longliners in the tropical Pacific Ocean. Our analyses show that the habitat model does not accurately predict the depth distribution of bigeye tuna; its predictions are worse than those from models that assume no effect of depth on catches. Statistical models provided superior fits to the observed depth distribution. The poor performance of the habitat model is probably due to (1) problems in estimating hook depth, (2) fine‐scale variations in environmental conditions, (3) incomplete knowledge of habitat preferences and (4) differences between the distribution of bigeye tuna and their vulnerability to longline gear.     

Incidental catch of marine turtles by Italian trawlers and longliners in the central Mediterranean

• 1. The fishing effort and turtle catch of vessels harbouring at Lampedusa island and fishing in the wider central Mediterranean area was monitored using a voluntary logbook programme. Two large trawlers were monitored between 2003 and 2005 and six small vessels using trawl nets, pelagic longline or bottom longline were monitored in the summer 2005.
• 2. The observed turtle catch rates of pelagic longline and bottom trawl were among the highest recorded in the basin, and high catch rates by bottom longline were observed too. This suggests that the area contains major oceanic and neritic habitats for the loggerhead turtle Caretta caretta in the Mediterranean Sea.
• 3. When fishing effort is considered, these results suggest a very high number of captures by Italian trawlers and longliners in the area, as well as by fleets from other countries. This is reason of concern for the conservation of the loggerhead turtle within the Mediterranean Sea.
• 4. Different fishing gear have different technical/operational characteristics affecting turtle catch and mortality and the present knowledge about associated parameters of these gear varies too.
• 5. All this considered, specific actions are recommended: (i) an awareness campaign to fishermen to reduce post‐release mortality, (ii) technical modifications to pelagic longline gear to reduce turtle catch, (iii) further investigation into turtle bycatch in all fishing gear, with priority given to bottom longline fishing and quantification of mortality caused by trawlers, (iv) assessment of the turtle populations affected by fishing activity in the area, and (v) international cooperation in undertaking threat assessments, and implementing regulations, management measures and monitoring.

Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

金枪鱼延绳钓渔获性能研究进展

金枪鱼延绳钓渔获性能主要按目标鱼种和兼捕物种渔获效率进行评价。对其研究有助于改进延绳钓渔具渔法, 提高目标鱼种捕捞效率和减少兼捕。本文以时间顺序为主对国内外关于金枪鱼延绳钓渔获性能研究的文献进行梳理, 从钓具选择性、钓钩深度、饵料选择性、环境因素以及钓具浸泡时长等方面概括了金枪鱼延绳钓渔获性能的研究进展, 并提出存在的不足和建议, 为金枪鱼延绳钓渔获性能的研究提供参考。前人研究取得的成果有: (1)不同鱼种最佳作业深度和钓具浸泡时长不同; (2)较大尺寸的圆形钩能减少兼捕; (3)拟饵也具有选择性, 鱼类饵料和蓝色染色饵料有利于减少兼捕; (4)具体水层的环境因素对延绳钓渔获性能影响较大。建议今后金枪鱼延绳钓渔获性能研究应: (1)确定钓钩最佳沉降速度和深度; (2)分水层建立不同物种渔获性能预测模型; (3)针对不同的目标鱼种探索最佳尺寸和钩形; (4)研究不同气味和颜色的饵料或拟饵对物种选择性的影响; (5)考虑诱饵、钓钩类型和尺寸和钓具浸泡时长对渔获率、死亡率、兼捕率和兼捕物种释放后存活率的潜在协同效应。     

Meta‐analysis of post‐release fishing mortality in apex predatory pelagic sharks and white marlin

Robust assessments of the effects of fishing require accounting for components of fishing mortality, including post‐release fishing mortality (F_r). Random‐effects meta‐analysis synthesized F_r in seven pelagic shark species captured, tagged and released with 401 pop‐up satellite archival tags compiled from 33 studies and three gears (longline, purse‐seine, rod & reel). The majority of F_r outcomes occurred within days of release, and the summary effect size for F_r was 0.27 [95% CI: 0.19–0.36], ranging from a low pooled effect size of 0.17 for blue shark (Prionace glauca, Carcharhinidae) to 0.38 (silky shark, Carcharhinus falciformis, Carcharhinidae). F_r rates in blue shark were consistent over dissimilar spatial and temporal scales, and results from earlier meta‐analysis were replicated, which is the most powerful way to authenticate results. Condition at tagging was a strong predictor, and dichotomized survival outcomes in silky shark and no sex‐, size‐, location‐ or gear‐specific F_r rates were demonstrated. Meta‐analyses and sensitivity analyses indicated exposure to risk factors and conditions whilst caught on the gear probably had the largest explanatory effect on F_r, rather than stressors incurred during handling and release. Records from 549 tagged istiophorid billfishes (six species, three gears, 43 studies) demonstrated they are more robust to stressors sustained during capture, handling and release than pelagic sharks. Findings from previous meta‐analysis on F_r rates in white marlin (Kajikia albida, Istiophoridae) were replicated. Synthesized F_r rates enable prioritizing approaches to mitigate by‐catch fishing mortality, to improve the quality of stock and ecological risk assessments and to expand our knowledge of factors influencing trophic structure.     

中东太平洋金枪鱼延绳钓渔获物组成分析

根据2000年9月至2002年8月两年的中东太平洋金枪鱼延绳钓探捕调查结果，对延绳钓渔获物组成进行了初步分析。结果显示，延绳钓的主要渔获种类有肥壮金枪鱼(Thunnus obesus)和黄鳍金怆鱼(Thunnus albacares)等15种大洋性鱼类，渔获物中金枪鱼类分别占重量和尾数的76．41％和76．91％，旗鱼类占11．05％和7．83％．鲨鱼类占10．80％和12．08％，其他鱼类占1．73％和3．18％。相对重要性指标(IRI)表明，延绳钓渔业以肥壮金枪鱼和黄鳍金枪鱼为目标鱼种，其他大型中上层经济鱼类为兼捕对象。各渔获种类的渔获重量组成比例月间变化和海域变化明显。     

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.     

A comparison of methods to spatially represent pelagic longline fishing effort in catch and bycatch studies

Bycatch in fisheries has been recognized as a threat to many endangered populations of sea turtles, sea birds and marine mammals. Interactions between pelagic longline fisheries and critically endangered populations of leatherback sea turtles (Dermochelys coriacea) have led to temporary closures of the Hawaiian pelagic longline swordfish fishery and severe bycatch quotas. The negative impact of these events on both the populations of certain endangered species and the economic livelihood of the fishermen has resulted in a strong push from all sides to better understand bycatch events. Typically, analyses of longline catch and bycatch have examined fishing effort summarized over large areas (≥1°). Although aggregation of effort to this level may be necessary to account for uncertainty, confidentiality concerns, or to make comparisons across regions, it specifically limits the researcher's ability to characterize the local oceanographic factors that may drive individual bycatch events. Higher resolution analyses must be undertaken to identify such features. However, for these higher resolution analyses, the methods currently used to spatially represent pelagic longline fishing effort may significantly affect researcher's results. Here, we look at different methods to represent this fishing effort (i.e., points, centroids, polylines and polygons) at various resolutions (2 km to 5°) to better understand which method and spatial resolution are most appropriate. Our results validate the use of point features to represent fishing effort in previous low resolution studies of the Hawaiian pelagic longline fishery by showing that the set point method is suitable for studies with resolutions lower than 15 km. However, at higher resolutions (≤15 km) and in areas with more sparsely distributed fishing, aggregated effort values differed significantly between spatial representation methods. We demonstrate that the use of polygons to describe pelagic longline fishing effort is more representative and necessary for such high resolution analyses.     

Three-dimensional underwater shape measurement of tuna longline using ultrasonic positioning system and ORBCOMM buoy

ABSTRACT:   The installation depth of the hook in longline fishing gear has previously been measured with micro depth loggers. Research that assumes the catenary shape of longline fishing gear by a simulation based on these data has been done. However, it was not known whether the branch line was tangled with the main line or flowed with the current. In this research, an ultrasonic positioning system generally used to investigate the underwater behavior of marine organisms, and a buoy with a communications satellite, have been used, and the 3-dimensional underwater shape of tuna longline fishing gear was measured. It was possible to monitor changes in fishing gear in real time. The possibility of high precision measurement was suggested with future technical improvement.     

Incidental capture of leatherback sea turtles in fixed fishing gear off Atlantic Canada

1. Incidental capture in commercial fishing gear is a threat to many populations of marine megafauna, including sea turtles. While research has largely focused on pelagic longline impacts on sea turtles, fixed‐gear fisheries are a significant, historically understudied source of injury and mortality.
2. The present study assesses the interaction of endangered leatherback sea turtles (Dermochelys coriacea) with fixed‐gear fisheries in high‐latitude seasonal foraging habitat where sub‐adult and adult turtles aggregate.
3. Records of leatherback‐fishery interactions (n = 205) were compiled from databases of publicly‐reported sea turtle sightings in Atlantic Canada (1998–2014) to identify the spatio‐temporal distribution of these events; to identify corresponding fisheries and gear types; and to describe the mechanics and outcomes of entanglements in fixed gear.
4. Most reports came from coastal Nova Scotia (n = 136) and Newfoundland (n = 40), with reporting rates peaking in the mid‐to‐late 2000s. The majority of entanglements were reported during the summer months of July and August when leatherbacks are seasonally resident and several fisheries are active in continental shelf waters.
5. Entanglements were most commonly reported in pot gear (e.g. snow crab, lobster, whelk) and trap nets (e.g. mackerel), reflecting extensive use of polypropylene lines distributed in the upper water column where leatherback foraging activity is concentrated.
6. Given reporting biases and uncertainty regarding post‐release survivorship, entanglement mortalities should be considered a gross underestimate of true mortality rates.
7. This study highlights both the importance of looking beyond pelagic longlines to evaluate leatherback interactions with fixed‐gear fisheries in high‐use continental shelf foraging habitat, and of involving the fishing industry in developing mitigation measures to reduce entanglement rates and associated turtle mortality.

     

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.     

Post‐release mortality estimates of loggerhead sea turtles (Caretta caretta) caught in pelagic longline fisheries based on satellite data and hooking location

相似文献   

Temperature and depth associations of porbeagle shark (Lamna nasus) in the northwest Atlantic

The porbeagle (Lamna nasus) is a large fast‐swimming pelagic shark found at high latitudes in both hemispheres. To examine the influence of temperature on porbeagle distribution, a detailed analysis of the relationship between catch rate, temperature, depth and location was carried out based on 420 temperature profiles taken during commercial fishing operations. More than half of the porbeagle were caught at temperatures of 5–10°C (at the depth of the hook); the mean temperature at gear of 7.4°C differed very little among seasons. Most of the spring fishing took place near fronts, although the affinity with fronts was not evident in the fall. Temperature at depth was a significant modifier of catch rate when included in a generalized linear model controlling for the effects of location, fishing vessel, month and year. However, sea surface temperature was a poor predictor of catch rate. The similarity between environmental and catch‐weighted cumulative distribution functions confirmed suggestions that fishers sought out the most appropriate temperature range in which to set their gear. As porbeagle are among the most cold tolerant of pelagic shark species, we suggest that they have evolved to take advantage of their thermoregulating capability by allowing them to seek out and feed on abundant coldwater prey in the absence of non‐thermoregulating competitors.     

Sea turtle by‐catch in the Mediterranean

Sea turtle by‐catch data in the Mediterranean were reviewed and analysed with fishing effort. The results indicate over 132 000 captures per year, with probably over 44 000 incidental deaths per year, while many others are killed intentionally. Small vessels using set net, demersal longline or pelagic longline represent most of the Mediterranean fleet and likely cause more incidental or intentional deaths than large vessels typically using bottom trawl or pelagic longline. When interactions, mortality, intentional killing, size (a proxy for reproductive value) and turtle populations are considered, results indicate that Mediterranean green (Chelonia mydas) and loggerhead turtles (Caretta caretta) are more affected (i) by fishing gears such as bottom trawlers, demersal longlines and set nets, (ii) by small‐scale fisheries, and (iii) by fishing in the eastern basin. Although small‐scale fisheries should be the priority target, available measures are easier to implement on the fewer large vessels. Moreover, these measures are few, and they are not implemented yet, while others should still be tested for the Mediterranean fisheries. Thus, measures for reducing captures or mortality through changing gear‐specific characteristics may help, but probably a more holistic conservation strategy aimed to an ecosystem‐based fishery management for a sustainable fishing would be the only solution for the long‐term survival of Mediterranean Sea turtle populations and their habitats. Small‐scale fisheries should manage marine resources, including turtles, in a responsible and sustainable way. Turtles may not only benefit from but can also help this process if their non‐consumptive value is fully recognized.     

基于GAM模型分析水温垂直结构对热带大西洋大眼金枪鱼渔获率的影响

通过模型分析环境变量对延绳钓大眼金枪鱼渔获率的影响,评估适宜垂直活动空间对大西洋大眼金枪鱼延绳钓渔获率的作用。首先采用回归分析检验环境变量对延绳钓渔获率(由单位捕捞努力渔获量(catch per unit fishing effort,CPUE)表示)的影响显著性,结合时空变量,采用GAM(generalized additive model)模型分析各变量对大眼金枪鱼CPUE非线性作用。模型结果表明,环境因子和时空变量对热带大西洋延绳钓大眼金枪鱼渔获率空间分布影响明显。大西洋大眼金枪鱼延绳钓的高渔获率月份出现在夏季和冬季,空间上在赤道以北和30?~50?W。12℃等温线深度对大眼金枪鱼延绳钓渔获率的影响表现为抛物线形状,高渔获率出现在深度较浅的250 m水层,随着12℃等温线深度的增加,大眼金枪鱼延绳钓渔获率降低。温跃层下界深度和深度差对大眼金枪鱼延绳钓渔获率的影响都是穹顶状。随着温跃层下界深度值和深度差由小变大至200 m,延绳钓渔获率递增;温跃层下界深度和深度差超过200 m后,延绳钓渔获率变小。温跃层下界深度和深度差对大眼金枪鱼延绳钓渔获率影响显著的水层分别是200 m和50 m。研究结果显示,12℃等温线深度和温跃层对热带大西洋延绳钓大眼金枪鱼渔获率影响是交叉的,在大眼金枪鱼适宜垂直活动水层受限到和延绳钓作业深度相同时,延绳钓渔获率最高;在适宜垂直活动空间过深或者过浅时,延绳钓渔获率都变小,但可以通过改变作业方式提高渔获率。采用延绳钓CPUE进行渔场和资源评估要考虑金枪鱼适宜垂直活动空间。     

Habitat utilization and vertical movements of white marlin (Tetrapturus albidus) released from commercial and recreational fishing gears in the western North Atlantic Ocean: inferences from short duration pop-up archival satellite tags

Researchers have applied numerous techniques to improve billfish stock assessments, including habitat‐based models that incorporate behavioral and oceanographic parameters to standardize historical catch‐per‐unit‐effort time‐series data. These methods have allowed researchers to account for significant changes in the depths of pelagic longline (PLL) gear deployments over time. This study presents habitat‐use data recovered from high‐resolution 5‐ and 10‐day pop‐up satellite archival tags (PSATs) attached to 47 surviving white marlin released from commercial and recreational fishing gears offshore of the U.S. East Coast, the northern Caribbean, and Venezuela between 2002 and 2004. Data recovered from transmitting tags indicated that white marlin spent nearly half of their time associated with warm, near‐surface waters (<10 m). All fish displayed frequent short duration (mean: 39.8 min) vertical excursions from surface waters to depths averaging 51 m. Qualitative and multivariate classifications of data from completely transmitted movements of surviving white marlin revealed two major types of descents: one pattern was characterized by deep ‘V’‐shaped excursions of relatively short duration (mean: 23.4 min) while the other featured descents that were more broadly ‘U’‐shaped and confined to a specific depth range for an extended period of time (mean: 75.8 min). Based on the frequency, persistence, and patterns of these vertical movements, white marlin appear to direct a considerable proportion of foraging effort well below surface waters, a behavior that may account for relatively high catch rates of white marlin on some deep‐set PLL deployments.     

Pelagic longline gear depth and shoaling

Temperature-depth recorders (TDRs) were attached to pelagic longline gear in the Hawaii-based commercial fishery to obtain actual fishing depths and to test the accuracy of catenary algorithms for predicting fishing depths. Swordfish gear was set shallow by typically deploying four hooks between successive floats. The observed depth of the settled deepest hook had a median value of 60 m for 333 swordfish sets. Tuna longline gear deployed more hooks between floats (mean = 26.8), and the observed median depth of the deepest hook was 248 m (n = 266 sets). Maximum gear depth was predicted from estimates of the longline sag ratio and catenary algorithms; however, depth was not predicted for all TDR-monitored sets because estimating sag ratios proved problematic. Swordfish sets had less slack in the main line and correspondingly smaller catenary angles (median = 54.2°) than tuna sets (median = 63.7°). Median values of the predicted catenary depth were 123 m for swordfish sets (n = 203) and 307 m for tuna sets (n = 198). Shallow swordfish sets reached only ∼50% of their predicted depth, while deeper tuna sets reached about 70%. These values indicated that capture depths using traditional catenary equations may be biased without the benefit of TDRs affixed to longlines. Generalized linear models (GLMs) and generalized additive models (GAMs) were developed to explain the percentage of longline shoaling as a function of predicted catenary depth and environmental effects of wind stress, surface current velocity, and current shear. The GAM explained 67.2% of the deviance in shoaling for tuna sets and 41.3% for swordfish sets. Predicted catenary depth was always the initial variable included in the stepwise process, and the inclusion of environmental information in the GAM approach explained an additional 10–17% of the deviance compared to the GLMs. The explanatory ability of the environmental data may have been limited by the scale of the observations (1° in space; weekly or monthly in time) or the geometric (transverse versus in-line) forcing between the environment and longline set. Longline gear models with environmental forcing affecting shoaling may be improved in future studies by incorporating contemporaneous environmental information, although this may restrict analyses to fine-scale experimental longlines.     

Dynamic simulation of tuna longline gear using numerical methods

ABSTRACT:   The underwater shape and hook depth of tuna longline gear are important factors determining fishing performance. In this study, how the shape of tuna longline gear changes in response to sea conditions and gear rigging is explained. Physical models of underwater gear shape were made to simulate fishing gear and analyzed according to the direction and velocity of currents. Then experiments with small-scale models were conducted in a flume tank to confirm the accuracy of the simulation analysis. Finally, the simulation was examined relative to actual longline fishing gear. This approach provided an improvement over previous analytical methods that did not consider fishing gear shape in response to different sea conditions. A useful result is an improved understanding of the relationship between ocean currents and the configuration of longline gear (the shortening ratio, and number of hooks per basket). These factors affect hook depth which, in turn, affects selectivity. Application of these results could lead to more effective and efficient fishing under different sea conditions.     

Acoustic telemetry versus monitored longline fishing for studying the vertical distribution of pelagic fish: bigeye tuna (Thunnus obesus) in French Polynesia

This study compares detailed, nearly continuous, observations on bigeye tuna, Thunnus obesus equipped with electronic tags, with discrete observations on a larger number of individuals from fishing experiments in order to validate the use of instrumented longlines to study the vertical distribution of fish. We show that the depth distributions obtained from the two different observation techniques regarding different environmental variables (temperature, dissolved oxygen (DO), prey distribution) are similar. Bigeye tuna do not seem to be attracted by baits in the vertical dimension (no modification of their vertical distribution by the fishing gear), which allows the use of instrumented longlines to study the vertical behaviour of pelagic species. This technique, when used with appropriate deployment strategy, could therefore represent an alternative to electronic tags (acoustic or archival tags) when there is a need to determine the vertical distribution of fish species by size or sex, in different environments for the study of fishery interactions.     

Forage and migration habitat of loggerhead (Caretta caretta) and olive ridley (Lepidochelys olivacea) sea turtles in the central North Pacific Ocean

Satellite telemetry from 26 loggerhead (Caretta caretta) and 10 olive ridley (Lepidochelys olivacea) sea turtles captured and released from pelagic longline fishing gear provided information on the turtles’ position and movement in the central North Pacific. These data together with environmental data from satellite remote sensing are used to describe the oceanic habitat used by these turtles. The results indicate that loggerheads travel westward, move seasonally north and south primarily through the region 28–40°N, and occupy sea surface temperatures (SST) of 15–25°C. Their dive depth distribution indicated that they spend 40% of their time at the surface and 90% of their time at depths <40 m. Loggerheads are found in association with fronts, eddies, and geostrophic currents. Specifically, the Transition Zone Chlorophyll Front (TZCF) and the southern edge of the Kuroshio Extension Current (KEC) appear to be important forage and migration habitats for loggerheads. In contrast, olive ridleys were found primarily south of loggerhead habitat in the region 8–31°N latitude, occupying warmer water with SSTs of 23–28°C. They have a deeper dive pattern than loggerheads, spending only 20% of their time at the surface and 60% shallower than 40 m. However, the three olive ridleys identified from genetics to be of western Pacific origin spent some time associated with major ocean currents, specifically the southern edge of the KEC, the North Equatorial Current (NEC), and the Equatorial Counter Current (ECC). These habitats were not used by any olive ridleys of eastern Pacific origin suggesting that olive ridleys from different populations may occupy different oceanic habitats.