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.     

基于海表温因子的太平洋褶柔鱼冬生群资源丰度预测模型比较

太平洋褶柔鱼是世界上重要的大洋性经济柔鱼类资源,其资源易受海洋环境因子的影响,科学预测其资源丰度有利于科学生产和管理。本实验依据2000—2010年太平洋褶柔鱼冬生群单位捕捞努力量渔获量(CPUE),以及产卵期间(1—3月)产卵场(28°~40°N、125°~140°E)的海表温(SST)数据,进行SST与CPUE的相关性分析,选取统计学有意义的SST作为影响资源丰度的因子,分别建立多元线性和BP神经网络的资源丰度预报模型,并利用2011和2012年的CPUE进行验证。结果显示,CPUE与产卵场1—3月SST相关系数较高的海域分别为1月的S1(30.5°N,136.5°E)和S2(31.5°N,136.5°E),2月的S3(30.5°N,137.5°E)和S4(30.5°N,135.5°E),3月的S5(37.5°N,129.5°E)和S6(37.5°N,130.5°E)。在多元线性及不同结构的BP神经网络等5种预报模型中,结构为6-4-1的BP神经网络模型预测精度最高,2011—2012年CPUE预测值精度平均为98%。研究表明,30°~32°N、135°~138°E和37°~38°N、129°~131°E附近海域的6个环境因子代表着1—3月产卵场暖流(黑潮和对马海流)势力的强弱,决定着当年太平洋褶柔鱼冬生群资源丰度,所建立的BP神经网络模型可作为其资源丰度的预测模型。     

基于空间相关性的西北太平洋柔鱼CPUE标准化研究

CPUE标准化方法通常都假设名义CPUE之间是相互独立且没有相关性,然而鱼类集群分布通常存在着空间相关性,为此本研究以西北太平洋柔鱼的CPUE标准化为例,采用1999-2012年6-11月中国鱿钓生产数据以及对应的海表面温度和叶绿素浓度的环境数据,将空间相关性加入广义线性模型(general linear model,GLM)中.在空间GLM模型中运用4个距离模型(指数模型、球面模型、线性模型和高斯模型),进行标准GLM模型和4种空间GLM模型的CPUE标准化结果比较.结果发现,4种空间GLM模型均比标准GLM模型的最小信息准则(akaike information criterion,AIC)更小,标准化结果更准确.同时,在4个距离模型中,指数模型的AIC值最小,其CPUE标准化结果最佳.研究表明,在CPUE标准化中,鉴于鱼类集群与分布特性,应该充分考虑空间相关性这一因素.     

Modeling environmental,temporal and spatial effects on twaite shad (Alosa fallax) by‐catches in the central Mediterranean Sea

Modeling the relationships between environmental factors and the distribution at sea of species of conservation interest can be useful in predicting their occurrence from a local to a regional scale. This information is essential for planning management and conservation initiatives. In this study, generalized additive models (GAMs) were applied to investigate the influence of environmental, temporal and spatial variables on the catch rates of the twaite shad Alosa fallax (Lacepède) by the pelagic trawl fishery in the north‐central Adriatic Sea. Presence/absence and abundance [catch per unit effort (CPUE)] data between 2006 and 2012 were separately modeled, and the two models were then validated using a test data set. The most important factor influencing the presence and abundance of adult twaite shads was the spatial predictor (latitude × longitude). Two areas of major shads aggregations were observed, the most important of which being located near the estuaries of three main river systems of northern Italy. The twaite shad presence was also significantly affected by season, the largest and lowest occurrences being observed in autumn and spring, respectively. Among the environmental variables tested, only sea surface temperature was included in both models. Alosa fallax showed a wide thermal tolerance (6–27°C) with preference for temperature around 23°C. The model developed from the abundance data showed a moderate predictive power, whereas the accuracy of the presence/absence model was rather low. Some conclusions on the ecological requirements of A. fallax at sea arising from this study are useful to orient future monitoring and research programs and to develop effective conservation actions.     

Environmental influences on Atlantic bluefin tuna (Thunnus thynnus) catch per unit effort in the southern Gulf of St. Lawrence

The Atlantic bluefin tuna (Thunnus thynnus) population in the western Atlantic supports substantial commercial and recreational fisheries. Despite quota establishment and management under the auspices of the International Commission for the Conservation of Atlantic Tunas, only small increases in population growth have been estimated. In contrast to other western bluefin tuna fisheries indices, contemporary estimates of catch per unit effort (CPUE) in the southern Gulf of St. Lawrence have increased rapidly and are at record highs. This area is characterized by the Cold Intermediate Layer (CIL) that is defined by waters <3°C and located at depths of 30–40 m in September. We investigated the influence of several in situ environmental variables on the bluefin tuna fishery CPUE using delta‐lognormal modelling and relatively extensive and consistent oceanographic survey data, as well as dockside monitoring and mandatory logbook data associated with the fishery. Although there is considerable spatial and temporal variation of water mass characteristics, the amount of available habitat in the southern Gulf of St. Lawrence (assuming a > 3°C thermal ambit) for bluefin tuna has been increasing. The percentage of the water column occupied by the CIL was a significant environmental variable in the standardization of CPUE estimates. There was also a negative relationship between the spatial extents of the CIL and the fishery. Properties of the CIL account for variation in the bluefin tuna CPUE and may be a factor in determining the amount of available feeding habitat for bluefin tuna in the southern Gulf of St. Lawrence.     

Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna (Thunnus albacares) longline CPUE data

Catch-per-unit-effort (CPUE) data have often been used to obtain a relative index of the abundance of a fish stock by standardizing nominal CPUE using various statistical methods. The theory underlying most of these methods assumes the independence of the observed CPUEs. This assumption is invalid for a fish population because of their spatial autocorrelation. To overcome this problem, we incorporated spatial autocorrelation into the standard general linear model (GLM). We also incorporated into it a habitat-based model (HBM), to reflect, more effectively, the vertical distributions of tuna. As a case study, we fitted both the standard-GLM and spatial-GLM (with or without HBM) to the yellowfin tuna CPUE data of the Japanese longline fisheries in the Indian Ocean. Four distance models (Gaussian, exponential, linear and spherical) were examined for spatial autocorrelation. We found that the spatial-GLMs always produced the best goodness-of-fit to the data and gave more realistic estimates of the variances of the parameters, and that HBM-based GLMs always produced better goodness-of-fit to the data than those without. Of the four distance models, the Gaussian model performed the best. The point estimates of the relative indices of the abundance of yellowfin tuna differed slightly between standard and spatial GLMs, while their 95% confidence intervals from the spatial-GLMs were larger than those from the standard-GLM. Therefore, spatial-GLMs yield more robust estimates of the relative indices of the abundance of yellowfin tuna, especially when the nominal CPUEs are strongly spatially autocorrelated.     

西南大西洋阿根廷滑柔鱼渔业中国大陆鱿钓船CPUE标准化

阿根廷滑柔鱼是我国重要的头足类渔业之一,对其单位捕捞努力量渔获量( CPUE)进行标准化是对其资源评估的重要内容。本研究根据2000～2010年我国在西南大西洋的产量统计数据和卫星遥感获得的海洋环境数据（表温,表温水平梯度,海面高度,叶绿素浓度）,利用广义线性模型（GLM,general linear model）和广义加性模型（GAM,generalized additive model）对其CPUE进行标准化。GLM模型结果表明,年、纬度、表温以及交互项年与纬度对CPUE影响最大。GAM模型研究结果表明,年、月、经度、纬度、表温、海面高度以及交互项年与纬度、年与经度对CPUE影响较大。根据AIC数值,包含上述8个显著变量的GAM模型为最佳模型,对CPUE的解释率为49.20%。高CPUE出现在夏季表温为12～16°C、海面高为-20~20cm、46.5°～48.5°S海域。研究表明,GAM模型较GLM模型更适合用于西南大西洋阿根廷滑柔鱼CPUE标准化。     

A new spatial framework incorporating uncertain stock and fleet dynamics for estimating fish abundance

Relative abundance indices based on catch and effort data can become biased unless consideration is given to the spatial dynamics of the fishery such as changes in either the spatial distribution of fishing effort or the range of the stock over time. The construction of such indices therefore needs to take into account features of the fishery itself. In this paper, a general framework is presented for developing more appropriate abundance indices based on fishery catch and effort data. In developing this framework, it adopts the approach of (i) developing a range of hypotheses which encompass the uncertainties in the spatial–temporal dynamics of the stock and the fishing effort, (ii) identifying the hypotheses underlying the different CPUE series, and (iii) evaluating the available information relative to these hypotheses as the basis for evaluating CPUE indices. Observations from the fishery for southern bluefin tuna (Thunnus maccoyii) are used to illustrate various hypotheses about the nature of the fishery which can be used to construct indices of stock abundance while a simple simulation framework is used to explore the implications of some of these hypotheses on the accuracy of such indices.     

Combined use of trawl fishery and research vessel survey data in a multivariate autoregressive state-space (MARSS) model to improve the accuracy of abundance index estimates

Fishery-dependent catch per unit effort (CPUE) data have been used as an abundance index (AI) in fish stock assessments. However, fishery-dependent CPUE data are influenced not only by changes in fish abundance but also by other factors, such as the choice or restrictions of fishing grounds to operate. Accordingly, bias may arise in AIs due to a lack of data from unfished or rarely fished areas. To improve the accuracy of AI estimates, spatially arranged CPUE datasets from both trawl fisheries and research vessel surveys in the East China Sea were concurrently analyzed in the present study using a multivariate autoregressive state-space (MARSS) model. Survey datasets complemented information on stock status in the fishing areas where fishery-dependent datasets were limited. As a result, the combined use of datasets from both sources effectively improved the accuracy of estimates of AIs and the spatial distribution of the population density of each fish species.     

Evaluating habitat suitability indices derived from CPUE and fishing effort data for Ommatrephes bratramii in the northwestern Pacific Ocean

Neon flying squid (Ommastrephes bartramii) plays an important role in the pelagic ecosystem and is an international fishery resource with high commercial value in the North Pacific Ocean. The west stock of winter–spring cohort of this species is an important target for the squid-jigging vessels of Japan, Korea and China (including Taiwan). The squid has a life span of less than 12 months, and its population dynamics is heavily influenced by its environment. Thus, a good understanding of its interactions with the habitats, often quantified with a habitat suitability index (HSI) model, is critical in developing a sustainable fishery. In this study, using the Chinese commercial squid-jigger fishery data and corresponding environmental variables we conducted HSI modeling to evaluate the habitat of the west stock of winter–spring cohort of neon flying squid in the northwestern Pacific Ocean. We compared catch per unit effort (CPUE) and fishing effort data in HSI modeling. This study suggests that the CPUE-based HSI model tends to overestimate the ranges of optimal habitats and under-estimate monthly variations in the spatial distribution of optimal habitats. We conclude that a fishing effort-based HSI model performs better in defining optimal habitats for neon flying squid. According to the fishing-effort-based HSI model, the optimal ranges of the following key habitat variables are defined: from 16.6 to 19.6 °C for SST, from 5.8 to 12 °C for temperature at depths of 35 m, from 3.4 to 4.8 °C for temperature at depth of 317 m, from 33.10 to 33.55 psu for SSS and from ?20 cm to ?4 cm for SLH.     

Impact of paralarvae and juveniles feeding environment on the neon flying squid (Ommastrephes bartramii) winter–spring cohort stock

In this study, we found that there were significant positive correlations between the catch per unit effort (CPUE, a squid abundance index) for the neon flying squid (Ommastrephes bartramii) winter–spring cohort and the satellite‐derived chlorophyll a concentrations in their spawning grounds located at 140–160°E where 21°C < sea surface temperature < 25°C from February to May. The spawning grounds of the winter–spring cohort are located in a quiet stream region, and a particle tracking experiment, based on the velocity field obtained from an ocean data assimilation system, showed that paralarvae and juveniles aged <90 days remained in their spawning grounds and the chlorophyll a concentration in their habitat had a significant positive correlation with the CPUE. A backward particle tracking experiment also showed that the chlorophyll a concentration in the spawning grounds had a significant positive correlation with the autumn–winter mixed layer depth. Based on these results, we hypothesize that the CPUE interannual variability is caused by variations in the feeding environment of the paralarvae and juveniles, which may be linked to autumn–winter mixed layer depth variations.     

印度洋长鳍金枪鱼资源评估的影响因素分析

多个模型被用于印度洋长鳍金枪鱼(Thunnus alalunga)的资源评估,但这些模型的评估结果均存在较大的不确定性,为此,本文对影响印度洋长鳍金枪鱼资源评估的因素进行了分析。分析结果认为:(1)由于渔业数据存在不报、漏报或混报及采样样本数过低、采样协议出现变化等问题,造成印度洋长鳍金枪鱼渔业的渔获量、体长组成或年龄组成数据存在质量问题;(2)尽管对单位捕捞努力渔获量(catch per unit effort,CPUE)进行了标准化,但目标鱼种变化及捕捞努力量空间分布变化仍严重影响了标准化CPUE数据的质量;(3)印度洋长鳍金枪鱼的种群生态学及繁殖生物学研究仍比较薄弱,种群结构、繁殖、生长、自然死亡信息比较缺乏,在资源评估中,相关参数设置需借用其他洋区的研究结果;(4)海洋环境对印度洋长鳍金枪鱼的资源变动与空间分布具有显著影响,但评估模型较少考虑海洋环境的影响。由于上述问题的存在,导致当前评估结果存在较大不确定性。未来,应继续探索提高资源评估质量的方法,同时研究建立管理策略评价框架,以避免渔业资源评估结果的不确定性对该渔业可持续开发的影响。     

Improving management advice through spatially explicit models and sharing information

Recent assessments of Chilean shrimp, Heterocarpus reedi, in central Chile have been conducted separately for the northern and southern zones of the fishery and treating them as two separate stocks. However, it is not clear whether H. reedi of the two zones interact with one another or whether they share similar characteristics. Such knowledge is necessary to determine whether they should be modeled as separate “stocks” or as a single stock. This has motivated the use of the Pella–Tomlinson model to test whether there are spatial differences in the population dynamics of H. reedi in the two zones and whether sharing information between the zones improves management advice. We test if it is better, from a stock assessment point of view, to model the stock as one unit in the whole area, or as two separate stocks. In the single-stock model, we sum the catch data of both zones, but each catch-per-unit-of-effort index is fit as a separate data set, using a joint likelihood. Under the single-stock hypothesis, the best model fit was the symmetric production function (i.e. the Schaefer model for which the biomass that supports maximum sustainable yield as a proportion of carrying capacity (B_MSY/B₀) = 0.5), with different catchability coefficients for each CPUE index, but a shared standard deviation of the log-normal likelihood function. Under the two-stock hypotheses, both catch and CPUE data were separated for each zone in the model. In this case, the best model fit is also the one with symmetrical production curve, and the only parameter that differed between the zones was B₀. However, B₀ per unit of habitat was similar for the two zones. Also, the precision of estimated management quantities was improved by modeling the appropriate spatial structure and sharing information among zones. The results suggest that the demographic parameters are similar for the two zones. It appears that the main difference between the two zones is the exploitation history, with the catch in the southern zone being reduced earlier than in the northern zone and consequently the biomass in the southern zone increased earlier than in the northern zone. This implies that local depletion can occur in this stock and that differences in management among zones may require explicitly modeling sub-stocks in the assessment of this and other species.     

What are the effects of climate on the offshore and onshore movements of adult grooved tiger prawns Penaeus semisulcatus in tropical northern Australia?

Historical catch per unit effort (CPUE) from the period in the development of commercial fisheries prior to the implementation of any temporal or spatial closures can provide insight into how environmental factors affect life history events. These insights can then inform contemporary fishery management practices to improve sustainability. We examined the timing of the offshore dispersal and onshore movement to aggregate on the spawning grounds of the grooved tiger prawn Penaeus semisulcatus in the north‐west Gulf of Carpentaria using all available historical CPUE data between 1970 and 1987. The impact of climate conditions on the timing of the winter minimum CPUE (dispersal) was quantified by fitting univariate relationships with non‐parametric weighted local regression (Loess). The timing of the winter minimum CPUE was delayed by 5 weeks in years with at least 1 week of rainfall > 250 mm, and was delayed by 5 weeks in years when the wet season continued through April, rather than ending in March. Under current climate predictions, rainfall is projected to increase in variability in this region. Our results indicate that the sustainability of the prawn trawl fishery may be enhanced if seasonal fishing effort restrictions and closure dates are not fixed, but are managed to reflect variation in the timing of the end of the previous wet season.     

Insights into the horizontal movements,migration patterns,and stock affiliation of California swordfish

This study reports on the horizontal movements of swordfish (Xiphias gladius L.) tagged during deep‐set fishery trials off the California coastline. Position estimates from several electronic tag types were used to better understand swordfish stock structure and regional affiliation with current boundary hypotheses used to manage swordfish in the eastern north Pacific. Swordfish were outfitted with (a) satellite‐linked mark–recapture tags (n = 66), (b) electronic data storage tags that were recaptured (n = 16), (c) fin‐mounted Argos transmitters (n = 6), and (d) satellite‐linked archival tags (n = 4). Twenty‐six percent of tagged swordfish reported close to (<225 km) their deployment location within the southern California Bight (SCB). Of the 50 swordfish that moved outside the SCB, 76% exhibited affiliation to the Eastern Pacific Ocean (EPO) management unit, 20% moved into the Western and Central North Pacific (WCNP) and 4% spent time within both the EPO and WCNP boundaries. Mean displacement between deployment and reporting locations was 1,250 ± 1,375 km, with daily rates of movement up to 55 km/day. Seasonal migrations ranged from the equator (0.8°N.132.4°W) to the Hawaiian Islands (17.0°N/154.2°W), with multiple individuals returning to the initial tagging locations the subsequent season. Seasonal site fidelity exhibited by several individuals highlights the importance of the SCB foraging grounds. While no evidence of trans‐equatorial or trans‐Pacific crossing was documented, extensive movements validate the highly migratory nature of California swordfish and support the need for future inclusion of spatial distribution data in management. Findings suggest that SCB swordfish may exhibit a higher level of EPO connectivity than previously proposed.     

Integrating the standardization of catch-per-unit-of-effort into stock assessment models: testing a population dynamics model and using multiple data types

Standardization of catch-per-unit-of-effort (CPUE) data can be integrated into stock assessment methods. We apply this method to the stock of trevally (Pseudocaranx dentex) off the west coast of New Zealand to address: (1) whether the stock assessment model explains all of the annual variation in the CPUE data, and (2) the impact on the assessment results of how the catch-at-age data are weighted. If not all of annual variation in CPUE is explained by the stock assessment model, the assessment may be statistically inadequate. The inadequacy may be in the representation of the population dynamics, in the relationship between CPUE and abundance, or due to additional variation in CPUE left unexplained by the independent variables. Catch-at-age data often have too much influence on the estimated abundance trajectory, so the sample size used in the catch-at-age likelihood function is often reduced when applying age-structured stock assessment methods. The integrated approach automatically places more weight on the CPUE data compared to the catch-at-age data, and may therefore provide an alternative to arbitrarily downweighting catch-at-age data.     

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.     

Using commercial landings data to identify environmental correlates with distributions of fish stocks

We examined the efficacy of using commercial landings data to identify potential environmental correlates with fish distributions. Historical landings data for two commercially important species, Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus), were used along with historical conductivity, temperature, and depth (CTD) data to infer monthly mean spatial distributions of catch per unit effort (CPUE), temperature, salinity, density, and stratification over Georges Bank. Relationships between CPUE and these environmental variables plus bottom sediment type and bottom depth were examined on seasonal, annual, and interannual time scales. Empirical analysis suggests that both cod and haddock are found preferentially in water temperatures of approximately 5°C in winter/spring, and as high as 10–11°C during late fall. Both species are also found preferentially over coarse sand and gravel as opposed to fine sand, and in water depths between 60 and 70 m. These preferences appear to vary seasonally. The above results are consistent with findings of previous investigators using semi‐annual research trawl survey data, and suggest that commercial landings data, despite their known errors and biases, can be used effectively to infer associations between fish and their environment.     

Application of the Tweedie distribution to zero-catch data in CPUE analysis

We focus on the zero-catch problem of CPUE (catch per unit effort) standardization. Because the traditional CPUE model with a log-normal error structure cannot be applied in this case, three methods have often been utilized as follows:

(1) Ad hoc method adds a small constant value to all response variables.

(2) Catch model with a Poisson or negative-binomial (NB) error structure.

(3) Delta-type two-step method such as the delta-normal model (after estimating the ratio of zero-catch using a logit or probit model, a model such as CPUE log-normal or Catch-Poisson is applied to CPUE without zero-data).

However, there are some statistical problems with each of these methods.In this paper, we carried out the CPUE standardization mainly using the Tweedie distribution model based on the actual by-catch data (silky shark, Carcharhimus falciformis, in the North Pacific Ocean caught by Japanese training vessels) including many observations with zero-catch (>2/3rd) and tuna fishery data as a target (yellowfin tuna, Thunnus albacares, in the Indian Ocean caught by Japanese commercial vessels) where the ratio of zero-catch is not so high (<1/3rd). The Tweedie model is an extension of compound Poisson model derived from the stochastic process where the weight of the counted objects (i.e., number of fish) has a gamma distribution and has an advantage of handling the zero-catch data in a unified way.We also compared four candidate models, the Catch-NB model, ad hoc method, Delta-lognormal model (delta-type two-step method) and Tweedie distribution, through CPUE analyses of actual fishery data in terms of the statistical performance. Square error and Pearson's correlation coefficient were calculated based on the observed CPUE and the corresponding predicted CPUE using the n-fold cross-validation.As a result, the differences in the trend of CPUE between years and model performance between the ad hoc method and Tweedie model were found to be not so large in the example of yellowfin tuna (target species). However, the statistical performance of Tweedie distribution is rather better than Delta-lognormal model, the Catch-NB distribution and ad hoc method in the example of silky shark (by-catch species). Standardized CPUE year trend of ad hoc method was found to be quite different from that of the Tweedie distribution and other two models. Model performance of the Tweedie distribution is good judging from the 5-fold cross-validation using the fishery data if including many zero-catch data such as by-catch species.     

基于渔船捕捞行为特征的远洋延绳钓渔场捕捞强度计算

渔场捕捞强度信息可以为渔业资源评估和管理提供帮助。本研究结合2017年10—11月船舶自动监控系统(AutomaticIdentificationSystem,AIS)信息和同期中国中西太平洋延绳钓渔船捕捞日志数据,通过挖掘延绳钓渔船作业航速和航向特征,建立渔场作业状态识别模型,提取渔场捕捞强度信息。以3～9节为航速阈值和0°～10°及300°～360°为航向阈值,渔船作业状态识别准确率为68.29%。阈值识别和日志记录的捕捞强度信息在空间上相关性很高(0.96),基于AIS信息挖掘的渔船捕捞强度空间分布特征和实际非常相似。阈值识别和日志记录的捕捞强度信息在空间上与单位捕捞努力量渔获量(catch per unite of effort, CPUE)、渔获尾数、渔获重量和投钩数的空间相关系数均大于0.62,基于AIS信息挖掘的渔船空间捕捞强度也可替代用于渔业资源分析。