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1.
Blue marlin is distributed throughout tropical and temperate waters in the Pacific Ocean. However, the preference of this species for particular habitats may impact its vulnerability to being caught. The relationship between spatio‐temporal patterns of blue marlin abundance and environmental factors is examined using generalized additive models fitted to catch and effort data from longline fisheries. The presence of blue marlin, and the catch rate given presence, are modeled separately. Latitude, longitude, and sea‐surface temperature explain the greatest proportion of the deviance. Spatial distributions of relative density of blue marlin, based on combining the probability of presence and relative density given presence, indicate that there is seasonal variation in the distribution of blue marlin, and that the highest densities occur in the tropics. Seasonal patterns in the relative density of blue marlin appear to be related to shifts in SST. The distribution and relative abundance of blue marlin are sufficiently heterogeneous in space and time that the results of analyses of catch and effort data to identify ‘hotspots’ could be used as the basis for time‐area management to reduce the amount of blue marlin bycaught in longline fisheries. 相似文献
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Mario La Mesa Gabriele La Mesa Barbara Catalano Christopher D. Jones 《Fisheries Oceanography》2016,25(6):624-636
The Bransfield Strait and adjacent waters represent one of the most important areas of larval retention off the Antarctic Peninsula. The species composition of larval fish assemblages has been described in detail in previous surveys carried out in the area, but the role of environmental parameters influencing the spatial distribution of early life stages was poorly known. By applying generalized additive models and multivariate analyses, we evaluated the role of environmental variables in shaping the small‐scale distribution of larval fish and investigated the spatial structure of the larval assemblage. It consisted of a few dominant notothenioid species, such as Champsocephalus gunnari, Lepidonotothen squamifrons, Lepidonotothen larseni, Pleuragramma antarctica and Trematomus scotti, and several other rarely caught species. Sea water temperature, salinity and sampling depth were the most important factors determining the spatial distribution of fish with different relative contributions, together explaining more than 80% of total deviance observed. Species richness was mostly affected by salinity, probably due to the narrow range of salinity preference by the species. Cluster analysis of abundance and presence data identified six and five distinct groups, respectively, each of them with substantial contributions of single or rarely two species. Differences in reproductive strategies of adult populations and spatial distribution of early life stages driven by different larval behaviour in response to environmental factors contribute to maintaining a well‐structured larval fish assemblage, ensuring spatial and food niche partitioning. 相似文献
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Generalized additive models (GAMs) were fitted to sardine (Sardina pilchardus) egg distribution data from three daily egg production method surveys. The results showed that the area of egg cover off Portugal decreased significantly from 11 800 km2 in 1988 to 7000 km2 in 1997 and 7400 km2 in 1999. This is because of a significant reduction in sardine egg presence off northern Portugal, GAM estimated areas being similar or higher in the late 1990s for southwestern and southern Portugal. The distributional area covered by larvae was not estimated for 1988 (larval distribution extended beyond the survey area), although it was probably higher than the 9600 km2 for 1997 and 5500 km2 for 1999. In 1997 and 1999, the Gulf of Cadiz was also sampled, indicating extensive areas with sardine eggs and larvae (more than 50% of the total area of distribution off Portugal). Standardized data from 15 ichthyoplankton surveys between 1985 and 2000 show a decline in the mean probability of egg presence within the Portuguese continental shelf from the mid‐1980s to the late‐1990s, because of a marked reduction in egg presence off northern Portugal. Sardine larval data from the same surveys suggest that the reduction in mean probability of presence in the north is less marked than for eggs (although this comparison ignores the presence of sardine larvae beyond the continental shelf in the 1980s). Similar changes off northern Portugal and western Galicia are observed in commercial sardine catches and the acoustically estimated area of fish distribution. It is possible that the observed decline in spawning area off northwestern Iberia during the 1990s is indirectly reflecting the prevalence of environmental conditions detrimental to sardine recruitment (northerly winds during winter that favour coastal upwelling and offshore transport), which have reduced the spawning contribution of young fish in that area. 相似文献
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Steven M. Porter 《Fisheries Oceanography》2022,31(1):108-122
Multiyear periods of relatively cold temperatures (2007–2013) and warm temperatures (2001–2005 and 2014–2018) altered the eastern Bering Sea ecosystem, affecting ocean currents and wind patterns, plankton community, and spatial distribution of fishes. Yellowfin sole Limanda aspera larvae were collected from the inner domain (≤50 m depth) of the eastern Bering Sea among four warm years (2002, 2004, 2005, 2016), an average year (2006), and three cold years (2007, 2010, 2012). Spatial distribution and density of larvae among those years was analyzed using generalized additive models that included timing of sea-ice retreat, areal coverage of water ≤0°C, and water temperature as covariates. Analyses indicated a combination of temperature effects on the location and timing of spawning, and on egg and larval survival, may explain the variation in larval density and distribution among years. During warm years, higher density and wider spatial distribution of larvae may be due to earlier spawning, an expansion of the spawning area, and higher egg and larvae survival due to favorable temperatures. Larval distribution contracted shoreward, and density was lower during cold conditions and was likely due to fish spawning closer to shore to remain in preferred temperatures, later spawning, and increased mortality. Predicted drift trajectories from spawning areas showed that larvae would reach nursery grounds in most years. Years when the drift period was longer than the pelagic phase of the larvae occurred during both warm and cold conditions indicating that settlement outside of nursery areas could happen during either temperature condition. 相似文献
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Stephanie Brodie Alistair J. Hobday James A. Smith Jason D. Everett Matt D. Taylor Charles A. Gray Iain M. Suthers 《Fisheries Oceanography》2015,24(5):463-477
Defining the oceanic habitats of migratory marine species is important for both single species and ecosystem‐based fisheries management, particularly when the distribution of these habitats vary temporally. This can be achieved using species distribution models that include physical environmental predictors. In the present study, species distribution models that describe the seasonal habitats of two pelagic fish (dolphinfish, Coryphaena hippurus and yellowtail kingfish, Seriola lalandi), are developed using 19 yr of presence‐only data from a recreational angler‐based catch‐and‐release fishing programme. A Poisson point process model within a generalized additive modelling framework was used to determine the species distributions off the east coast of Australia as a function of several oceanographic covariates. This modelling framework uses presence‐only data to determine the intensity of fish (fish km?2), rather than a probability of fish presence. Sea surface temperature (SST), sea level anomaly, SST frontal index and eddy kinetic energy were significant environmental predictors for both dolphinfish and kingfish distributions. Models for both species indicate a greater fish intensity off the east Australian coast during summer and autumn in response to the regional oceanography, namely shelf incursions by the East Australian Current. This study provides a framework for using presence‐only recreational fisheries data to create species distribution models that can contribute to the future dynamic spatial management of pelagic fisheries. 相似文献
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时空和环境因子对海州湾方氏云鳚资源丰度分布的影响 总被引：6，自引：5，他引：1
根据2011年3月、5月、7月、9月和12月在海州湾及邻近海域进行的渔业资源底拖网调查数据,研究了该海域方氏云鳚(Pholis fang)资源丰度的分布特征,并用广义可加模型(GAM)分析了其资源丰度分布与时空和环境因子之间的关系。结果表明,海州湾及邻近海域方氏云鳚资源丰度呈现明显的时空变化。方氏云鳚资源丰度7月最高,9月最低;其主要分布在海州湾东北部海域,这与受到南黄海西侧冷中心的影响有关。GAM分析表明,月份、水深和离岸距离对方氏云鳚的资源丰度具有显著影响。方氏云鳚的资源丰度随水深的增加而增大,随离岸距离的增大呈现先增加后降低的趋势。在离岸距离70 km左右,水深14~36 m时,方氏云鳚的资源丰度较大,这可能与其产卵洄游和环境因子的时空差异有关。 相似文献
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Ana de la Torriente Jose Manuel Gonzlez‐Irusta Ricardo Aguilar Luis Miguel Fernndez‐Salas Antonio Punzn Alberto Serrano 《水产资源保护:海洋与淡水生态系统》2019,29(5):732-750
- An ecologically representative, well‐connected, and effectively managed system of marine protected areas (MPAs) has positive ecological and environmental effects as well as social and economic benefits. Although progress in expanding the coverage of MPAs has been made, the application of management tools has not yet been implemented in most of these areas.
- In this work, distribution models were applied to nine benthic habitats on a Mediterranean seamount within an MPA for conservation purposes. Benthic habitat occurrences were identified from 55 remotely operated vehicle (ROV) transects, at depths from 76 to 700 m, and data derived from multibeam bathymetry. Generalized additive models (GAMs) were applied to link the presence of each benthic habitat to local environmental proxies (depth, slope, backscatter, aspect, and bathymetric position index, BPI).
- The main environmental drivers of habitat distribution were depth, slope, and BPI. Based on this result, five different geomorphological areas were distinguished. A full coverage map indicating the potential benthic habitat distribution on the seamount was obtained to inform spatial management.
- The distribution of those habitats identified as vulnerable marine ecosystems (VMEs) was used to make recommendations on zonation for developing the management plan of the MPA. This process reveals itself as an appropriate methodological approach that can be developed in other areas of the Natura 2000 marine network.
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Bruno L. Mourato Fábio Hazin Keith Bigelow Michael Musyl Felipe Carvalho Humberto Hazin 《Fisheries Oceanography》2014,23(1):32-44
Spatial and temporal trends of sailfish catch rates in the southwestern and equatorial Atlantic Ocean in relation to environmental variables were investigated using generalized additive models and fishery‐dependent data. Two generalized additive models were fit: (i) ‘spatio‐temporal’, including only latitude, longitude, month, and year; and (ii) ‘oceanographic’, including sea surface temperature (SST), chlorophyll‐a concentration, wind velocity, bottom depth, and depth of mixed layer and year. The spatio‐temporal model explained more (average ~40%) of the variability in catch rates than the oceanographic model (average ~30%). Modeled catch rate predictions showed that sailfish tend to aggregate off the southeast coast of Brazil during the peak of the spawning season (November to February). Sailfish also seem to aggregate for feeding in two different areas, one located in the mid‐west Atlantic to the south of ~15°S and another area off the north coast of Brazil. The oceanographic model revealed that wind velocity and chlorophyll‐a concentration were the most important variables describing catch rate variability. The results presented herein may help to understand sailfish movements in the Atlantic Ocean and the relationship of these movements with environmental effects. 相似文献
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Gin Swen Ham Elise Lahaye Massimiliano Rosso Aurelie Moulins Ellen Hines Paola Tepsich 《水产资源保护:海洋与淡水生态系统》2021,31(8):2257-2277
- Mediterranean fin whales aggregating in the Pelagos Sanctuary in summer to feed are exposed to vessel collision risk, particularly from high-speed ferries.
- This study developed models to predict summer fin whale distribution using a generalized additive model (GAM) and MaxEnt, with the aim of providing a tool to identify potential high whale–ferry collision risk areas along ferry routes within the Pelagos Sanctuary during summertime.
- Models were trained using sightings data collected in the summer months of 2009–2018 on board ferries crossing the central area of the Pelagos Sanctuary. Environmental predictors were bathymetry and mean sea surface chlorophyll concentration of the annual spring bloom period.
- The predictive ability of GAM and MaxEnt was assessed using existing knowledge of summer fin whale distribution in the region. GAM (deviance explained = 20.2%) predictions matched documented distributions more closely than that of MaxEnt, with highest predicted fin whale occurrence in deep offshore waters (>2000 m) encompassing the central north-western and western regions, and in the south-eastern region, consistent with known fin whale habitats within the Pelagos Sanctuary. Inter-annual variability was evident, influencing collision risk areas.
- Collision risk was estimated as a function of the overlap between the predicted probability of fin whale occurrence and ferry density estimated from Automated Identification System data. Ferry routes that cross the northern and eastern regions of the Pelagos Sanctuary presented relatively higher collision risk.
- Areas with changes in risk intensity between the years were temporally and spatially dynamic: some appeared intermittently throughout the study period while others persisted over consecutive years or recurred in different years.
- Due to the vastness of the Pelagos Sanctuary, vessel speed reduction maybe a more practical measure to manage collision risk than re-routing shipping lanes. A combination of Seasonal Management Areas and Dynamic Management Areas approaches could be adopted for high-risk areas.
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HONGSHENG BI WILLIAM T. PETERSON JESSE LAMB EDMUNDO CASILLAS 《Fisheries Oceanography》2011,20(2):125-138
Yearling Chinook (Oncorhynchus tshawytscha) and coho salmon (Oncorhynchus kisutch) were sampled concurrently with physical variables (temperature, salinity, depth) and biological variables (chlorophyll a concentration and copepod abundance) along the Washington and Oregon coast in June 1998–2008. Copepod species were divided into four different groups based on their water‐type affinities: cold neritic, subarctic oceanic, warm neritic, and warm oceanic. Generalized linear mixed models were used to quantify the relationship between the abundance of these four different copepod groups and the abundance of juvenile salmon. The relationships between juvenile salmon and different copepod groups were further validated using regression analysis of annual mean juvenile salmon abundance versus the mean abundance of the copepod groups. Yearling Chinook salmon abundance was negatively correlated with warm oceanic copepods, warm neritic copepods, and bottom depth, and positively correlated with cold neritic copepods, subarctic copepods, and chlorophyll a concentration. The selected habitat variables explained 67% of the variation in yearling Chinook abundance. Yearling coho salmon abundance was negatively correlated with warm oceanic copepods, warm neritic copepods, and bottom depth, and positively correlated with temperature. The selected habitat variables explained 40% of the variation in yearling coho abundance. Results suggest that copepod communities can be used to characterize spatio‐temporal patterns of abundance of juvenile salmon, i.e., large‐scale interannual variations in ocean conditions (warm versus cold years) and inshore‐offshore (cross‐shelf) gradients in the abundance of juvenile salmon can be characterized by differences in the abundance of copepod species with various water mass affinities. 相似文献
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为了给大泷六线鱼(Hexagrammos otakii)在辽宁海域增殖放流的开展提供科学依据, 本研究根据春季(2020 年 5 月)、夏季(2019 年 6 月、7 月和 2020 年 8 月)和秋季(2019 年 9 月和 2020 年 10 月)共计 6 个航次的底拖网调查, 研究了辽宁海域大泷六线鱼的资源丰度及生物学特征变化, 并基于广义加性模型(GAM)分析了资源丰度的时空分布与环境因子的关系。结果表明, 大泷六线鱼资源丰度的季节变化表现为夏秋季高于春季, 资源丰度在不同站位的差异较大, 空间异质性较高。大泷六线鱼的体长、体重在 5 月至 10 月均表现为逐月增大, 肥满度也表现为夏季较春、秋季高, 且体长、体重和肥满度均具有月份间的显著差异。资源丰度的时空分布表明大泷六线鱼具有对岩礁底、砂底这两种底质生境类型和较低水温的偏好性, 黄海北部海域的资源丰度高于辽东湾海域。GAM 模型显示水深、盐度、水温、底质类型、月份和年份是影响大泷六线鱼资源丰度分布的因素, 大泷六线鱼的资源丰度与水深在 55 m 内具有显著的正相关性, 资源丰度高值区分布在底层盐度低于 31.5 和底层水温区间为 12~22.5 ℃的环境条件下, GAM 的分析结果与大泷六线鱼的生态习性相符。本研究通过上述分析初步建议具有较多岩礁、砂底质生境类型和较低水温的黄海北部海域可作为辽宁海域大泷六线鱼的适宜放流区域, 水温升高至 12 ℃以上的 5—6 月可作为适宜放流时间, 选择以上放流地点和时间, 可对放流群体的成活率及品质提供较好的保障。 相似文献
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Tatiana A. Acosta‐Pachn Mariana L. Muzquiz‐Villalobos Sofia Ortega‐García Raúl O. Martínez‐Rincn 《Fisheries Oceanography》2019,28(2):203-211
Striped marlin (Kajikia audax) is an epipelagic fish distributed in oceanic and coastal waters of the Pacific Ocean. This species is usually found in warm and coastal waters with high primary productivity. The main goal of this study was to describe the spatial segregation of striped marlin by average Eye‐Fork length (EFL) in the eastern Pacific Ocean (EPO) and its relationship with environmental variables using EFL data obtained from tuna purse‐seining and Generalized Additive Models (GAMs). The model suggested that larger individuals of striped marlin were more likely to be found in waters with high Chlorophyll‐a concentration (>2 mg/m3) and with temperatures lower than 25°C, within a region known as the “cold tongue” and the Humboldt current system, while smaller individuals were more likely to be found in warmer and low productive areas within a region known as the “warm pool of the EPO.” We observed that set type caused a large variation on average EFL of striped marlin; larger fish were captured in sets associated with floating objects (natural and manmade), while smaller fish were captured in sets associated with dolphins. Despite this, our findings suggest that striped marlin has a latitudinal gradient in average EFL; larger individuals occurred predominantly south of 10°N, while smaller ones occurred predominantly in coastal waters between 10°N and 20°N, thus demonstrating a spatial segregation of the species affected by its maturity stage. 相似文献
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西南大西洋鱿鱼钓渔船作业空间分布及其与环境的关系 总被引：1，自引：0，他引：1
为了解西南大西洋鱿鱼钓渔船捕捞作业空间分布特征及其与海洋环境的关系, 本研究基于 2018—2019 年 1—5 月的西南大西洋鱿鱼钓渔船船位数据挖掘渔船捕捞努力量信息, 统计分析了渔船作业重心变化; 结合同期的海表温度(sea surface temperature, SST)、海面高度(sea surface height, SSH)、底层温度(bottom temperature, BT)和叶绿素 a 浓度(chlorophyll-a, CHL-a) 4 种环境数据, 绘制环境因子和捕捞努力量的空间分布叠加图, 定性分析了捕捞努力量空间分布与环境因子的关系。采用广义加性模型(generalized additive model, GAM)构建捕捞努力量对海洋环境非线性表达方式, 分析海洋环境变动对西南大西洋鱿鱼钓渔船捕捞的影响。结果表明: 1—5 月研究区域渔船捕捞努力量呈现先增后减趋势, 其中 2 月捕捞努力量最高; 渔船作业重心具有明显的纬向月变化特征, 1—5 月渔船作业重心整体呈现由北向南转移。鱿鱼钓渔船主要在 SST 8~15 ℃, SSH –0.14~0.16 m, BT 5~8 ℃和 CHL-a 浓度 0.2~0.6 mg/m3 范围内作业。GAM 模型表明空间因子和环境因子对西南大西洋鱿鱼钓分布有显著影响。经度和纬度的协同作用对捕捞努力量的解释方差贡献最大, SST、SSH、CHL-a 浓度和 BT 4 个环境因子对捕捞努力量均有重要影响。 所有变量对捕捞努力量的影响作用都是非线性的, 其中 SST 影响最密切范围在 14~15 ℃, SSH 影响最密切范围是 ?0.1~0.02 m, BT 影响最密切的范围是 5~6 ℃, CHL-a 浓度影响最密切范围是 0.2~0.4 mg/m3 。 相似文献
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根据2011年、2013—2016年春季在海州湾进行的渔业资源调查数据,应用结构化加性回归(structured additive regression, STAR)模型框架,结合delta方法,根据对空间数据的不同处理方式构建了5种物种分布模型,并比较各模型对出现概率和资源量2种数据类型的拟合效果、残差空间独立性和预测性能。结果显示,加入空间项后模型拟合效果提升,残差空间自相关性显著降低,且正态模型和delta模型的提升较二项模型明显。空间加性模型(geoadditive models)的AIC值在二项模型和正态模型中均为最低,较无空间项广义可加模型(generalized additive model, GAM)分别下降7.60和144.90。模型拟合上,变系数模型(varying coefficient models, VCM)的决定系数和AUC均最高,分别为0.68和0.94。预测性能上,空间加性模型交叉验证的AUC值为(0.793±0.100)最高,均方根误差(RMSE)值为(21.65±4.83)最低,表明对小黄鱼出现概率和资源密度的估计均最准确。在最优模型的基础上,根据无... 相似文献
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根据1982—2014年33年的长江口汛期凤鲚(Coilia mystus)捕捞数据,采用多元统计分析研究凤鲚捕捞量的年际变化,利用格局转变贯序t检验(sequential t-test testing analysis of regime shifts, STARS)的方法研究捕捞量年际转变规律,并结合广义可加模型(generalized additive model,GAM)定量分析了凤鲚捕捞量与捕捞船数及环境因子长江口水温、泥沙量和径流量的相关性。结果显示,1982—2014年长江口凤鲚汛期总捕捞量和单船全汛捕捞量变幅分别为7.9～3 252.0 t·年-1和130.0～12 945.5 kg·年-1,总体都呈波动下降趋势。总捕捞量在1997年和2007年发生2次格局转变,年际格局转变指数RSI值分别为-0.580和-0.405;单船全汛捕捞量在1990年和2007年发生格局转变,RSI值分别为-0.806和-0.371。捕捞船数变幅为59～330艘·年-1,分别在1990年、2000年和2006年发生3次年际格局... 相似文献
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Dolphinfish are little known migratory fish targeted by sport, artisanal and commercial fleets. In this study, we analyzed a 10 year database of incidental catches of the tuna purse seine fleet in the Pacific Ocean off Mexico with the aim to understand the environmental determinants of the spatial distribution and seasonal migration patterns of dolphinfish. We modeled the probability of occurrence of dolphinfish as a function of spatial (geographical coordinates), temporal (month/year) and environmental variables (sea surface temperature [SST], chlorophyll [CHL] and sea surface height [SSH], inferred from satellites) using logistic Generalized Additive Models. Dolphinfish preferred waters with SST values from 23 to 28°C, low (<0.2 mg/m3) CHL values, and primarily positive SSH values. Two dolphinfish hot spots were found in the study area: one in an oceanic zone (10°–15°N, 120°–125°W), which was more defined during spring, and one on the Pacific side of the Baja California Peninsula, which became important during summer. Models suggested that dolphinfish migrated through the study area following a “corridor” that ran from the Gulf of Tehuantepec along the Equatorial Upwelling zone to the oceanic hot spot zone, which in turn connected with the hot spot off the BCP. This “migratory corridor” went around the Eastern Pacific Warm Pool, which suggested that dolphinfish avoided this high temperature‐low production zone. Dolphinfish occupied zones close to certain oceanic features, such as eddies and thermal fronts. Results suggested that the primary cause of the biological hot spots was wind‐driven upwelling, because the hot spots became more important 3–4 months after the peak in upwelling activity. 相似文献
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Pamela E. Michael Geoffrey N. Tuck Peter Strutton Alistair Hobday 《Fisheries Oceanography》2015,24(5):478-493