基于GAM的长江口鱼类资源时空分布及影响因素

根据2006至2017年长江口及其邻近海域鱼类资源调查,运用广义加性模型研究长江口鱼类资源密度与环境因子之间的关系,并对2017年鱼类资源密度的时空分布进行预测。结果显示,春夏秋冬四个季节最佳GAM偏差解释率分别为69.6%、55.9%、51.4%和47.4%,交叉验证回归线斜率的平均效应为0.62～0.88。盐度、水温和溶解氧是影响长江口鱼类资源密度的主要环境影响因子且在不同季节对鱼类资源密度有不同的影响机制。总体上,在春、夏、秋季,盐度与鱼类资源密度之间存在正向相关性;在夏、秋、冬季,水温对鱼类资源密度有显著影响,在秋季与鱼类资源密度之间存在正向相关性;在春、秋、冬季,溶解氧对鱼类资源密度有显著影响,在冬季与鱼类资源密度之间存在正向线性相关。研究表明,2017年夏季鱼类资源密度较高;在长江口南支的自然延伸水域存在鱼类资源密度的相对低值,在崇明岛向海自然延伸方向水域存在鱼类资源密度的相对高值。后续研究将对长江口鱼类资源进行不同生态类型区分,以期更加准确地掌握影响各生态类型鱼类时空分布的环境因素及其时空分布信息。     

北太平洋远东拟沙丁鱼渔场时空分布及其最适环境特征

基于2019—2020年北太平洋灯光敷网渔业数据和海表温度、叶绿素、海面高度等环境数据,采用空间叠加图、频次分析与经验累积分布函数、K-S检验和GAM模型4种方法分析了远东拟沙丁鱼(Sardinops sagax)渔场的单位捕捞努力量(CPUE)时空分布特征及与关键环境因子的相关关系。分析结果显示,作业渔场重心分布范围为147°~153°E、39°~43°N,在4—8月向东北方向移动,9—11月则向西南方向折返。通过频次分析与经验累积分布函数分析,中心渔场区域最适海表温度为10.0~18.0 ℃,最适叶绿素浓度为0.2~0.6 mg/m3,最适海面高度为0.2~0.7 m。K-S检验分析表明,高值CPUE海域和海表温度、叶绿素浓度、海面高度均有密切关系,最适范围分别为10.9~18.9 ℃、0.2~0.6 mg/m3、0.2~0.7 m。GAM模型模拟结果表明,高值CPUE的最适海表温度为11.0~17.0 ℃,最适叶绿素浓度为0.3~0.8 mg/m3,最适海面高度为0.1~0.4 m。综合来说,CPUE高值区海域的最适海表温度为11.0~18.0 ℃,叶绿素浓度为0.2~0.6 mg/m3,海面高度为0.2~0.7 m。     

Standardizing catch and effort data of the Taiwanese distant-water longline fishery in the western and central Pacific Ocean for bigeye tuna, Thunnus obesus

Catch per unit effort (CPUE) is often used as an index of relative abundance in fisheries stock assessments. However, the trends in nominal CPUE can be influenced by many factors in addition to stock abundance, including the choice of fishing location and target species, and environmental conditions. Consequently, catch and effort data are usually ‘standardized’ to remove the impact of such factors. Standardized CPUE for bigeye tuna, Thunnus obesus, caught by the Taiwanese distant-water longline fishery in the western and central Pacific Ocean (WCPO) for 1964–2004 were derived using three alternative approaches (GLM, GAM and the delta approach), and sensitivity was explored to whether catch-rates of yellowfin tuna and albacore tuna are included in the analyses. Year, latitude, and the catch-rate of yellowfin explained the most of the deviance (32–49%, depending on model configuration) and were identified consistently among methods, while trends in standardized catch-rate differed spatially. However, the trends in standardized catch-rates by area were found to be relatively insensitive to the approach used for standardization, including whether the catch-rates of yellowfin and albacore were included in the analyses.     

我国东、黄海鲐鱼灯光围网渔业CPUE标准化研究

日本鲐是我国近海重要的中上层鱼类资源之一,评估其资源量需要对单位捕捞努力量渔获量（CPUE）进行标准化。影响CPUE标准化的因素很多，包括季节、区域和海洋环境等。本文利用广义线型模型（GLM）和广义加性模型（GAM），结合时空、捕捞船、表温等因子，对1998-2006年东、黄海大型灯光围网渔业鲐鱼CPUE进行标准化，并评价各因子对CPUE的影响。首先应用GLM模型评价时间、空间、环境以及捕捞渔船参数对CPUE的影响，并确定显著性变量。其次，将显著性变量逐一加入GAM模型，根据Akaike信息法则（AIC），选择最优的GAM模型。最后，利用最优的GAM模型对CPUE标准化，并定量分析时间、空间、环境以及捕捞渔船参数对鲐鱼CPUE的影响。GLM模型结果表明：8个变量对CPUE有重要影响，依次为年、船队、船队与年的交互效应、月、船队与月份的交换效应、经度、纬度和海表温。根据AIC，包含上述8个显著性变量的GAM模型为最优模型，对CPUE偏差的解释为27.78%。GAM模型结果表明：高CPUE分别出现在夏季海表温为28～31 ℃的东海中部和冬季海表温为12～16 ℃的黄海；1998-2006年，标准化后的CPUE呈逐年下降趋势，与持续增长的捕捞努力量有关。     

2003-2012年秘鲁外海茎柔鱼资源丰度年间变化分析

茎柔鱼是世界上重要的经济头足类,也是我国大陆鱿钓渔业最重要的捕捞对象之一。根据中国大陆鱿钓船2003-2004年、2006-2012年9年的渔业生产统计数据和卫星遥感获得的海洋环境数据,利用广义线性模型(generalized linear model,GLM)和广义加性模型(generalized additive model,GAM)对秘鲁外海茎柔鱼的资源丰度进行CPUE标准化,分析秘鲁外海茎柔鱼资源丰度年际变化。经显著性检验,得到最终选择加入模型的自变量有年、月、纬度、经度、叶绿素浓度、年与经度交互项、年与纬度交互项、月与经度交互项、月与纬度交互项共9个自变量。GAM结果表明,模型自变量对因变量的决定系数高达42.3%。标准化后CPUE与名义CPUE变化趋势相同,年平均值略低于名义CPUE,2003-2012年秘鲁外海茎柔鱼资源丰度年间变化较大,资源丰度最高的年份为2004年,GLM标准化后的平均CPUE为7.94 t/d;资源丰度最低的年份为2007年,GLM标准化后的平均CPUE为3.28 t/d。     

基于GLM和GAM的日本鲭太平洋群体补充量与产卵场影响因子关系分析

依据日本渔业机构提供的1980—2016年日本鲭太平洋群体资源丰度(补充量和亲体量)数据,对补充量的自然对数进行正态性检验,通过正态性检验的时间为1980—1999年,再结合产卵场海洋环境数据,利用广义线性模型(GLM)和广义加性模型(GAM)对1980—1999年日本鲭太平洋群体产卵场的海表面高度(sea surface height,SSH)、海表面盐度(sea surface salinity,SSS)、海表面温度(sea surface temperature,SST)、亲体量[ln(spawning stock biomass),ln(SSB)]与补充量之间的关系进行研究。GLM模型结果显示,考虑因子的综合效应,影响程度依次为ln(SSB)×年、ln(SSB)、SSS×年、SSS对补充量的影响最显著;考虑单因子对补充量的影响,影响程度依次为产卵场SST、SSH、年份、ln(SSB)和SSS。GAM模型研究表明,基于赤池信息准则,包含年份、产卵场SST和SSH的GAM模型为最优模型,模型中各因子的影响程度由大到小依次为年份、产卵场SST、产卵场SSH;考虑单因子对补充量的影响,GAM模型中影响程度依次为年份、产卵场SSS、ln(SSB)、产卵场SST和SSH,补充量的适宜SSH范围为62～65 cm,适宜SSS范围为34.72～34.74和34.78～34.83,适宜SST范围为20.2～20.6°C。当ln(SSB)6.0时,补充量处于较高水平。     

Threshold change in forest understory vegetation as a result of selective fuelwood extraction in Nairobi, Kenya

We examined whether heavy fuelwood collection can cause threshold change in understory forest community and evaluated how selective wood extraction might lead to delayed forest recovery in an urban forest of Nairobi, Kenya. Piecewise regression which represents strongest support for threshold change provided the best fit for the relationships between understory floristic composition (i.e. DCA axis 1) and human disturbance gradients (i.e. canopy cover, and distance from the slum), where threshold changes were detected at c.a. 350 m from the slum and c.a. 30% canopy cover. Only one tree species significantly indicated communities beyond the threshold while an aggressive invasive alien plant (IAP) Lantana camara was strongly represented. Total species diversity along the two human disturbance gradients peaked before the threshold was reached, suggesting that decline in species diversity along the prevailing disturbance gradient might be able to forecast threshold change. Tree species richness in the understory rapidly declined as the threshold was surpassed while other growth forms (i.e. shrubs, herbs and climbers) were relatively unaffected. The effect of selective tree cutting was indirectly impacting the forest understory as species richness pattern of preferred and non-preferred species paralleled that of trees and shrubs, respectively. Thickets of L. camara can negatively affect indigenous flora and its establishment was favored under selective fuelwood extraction removing certain tree species while leaving the IAP untouched. Shading can readily eliminate the IAP, but weak tree regeneration beyond the threshold suggested forest recovery might be delayed for longer than expected because of the interaction between selective fuelwood use and the IAP.     

基于PCA-GAM的阿拉伯海公海鸢乌贼资源量空间分布预测模型研究

鸢乌贼是阿拉伯海重要的渔业资源之一,科学预测鸢乌贼资源量的分布有利于其资源的合理开发和利用。本研究利用2017-2019年阿拉伯海公海灯光围网鸢乌贼生产数据,结合同期的盐度、温度、混合层厚度、海表面高度异常、叶绿素a浓度、海表流速、经度、纬度数据构建了阿拉伯海鸢乌贼渔场的PCA-GAM预报模型。环境因子间的相关性会形成多重共线性,易造成模型过拟合,降低模型的预报能力。基于主成分分析（Principal Component Analysis,PCA）降维技术,将环境数据转变成少数几个不相关但保留重要信息的主成分（Principal Component,PCs）,将前8个PCs作为GAM模型的解释变量,构建模型。利用交叉验证得到预报值和实际CPUE(经过ln(CPUE+1)变换)的相关系数的均值为0.5327,回归模型斜率的均值为0.7087,截断的均值为1.4711。模型预报的鸢乌贼资源量分布和实际的CPUE(经过ln(CPUE+1)变换)在空间上重叠度较高,表明PCA-GAM模型能够较好的预报阿拉伯海鸢乌贼资源量的空间分布。     

南沙海域鸢乌贼渔场与海洋环境因子的关系

根据2013年在南沙海域4个航次的声学走航调查数据,以鸢乌贼的资源生物量为渔场指标,结合遥感获得的海表温度(sea surface temperature,SST)、海面高度(sea surface height, SSH)、海表盐度(sea surface salinity,SSS)和叶绿素a浓度(chlorophyll-a concentration,Chl.a)等海洋环境数据,采用广义可加模型(generalized additive models,GAM)建立渔场与海洋环境因子之间关系模型。研究表明:南沙海域鸢乌贼渔场分布有明显季节差异,春季主要集中于南沙西南部和越南东部海域,夏季和秋季主要分布于南沙南部海域,冬季主要分布于南沙中南部海域;各环境因子中SSH与SSS对渔场有显著影响,SST和Chl.a影响不显著;影响渔场的主要因子有SSH、SSS、经度(LON)和纬度(LAT),并依据赤池信息准则(akaike information criterion,AIC)建立渔场与影响因子的最优关系模型,模型的总偏差解释率为54.7%,进入最优模型的4个因子中,纬度对模型的影响最大;南海鸢乌贼渔场的形成除地理位置影响外,与海流洋流影响关系密切。     

Bathymetric distribution of demersal fish in the Aegean and Ionian Seas based on generalized additive modeling

Good knowledge of the spatial distribution of fish is critical to stock assessment and successful fisheries management. Depth is often the main gradient along which faunal changes occur when analyzing shelf and upper slope assemblages, and thus knowledge of the bathymetric distribution of fish species is of great importance. The depth distribution of 16 fish species (Chelidonichthys lucernus, Helicolenus dactylopterus, Hoplostethus mediterraneus, Lepidorhombus boscii, Lophius budegassa, Merlangius merlangus, Merluccius merluccius, Micromesistius poutassou, Mullus barbatus, Mullus surmuletus, Pagellus erythrinus, Peristedion cataphractum, Phycis blennoides, Serranus cabrilla, Trigla lyra, and Trisopterus minutus) of the Aegean and the Ionian Seas was evaluated by analyzing experimental bottom trawl data, using generalized additive modeling (GAM) techniques. A variety of bathymetric distribution patterns was observed. The main bathymetric zone of each species was defined based on the modeled relative density. Specifically, the lower and upper limits of the main bathymetric zone of each species were defined as the depths where the estimated relative density of the species becomes less than 1% of the maximum. This definition is proposed as a better and more informative alternative than reporting the minimum and maximum depths of encounter.