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.     

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。     

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。     

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。     

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。     

The effects of land use and climate on red kite distribution in the Iberian peninsula

The main breeding populations of the red kite (Milvus milvus L.), have been declining in the Iberian peninsula during the last decade. However, there is a lack of regional assessments of habitat suitability that identifies limiting ecological factors for the species and areas with conservation problems. In this work we present a regional model for the distribution and abundance of breeding red kites in the Iberian peninsula. The occurrence and estimated abundance in 100 km² UTM squares resulting from road censuses were modelled with broad-scale explanatory variables obtained from satellite imagery, thematic digital cartography, climatic data and spatial coordinates. The occurrence model incorporated mainly climatic variables and had a good discrimination ability, while the abundance model incorporated mainly land-use variables and had a lower explanatory power (r²=0.14). The predictions somewhat overestimated the results of the censuses, and this agrees with the decline of population size and range observed for this species in the Iberian peninsula. These models are relevant in the conservation of the species: first, they suggest the limiting factors for red kite in the Iberian peninsula, and, second, they generate predictive maps pointing out both areas in which conservation problems may be acute (suitable locations that are unoccupied), and areas where no data is available but the red kite is likely to be present (thus guiding further survey and research).     

Density and temperature dependence of gill net catch per unit effort for perch, Perca fluviatilis, and roach, Rutilus rutilus

Abstract  The densities of perch, Perca fluviatilis L., and roach, Rutilus rutilus (L.), were estimated in six and three lakes, respectively, using mark–recapture and ranged from 25–1064 perch ha⁻¹ and 865–2749 roach ha⁻¹. Effects of fish density, net type and water temperature on catch-per-unit-effort (CPUE) were analysed by generalised linear models (GLM) and generalised additive models (GAM). GAMs were fitted to estimate the simultaneous linear and nonlinear effects of temperature and density on CPUE. These models showed significant nonlinear effects of density on CPUE – mostly for perch, and also for both species at high densities. The models also revealed that both species had distinct density–temperature criteria of expected maximum CPUE and zero CPUE values. Because of the nonlinear relationship between CPUE and density, it was concluded that CPUE should be used with caution as a proxy of density.     

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。     

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.     

Use of individual types of fishing effort in analyzing catch and effort data by use of a generalized linear model

Fishing effort is a function of many (continuous) variables which fishers can manipulate. However, when catch and fishing effort data are analysed using a generalized linear model, individual types of fishing effort usually enter as a composite quantity. But not all quantities can be combined into a composite quantity. Use of such data this way generally leads to a loss of information and incurs a model bias. In this paper, I analyse catch and effort data for the blue swimmer crab off South Australia by a direct use of individual types of fishing effort to extract a relative index of biomass, and use the concept of homogeneous functions to present some of the results. I also give formulae for choosing a combination of different types of fishing effort to effect a specified level of catch in both absolute and relative terms. Assuming that catch follows an independent gamma, normal, negative binomial, or Poisson distribution, fitting of a generalized linear model with a log-link function to the commercial catch and effort data suggests that: (1) the exploitable biomass remained relatively constant from 1 July 1983 to 30 June 1996; (2) the relative instantaneous rate of fishing mortality of a particular sex and age (if gear selectivity was constant over time) slightly increased over time; (3) a 1% increase in the number of days fished gave about 0.85% increase in catch whereas a 1% increase in the number of people on a boat led to only about a 0.45% increase in catch. This implies that use of a composite measure of fishing effort such as boat days and man days when analysing catch and effort data is inappropriate for this fishery. Although a generalized linear model may be a reasonable first-order approximation, catch and effort data are best interpreted through a process model.