Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables

Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario‐based modelling approach coupled with Bayesian model averaging to build predictive models for absolute values in mixed‐species woody plantings and differences from their adjacent pasture, for litter stocks, soil C stocks and soil C:N ratios. Modelling scenarios of increasing data availability and effort were tested. These included variables that could be derived without a site visit (e.g. location, climate and management) that were sampled in the adjacent pasture (e.g. soil C and nutrients) or were sampled in the environmental planting (e.g. vegetation, litter properties, soil C and nutrients). The predictive power of models varied considerably among C variables (litter stocks, soil C stocks and soil C:N ratios in tree plantings and their differences to their adjacent pastures) and the model scenarios used. The use of a sampling scenario‐based approach to building predictive models shows promise for monitoring changes in tree plantings, following reforestation. The approach could also be readily adapted to other contexts where sampling effort for predictor variables in models is a major potential limitation to model utilization. This study demonstrates the benefit of exploring scenarios of data availability during modelling and will be especially valuable where the sampling effort differs greatly among variables. Copyright © 2016 John Wiley & Sons, Ltd.     

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。     

夏南牛核心群母牛部分生长性状影响因素的分析与研究

利用SAS统计分析软件中的GLM过程分析了不同出生年度、不同出生季节和不同代次对夏南牛体尺性状的影响。结果表明:出生年度和代次对夏南牛各年龄阶段体尺性状的影响显著(P<0.05);出生季节对夏南牛各年龄阶段体尺性状的影响不显著(P>0.05)。     

PROC MIXED在多误差试验分析中的应用

以条区试验设计为例,讨论了多误差试验数据分析的特点,以及利用统计软件SAS中传统程序PROCANOVA和PROCGLM对其进行分析的局限性,介绍和讨论了PROCMIXED对试验数据进行统计分析的原理、特点和应用效果,在此基础上提出了利用PROCMIXED对多误差试验数据进行分析的建议。     

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。     

Catch per unit effort standardization of the eastern Bering Sea walleye pollock (Theragra chalcogramma) fleet

A general linear model (GLM) was used to standardize catch per unit effort (CPUE) data for Alaska walleye pollock (Theragra chalcogramma) from the Bering Sea fleet for the years 1995–1999. Data were stratified temporally by year and season and spatially by area using either Alaska Department of Fish and Game (ADF&G) or National Marine Fisheries Service (NMFS) reporting areas. Four factors were used: vessel identification (ID) number, vessel speed, percentage of pollock by weight in the haul (a measure of targeting), and whether most of the haul took place before or after sunset. At least 29 combinations of main effects, quadratic covariates, and interactions were tested for each year/area/season stratum. GLM models explained from 31 to 48% of the total sums of squares. Vessel identification number was included in all models and explained the most variability. Of the remaining factors, the square of the percentage of pollock in the haul was included in most models, following an F-test to determine parsimony. Analysis of the vessel identification number coefficients indicated that larger vessels tended to have higher CPUEs; and that this relationship differed between dedicated catcher vessels and offshore catcher processors. Coefficient estimates and response surfaces generally indicated increased CPUEs with the percentage of pollock in the haul and showed mixed results with vessel speed. The vessel identification number incorporated most vessel characteristics, leaving vessel speed primarily as a fitting variable with less biological meaning. The year/area/season stratification procedure was found to be necessary due to the unbalanced design, which otherwise would have factor levels with no data in a large combined model. In addition, the stratification procedure reduced the variability in CPUE substantially.     

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。     

Effects of environmental factors on species richness patterns of herb layer in Eastern Zhongtiao Mountain

The species richness of herb layer was investigated among 43 plots of forest vegetation in the eastern Zhongtiao Mountain, in southern Shanxi Province, China. The forest vegetation was divided into two major vegetation types such as the deciduous forest and the coniferous forest by the two-way indicator species analysis （TWINSPAN）. The species richness of herb layer was fitted in the topographic and soil feature factors, as well as the topographic relative moisture index （TRMI） by the generalized linear models （GLM）. The results showed that canopy cover and altitude were the most significant environmental factors. Soil pH value and soil nutrients index such as total N, organic matter content had no significant influence. The effect of environment factors on species richness of herb layer had significant difference in vegetation types. For the broad-leaved forest, litter depth and TRMI were the important environment factors. For the coniferous forest, soil clay content was another important environment factor. The range of environmental gradient such as altitude may contribute to the difference.     

印度洋中国大眼金枪鱼延绳钓渔业CPUE标准化

使用广义线性模型(GLM)和广义可加模型(GAM)对印度洋中国大眼金枪鱼渔业的单位捕捞努力量渔获量(CPUE)进行标准化。在CPUE标准化模型中,考虑了空间(经度与纬度)、时间(年与月)和环境(包括各深度温度、各深度盐度和海平面高度)等变量。结果表明,标准化CPUE和名义CPUE在时空分布上呈相似的趋势。年CPUE随时间呈现下降的趋势,高CPUE经常出现在42°E～60°E、85°E～90°E、15°S～5°S和10°N～15°N的区域内。GLM和GAM分析都显示出经度是影响CPUE最重要的变量,可分别解释17.3%和23.81%的变异;纬度、经度和纬度的交互效应、年份、381 m水层温度、317 m水层温度对CPUE的影响也是明显的。此研究中GLM模型比GAM模型更合适。