Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies

The most widespread reserve selection strategy is target-based planning, as specified under the framework of systematic conservation planning. Targets are given for the representation levels of biodiversity features, and site selection algorithms are employed to either meet the targets with least cost (the minimum set formulation) or to maximize the number of targets met with a given resource (maximum coverage). Benefit functions are another recent approach to reserve selection. In the benefit function framework the objective is to maximize the value of the reserve network, however value is defined. In one benefit function formulation value is a sum over species-specific values, and species-specific value is an increasing function of representation. This benefit function approach is computationally convenient, but because it allows free tradeoffs between species, it essentially makes the assumption that species are acting as surrogates, or samples from a larger regional species pool. The Zonation algorithm is a recent computational method that produces a hierarchy of conservation priority through the landscape. This hierarchy is produced via iterative removal of selection units (cells) using the criterion of least marginal loss of conservation value to decide which cell to remove next. The first variant of Zonation, here called core-area Zonation, has a characteristic of emphasizing core-areas of all species. Here I separate the Zonation meta-algorithm from the cell removal rule, the definition of marginal loss of conservation value utilized inside the algorithm. I show how additive benefit functions and target-based planning can be implemented into the Zonation framework via the use of particular kinds of cell removal rules. The core-area, additive benefit function and targeting benefit function variants of Zonation have interesting conceptual differences in how they treat and trade off between species in the planning process.     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.     

国外生猪育种体系简析及对我国生猪育种的几点思考

选择是生猪育种的核心,而准确地选择和选种依赖于完善和成熟的育种体系。国际养猪发达国家(国际育种企业)对育种体系不断完善,长期进行生长、繁殖和肉品质等各类性状的表型测定,运用多场联合评估和基因组选择等方法不断提高遗传评估的准确性,不断挖掘新的育种性状,并进行测定、评估和选育,实现了种猪群体持续的遗传改良和综合性能的不断提升。本文对欧美发达国家(国际育种企业)的生猪育种技术体系进行了综述,以期为我国的生猪育种工作提供参考,并促进我国生猪种业的创新发展。     

快速型黄羽肉鸡饲料利用效率性状的基因组选择研究

旨在探究快速型黄羽肉鸡饲料利用效率性状的遗传参数,评估不同方法所得估计育种值的准确性。本研究以自主培育的快速型黄羽肉鸡E系1 923个个体（其中公鸡1 199只,母鸡724只）为研究素材,采用"京芯一号"鸡55K SNP芯片进行基因分型。分别利用传统最佳线性无偏预测（BLUP）、基因组最佳线性无偏预测（GBLUP）和一步法（SSGBLUP）3种方法,基于加性效应模型进行遗传参数估计,通过10倍交叉验证比较3种方法所得估计育种值的准确性。研究性状包括4个生长性状和4个饲料利用效率性状：42日龄体重（BW42D）、56日龄体重（BW56D）、日均增重（ADG）、日均采食量（ADFI）和饲料转化率（FCR）、剩余采食量（RFI）、剩余增长体重（RG）、剩余采食和增长体重（RIG）。结果显示,4个饲料利用效率性状均为低遗传力（0.08~0.20）,其他生长性状为中等偏低遗传力（0.11~0.35）;4个饲料利用效率性状间均为高度遗传相关,RFI、RIG与ADFI间为中度遗传相关,RFI与ADG间无显著相关性,RIG与ADG间为低度遗传相关。本研究在获得SSGBLUP方法的最佳基因型和系谱矩阵权重比基础上,比较8个性状的估计育种值准确性,SSGBLUP方法获得的准确性分别比传统BLUP和GBLUP方法提高3.85%~14.43%和5.21%~17.89%。综上,以RIG为选择指标能够在降低日均采食量的同时保持日均增重,比RFI更适合快速型黄羽肉鸡的选育目标;采用最佳权重比进行SSGBLUP分析,对目标性状估计育种值的预测性能最优,建议作为快速型黄羽肉鸡基因组选择方法。     

一种适用于单胎动物多品种选择的基因组关系矩阵

旨在提出一种新型基因组关系矩阵并验证其在多品种联合群体中的模拟应用效果。本研究利用QMsim软件模拟牛的表型数据和基因型数据;利用Gmatrix软件构建常规G阵;利用R语言构建新型G阵,新型G阵在常规G阵的基础上,将多品种联合群体的非哈代-温伯格平衡位点考虑在内;利用DMU软件使用“一步”法模型计算基因组估计育种值（estimated genomic breeding value,GEBV）;比较不同情况下使用两种G阵的GEBV预测准确性。结果表明,在不同遗传力及QTL数下,不对新型G阵使用A₂₂阵加权就能达到常规G阵使用A₂₂阵加权时的GEBV预测准确性。在系谱部分缺失时,新型G阵不加权较常规G阵加权时GEBV预测准确性高。证明,在系谱有部分缺失时,新型G阵对多品种GEBV的预测有一定优势。     

参考群筛选方法及规模对基因型填充准确性的影响

为探究基于A矩阵期望遗传关系最大化（maximizing the expected genetic relationship for matrix A,RELA）、基于A矩阵目标群体遗传方差最小化（minimized the target population genetic variance for matrix A,MCA）、平均亲缘关系最大化（the highest mean kinship coefficients,KIN）、随机选择（random selection,RAN）、共同祖先筛选（common ancestor,CA）等不同参考群筛选方法及参考群规模对基因型填充准确性的影响。本研究使用矮小型黄羽肉鸡作为试验群体,采用鸡600K SNP芯片（Affymetrix Axion HD genotyping array）进行基因分型,测定435羽子代公鸡45、56、70、84、91日龄体重。利用Beagle软件将低密度SNP芯片填充为高密度SNP芯片数据,比较不同参考群筛选方法、参考群规模对基因型填充准确性的影响,以及填充芯片基因组预测准确性。结果表明,使用Beagle 4.0结合系谱信息进行填充效果最佳,其次为Beagle 4.0,而Beagle 5.1填充效果最差。使用MCA方法筛选参考群进行基因型填充准确性最高,使用RAN方法筛选参考群进行基因型填充准确性最低,MCA、RELA、CA 3种方法基因型填充准确性差别较小。相比其他方法,使用MCA方法筛选个体作为参考群将低密度SNP芯片填充至高密度SNP芯片进行基因组选择的预测准确性较高,与真实高密度SNP芯片的基因组预测准确性相差甚微。随着参考群规模增大,基因型填充准确性也随之增加,但增速逐渐下降,最后趋于平缓。综上所述,可以通过参考群筛选方法构建参考群以及控制参考群规模,以保证基因型填充和基因组预测准确性并节省成本,本研究为基因型填充在畜禽遗传育种中的应用提供技术参考。     

The value of incorporating carcass trait phenotypes in terminal sire selection indexes to improve carcass weight and quality of heavy lambs

Genetic selection for carcass traits is paramount to maximize the profitability and long‐term sustainability of any meat‐producing livestock species. The main objectives of this research were to evaluate the efficiency of indicator traits for the genetic improvement of lamb carcass traits and to determine the value of including carcass traits into terminal sire selection indexes for the Canadian sheep industry. The carcass traits included hot carcass weight (HCW), fat depth at the GR site (FATGR) and average carcass conformation score (AVGCONF), and were measured on heavy lambs (slaughter age less than 365 days and HCW greater than 16.3 kg) in commercial abattoirs. Growth traits were found to be moderately efficient indicator traits for the genetic improvement of HCW but selection on ultrasound traits was necessary to substantially improve the carcass quality traits (FATGR and AVGCONF). Economic selection indexes were designed by adding various combinations of carcass traits into the Canadian Sheep Genetic Evaluation System terminal indexes. Records measured on individuals and progeny were assumed to be the sources of information for live animal and carcass traits, respectively. The changes in index accuracy, efficiency and expected correlated response were used to assess the value of their inclusion. HCW was found to have a large economic value, and its inclusion into terminal selection indexes was expected to substantially increase their accuracy (0.08–0.12 points) and efficiency (20%–30%). However, further including FATGR (measured 110 mm from the carcass midline over the 12th rib) and AVGCONF had little impact on the accuracy (≤0.03) and efficiency (1%–7%) of the proposed indexes. Thus, the inclusion of carcass traits into the existing terminal selection indexes could be beneficial for the genetic improvement of HCW, but further research is needed to determine optimal methods of increasing carcass fatness and muscularity.     

农业推广方法选择研究——以陕西省三原县为例

本文以陕西省三原县为例,采用典型调查方法,运用问卷调查、农户访问等资料搜集手段,研究各种因素对农业推广方法选择的影响.研究结果表明:推广对象的经济状况、采用创新的不同阶段以及推广对象的文化程度等都影响到农业推广方法的选择,而要最大限度地提高推广效果需要综合采用多种农业推广方法.     

溪岸蠼螋对棉铃虫的捕食能力

为明确棉田溪岸蠼螋 Labidura riparia 对棉铃虫的控害潜能, 在实验室条件下观察和测定了溪岸蠼螋不同龄期的若虫、雌雄成虫对棉铃虫幼虫、蛹和成虫的捕食能力以及其5龄若虫和雌雄成虫的捕食选择性。结果表明:不同发育阶段的溪岸蠼螋都不捕食棉铃虫完整的蛹体, 但对棉铃虫幼虫和成虫都具有较强的捕食能力。其中, 雌性溪岸蠼螋成虫的捕食能力最强, 对棉铃虫1龄幼虫日捕食量最大, 为(31.3±1.1)头。1龄溪岸蠼螋若虫也具有一定的捕食能力, 对棉铃虫1龄幼虫日捕食量为(1.7±0.7)头。不同龄期的溪岸蠼螋对相同龄期棉铃虫幼虫的捕食能力随龄期增加而增大。对1~3龄棉铃虫幼虫,溪岸蠼螋雌成虫捕食能力最强,其次是溪岸蠼螋雄成虫,再次是溪岸蠼螋5龄若虫。但是,对棉铃虫4~6龄幼虫和雌雄成虫,溪岸蠼螋雌雄成虫和5龄若虫的捕食量差异不显著。捕食选择试验表明:溪岸蠼螋5龄若虫和雌雄成虫对棉铃虫1～4龄幼虫均表现正喜好性, 对5～6龄棉铃虫幼虫和雌雄成虫表现负喜好性。     

Decision Making for Vendor Selection With AHP and Random Disposal of DEA

The vendor selection directly relatives to the procurement quality and influences the competition of the corporation in the supply chains. It is important and practical to study the vendor selection method. The measuring criteria and the method for vendor selection are analyzed. The authors put forward an new method which is combined with both the advantage of analytic hierarchy process (AHP ) and random disposal of data envelopment analysis ( DEA) for vendor selection. It introduces a random variable which solves the disadvantage of the weigh value in DEA which convert subjective estimation to objective opinion in the course of the vendor selecting. The method can improve the reliability of the vendor selection.