Differences in Responses of Chinook Salmon to Climate Shifts: Implications for Conservation

Understanding how organisms respond to climate is critical for focusing the debate about ways to recover imperiled or manage exploited species. However, efforts to understand climate effects on biota are complicated by differences among species in life history and physiology. Even within a species it is not clear if different populations will react similarly to large-scale climate trends. Climate regimes exhibit basin-wide effects similar to the El Ni no Southern Oscillation but persist for decades. In the North Pacific Ocean, two regime shifts (abrupt changes from one regime to another) occurred in 1976–1977 and 1989–1990 and had wide ranging effects on many species. We examined the response of chinook salmon from 9 evolutionary significant units (ESUs) to the regime shifts. While there was an average decline in spawner numbers associated with the regime shifts, ESUs did not respond in a uniform manner: some ESUs declined, some did not respond and one may have increased. Four ESUs currently listed under the Endangered Species Act may have declined more across regime boundaries than did the five non-listed ones. Interpretation of this result depends on two ESUs: the Snake River spring/summer run and the Central Valley fall run. The Snake River ESU had the largest decline and most sampling effort. If this ESU was excluded from the analysis, there was no evidence that listed and non-listed stocks responded differently to the regimes. The Central Valley ESU is currently a candidate for listing. If this ESU is considered to be a threatened or endangered, then listed ESUs declined more on average than did non-listed ESUs across the regime boundaries regardless of the Snake River ESU. As a whole, these results suggest that long-term climate trends are important to the dynamics of chinook populations and that sub-units of a species (here ESUs) can respond differently to these regimes.     

Laplace approximation in measurement error models

Likelihood analysis for regression models with measurement errors in explanatory variables typically involves integrals that do not have a closed-form solution. In this case, numerical methods such as Gaussian quadrature are generally employed. However, when the dimension of the integral is large, these methods become computationally demanding or even unfeasible. This paper proposes the use of the Laplace approximation to deal with measurement error problems when the likelihood function involves high-dimensional integrals. The cases considered are generalized linear models with multiple covariates measured with error and generalized linear mixed models with measurement error in the covariates. The asymptotic order of the approximation and the asymptotic properties of the Laplace-based estimator for these models are derived. The method is illustrated using simulations and real-data analysis.     

Comparison of statistical models to analyse the genetic effect on within-litter variance in pigs

Genetics affects not only the weight of piglets at birth but also the variability of birth weight within litter. Previous studies on this topic assigned the sample standard deviation of piglet birth weights within litter as an observation to the sow. However, the contribution of the difference in mean birth weight per sex on the within-litter variance has been neglected so far. This work deals with the genetic effect on within-litter variance when different statistical models with different distributional assumptions are used and considers the sex effect and appropriate weights per trait. Traits were formed from the pooled sample variance of male and female birth weights within litter. A linear model approach was fitted to the logarithmized within-litter variance and the sample standard deviation. A generalized linear model with gamma-distributed residuals and log-link function was applied to the untransformed sample variance. Models were compared by analysing data from 9439 litters from Landrace and Large White of a commercial breeding programme. The estimates of heritability for different traits ranged from 7% to 11%. Although the generalized linear mixed model is preferred from a mathematical view, the rank correlations between breeding values of the linear mixed models and the generalized linear mixed model were relatively high, i.e. 94% to 98%. By residual diagnostics, a linear mixed model using the weighted and pooled within-litter standard deviation was identified as most suitable.     

Seasonal variation of carrageenan yield,gel strength and viscosity in Sarcopeltis (ex Gigartina) skottsbergii from Southern Chile

Chile is one of the top carrageenan producers worldwide, and Sarcopeltis (ex Gigartina) skottsbergii one of the topmost exploited carrageenophytes from the wild in the world. Total yield, gel strength and viscosity from two contrasting environments Calbuco and Ancud (Inner and Outer Sea, Chile) were estimated monthly in approximately 2 years for this species. While carrageenan yields did not show differences between localities, gametophytes in spring–summer had 15% higher, compared to tetrasporophytes. Sizes (frond surface) normally did not affect carrageenan yields. Gametophytes showed clear differences in gel strength between seasons, but not between localities, with maximum peaks during winter–spring months in Calbuco and autumn-winter months in Ancud. Seasonal variations in viscosity were also significant. While gametophyte viscosity did not exceed 120 cPs, tetrasporophytes reached 1400 cPs in Calbuco and 1000 cPs in Ancud. More remarkably, a positive correlation between viscosity and gel strength was found in S. skottsbergii gametophytes, which is significantly different between both localities. These results suggest that selective harvesting in spring–summer should be preferred to optimize cost–benefit of harvesting activities and subsequent carrageenan productivity.     

Identifiability of single-index models and additive-index models

We provide a proof for the identifiability for both single-indexmodels and partially linear single-index models assuming onlythe continuity of the regression function, a condition muchweaker than the differentiability conditions assumed in theexisting literature. Our discussion is then extended to theidentifiability of the additive-index models.     

中国北方5种栎属树木多度分布及其对未来气候变化的响应

植物分布与气候之间的关系是预估未来气候变化对生态系统影响的实现基础。以往的物种分布模型通常以物种的分布区或者分布点的物种存在数据作为物种分布的响应变量。相较于物种存在数据, 多度反映了一个物种占用资源并把资源分配给个体的能力, 更能衡量物种对区域生态系统的影响。该研究通过野外调查获取了华北及周边地区1 045个样方的栎属树木多度, 利用广义线性模型、广义加性模型和随机森林模型模拟栓皮栎(Quercus variabilis)、麻栎(Q. acutissima)、槲栎(Q. aliena)、锐齿槲栎(Q. aliena var. acuteserrata)和蒙古栎(Q. mongolica) 5个树种多度的地理分布及未来2个不同时期(2050年和2070年)的潜在分布。结果表明: 随机森林模型对5个栎属树种的多度的拟合结果要优于广义线性模型和广义加性模型; 典型浓度路径(RCP) 8.5下的5个栎属树种在未来两个时期的多度变化幅度都要大于RCP 2.6下的变化, 在超过一半面积的区域中麻栎、槲栎、锐齿槲栎和蒙古栎的多度减少, 其中内蒙古东北部和黑龙江北部地区是5种栎属植物多度减少的集中分布地区。未来气候变化背景下, 需要加强对这几个区域的监测与物种保护。     

温度与昆虫生长发育关系模型的发展与应用

昆虫作为变温动物,对温度变化更为敏感。研究温度变化对昆虫生长、发育的影响有重要理论和实践意义。目前已构建了多个描述温度与昆虫增长速率的关系模型,用于解释温度对昆虫发育速率的影响。这些模型大体可分为两类:没有热动力学基础的纯描述性模型和有热动力学基础的应用性模型。本文在对现有的有关温度变化与昆虫生长发育关系的11个模型进行评述的基础上,结合作者近年来的研究,重点介绍了迄今为止国际上最为合理的、用以反映温度对昆虫发育速率影响的Sharpe-Schoolfield-Ikemoto模型,并利用这些模型拟合了一组温发育速率数据用以展示这些模型的应用。     

Association of twenty-three plasma elements with fasting serum glucose among Chinese population from four areas with different pollution level

BackgroundAssociation between fasting serum glucose (FSG) and certain mineral elements has been extensively reported. Investigation regarding multi-element exposure among subjects with different exposure level is warranted to confirm the association and further explore dose-dependent relationship.MethodsA total of 3488 participants were recruited from four counties of Hunan province, South China. Basic characteristics were collected by face to face interview and 23 elements in plasma were determined by inductively coupled plasma mass spectrometry. We applied fully adjusted generalized linear regression model and multivariable restricted cubic spline function to test the association and dose-response relationship of FSG with 23 elements.ResultsThe results indicated that FSG was positively associated with plasma⁷⁸selenium level [regression coefficient (β), 0.001; 95 % confidence interval (CI), 0.001, 0.001] in a dose-dependent manner, robust to the adjustment for suspected covariates and stratification by age, gender, BMI and smoking status. A negative association was found between FSG and plasma ²⁰⁸lead (β, -0.004; 95 % CI, -0.016, -0.002), ⁵²chromium (β, -0.002; 95 % CI, -0.004, -0.001) and ⁴⁷titanium (β, -0.001; 95 % CI, -0.002, -0.001).Conclusion⁷⁸selenium was positively while ²⁰⁸lead, ⁵²chromium and ⁴⁷titanium were negatively associated with FSG in the present study. However, prospective studies are needed to confirm the results.     

玉米出籽率全基因组关联分析

出籽率与玉米单穗产量密切相关,其遗传机制的解析对玉米高产育种具有重要意义.本研究利用309份玉米自交系为关联群体,利用固定和随机模型交替概率统一(FarmCPU)、压缩混合线性模型(CMLM)和多位点混合线性模型(MLMM)对2017年和2019年河南新乡原阳、周口郸城、海南三亚以及最佳线性无偏估计值(BLUE)的出籽...     

Evaluation of different models to describe egg and pupal development of Xyleborus fornicatus Eichh, （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka

Development data of eggs and pupae ofXyleborusfornicatus Eichh. （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka, at constant temperatures were used to evaluate a linear and seven nonlinear models for insect development. Model evaluation was based on fit to data （residual sum of squares and coefficient of determination or coefficient of nonlinear regression）, number of measurable parameters, the biological value of the fitted coefficients and accuracy in the estimation of thresholds. Of the nonlinear models, the Lactin model fitted experimental data well and along with the linear model, can be used to describe the temperature-dependent development of this species.     

A Primer on Interpreting Regression Models

Abstract: We perceive a need for more complete interpretation of regression models published in the wildlife literature to minimize the appearance of poor models and to maximize the extraction of information from good models. Accordingly, we offer this primer on interpretation of parameters in single- and multi-variable regression models. Using examples from the wildlife literature, we illustrate how to interpret linear zero-intercept, simple linear, semi-log, log-log, and polynomial models based on intercepts, coefficients, and shapes of relationships. We show how intercepts and coefficients have biological and management interpretations. We examine multiple linear regression models and show how to use the signs (+, -) of coefficients to assess the merit and meaning of a derived model. We discuss 3 methods of viewing the output of 3-dimensional models (y, x₁, x₂) in 2-dimensional space (sheet of paper) and illustrate graphical model interpretation with a 4-dimensional logistic regression model. Statistical significance or Akaike best-ness does not prevent the appearance of implausible regression models. We recommend that members of the peer review process be sensitive to full interpretation of regression models to forestall bad models and maximize information retrieval from good models