Aligning agri-environmental subsidies and environmental needs: a comparative analysis between the US and EU

Summary 1. Biotic homogenization (BH), a dominant process shaping the response of natural communities to human disturbance, reflects both the expansion of exotic species at large scales and other mechanisms that often operate at smaller scales. 2. Here, we examined the relationship between BH in plant communities and spatio-temporal landscape disturbance (habitat fragmentation and surrounding habitat conversion) at a local scale (1 km²), using data from a standardized monitoring programme in France. We quantified BH using both a spatial partitioning of taxonomic diversity and the average habitat specialization of communities, which informs on functional BH. 3. We observed a positive relationship between local taxonomic diversity and landscape fragmentation or instability. This increase in local taxonomic diversity was, however, paralleled by a decrease in average community specialization in more fragmented landscapes and in more unstable landscapes around forest sites. The decrease in average community specialization suggests that landscape disturbance causes functional BH, but there was limited evidence for concurrent taxonomic BH. 4. Synthesis. Our results show that landscape disturbance is partly responsible for functional BH at small scales via the extirpation of specialist species, with possible consequences for ecosystem functioning. However, this change in community composition is not systematically associated with taxonomic BH. This has direct relevance in designing biodiversity indicators: metrics incorporating species sensitivity to disturbance (such as species specialization to habitat) appear much more reliable than taxonomic diversity for documenting the response of communities to disturbance.

Comparison of mixed modeling regression methods for the assessment of longitudinal CyTOF® data.

Population structure is known to cause false-positive detection in association studies. We compared the power, precision, and type-I error rates of various association models in analyses of a simulated dataset with structure at the population (admixture from two populations; P) and family (K) levels. We also compared type-I error rates among models in analyses of publicly available human and dog datasets. The models corrected for none, one, or both structure levels. Correction for K was performed with linear mixed models incorporating familial relationships estimated from pedigrees or genetic markers. Linear models that ignored K were also tested. Correction for P was performed using principal component or structured association analysis. In analyses of simulated and real data, linear mixed models that corrected for K were able to control for type-I error, regardless of whether they also corrected for P. In contrast, correction for P alone in linear models was insufficient. The power and precision of linear mixed models with and without correction for P were similar. Furthermore, power, precision, and type-I error rate were comparable in linear mixed models incorporating pedigree and genomic relationships. In summary, in association studies using samples with both P and K, ancestries estimated using principal components or structured assignment were not sufficient to correct type-I errors. In such cases type-I errors may be controlled by use of linear mixed models with relationships derived from either pedigree or from genetic markers.

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population stratification and relatedness through inclusion of random effects for a genetic relationship matrix. However, the utility of linear (mixed) models in the context of meta-analysis of GWAS of binary phenotypes has not been previously explored. In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, considering designs that incorporate variable case–control imbalance, confounding factors and population stratification. Our results demonstrate that linear models can be used for meta-analysis of GWAS of binary phenotypes, without loss of power, even in the presence of extreme case–control imbalance, provided that one of the following schemes is used: (i) effective sample size weighting of Z-scores or (ii) inverse-variance weighting of allelic effect sizes after conversion onto the log-odds scale. Our conclusions thus provide essential recommendations for the development of robust protocols for meta-analysis of binary phenotypes with linear models.

Creating and restoring patches of noncrop early-succession vegetation within agricultural landscapes may mitigate grassland bird population declines caused by agricultural land use and intensification. Achieving this goal requires an ability to balance avian benefits with agronomics, which may be facilitated by understanding how bird communities respond to various conservation practices. We evaluated bird richness, abundance, Shannon diversity, and Total Avian Conservation Value in 20 replicates of four Conservation Reserve Program practices in an intensive rowcrop agricultural landscape in the Mississippi Alluvial Valley from May–Jul., 2005–2007. Conservation practices included: (1) large blocks of structurally-diverse early-succession vegetation (6–8 y old trees) and three buffer types; (2) 30 m wide monotypic filter strips with tall dense switchgrass (Panicum virgatum); (3) 30 m wide diverse filter strips with a forb-native warm season grass mixture; and (4) 60 m wide early-succession riparian forest buffers (1–3 y old trees). The breeding bird community was dominated by red-winged blackbirds (Agelaius phoeniceus; 43% of total) and dickcissels (Spiza americana; 42% of total) but commonly included eastern meadowlarks (Sturnella magna), indigo buntings (Passerina cyanea), mourning doves (Zenaida macroura), and northern bobwhite (Colinus virginianus). We observed ≥1.8 × more dickcissels in large blocks and diverse filter strips than other buffers and greater Shannon diversity in large blocks than any buffers (P < 0.05). Diverse filter strips had ≥1.6 × greater overall bird density (7.2 birds/0.6 ha), on average, than all other practices. Based on these data, we conclude that buffers are attractive to farmland breeding birds and may provide important ecological benefits to supplement a conservation management system founded on large blocks of early-succession vegetation.

This study explores how aggregate payments received from two different U.S. agricultural conservation programs—the Environmental Quality Incentives Program (EQIP) and the Conservation Stewardship Program (CSP)—influence county‐level cover crop adoption rates in the Corn Belt. We utilize 2006–2015 county‐level panel data with information on cover crop adoption rates and per acre conservation program payments to achieve the study objective. Cover crop adoption information is collected from a unique satellite‐based data set of soil health practices. Linear fixed effect models, fractional regression models, and a moment‐based instrumental variables model are used in the empirical analysis. Our estimation results suggest that EQIP payments have a statistically significant positive effect on cover crop adoption at the county level. In contrast, we find statistical evidence that CSP payments reduce the county‐level proportion of acres planted to cover crops. These opposing effects indicate that it is possible for conservation payment programs to have differing aggregate effects on adoption rates of specific conservation practices (e.g., cover crops in this case). Moreover, these results imply that not all conservation programs “are created equal” and differences in policy designs and focus areas may induce diverging effects in the uptake of particular conservation practices.

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r = 0.71–0.88, RMSE: 1.11–1.61 METs; p > 0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN—correlations: r = 0.89, RMSE: 1.07–1.08 METs. Linear models—correlations: r = 0.88, RMSE: 1.10–1.11 METs; p > 0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN—correlation: r = 0.88, RMSE: 1.12 METs. Linear models—correlations: r = 0.86, RMSE: 1.18–1.19 METs; p < 0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN—correlations: r = 0.82–0.84, RMSE: 1.26–1.32 METs. Linear models—correlations: r = 0.71–0.73, RMSE: 1.55–1.61 METs; p < 0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

Lambing interval is a relevant reproductive indicator for sheep populations under continuous mating systems, although there is a shortage of selection programs accounting for this trait in the sheep industry. Both the historical assumption of small genetic background and its unorthodox distribution pattern have limited its implementation as a breeding objective. In this manuscript, statistical performances of 3 alternative parametrizations [i.e., symmetric Gaussian mixed linear (GML) model, skew-Gaussian mixed linear (SGML) model, and piecewise Weibull proportional hazard (PWPH) model] have been compared to elucidate the preferred methodology to handle lambing interval data. More specifically, flock-by-flock analyses were performed on 31,986 lambing interval records (257.3 ± 0.2 d) from 6 purebred Ripollesa flocks. Model performances were compared in terms of deviance information criterion (DIC) and Bayes factor (BF). For all flocks, PWPH models were clearly preferred; they generated a reduction of 1,900 or more DIC units and provided BF estimates larger than 100 (i.e., PWPH models against linear models). These differences were reduced when comparing PWPH models with different number of change points for the baseline hazard function. In 4 flocks, only 2 change points were required to minimize the DIC, whereas 4 and 6 change points were needed for the 2 remaining flocks. These differences demonstrated a remarkable degree of heterogeneity across sheep flocks that must be properly accounted for in genetic evaluation models to avoid statistical biases and suboptimal genetic trends. Within this context, all 6 Ripollesa flocks revealed substantial genetic background for lambing interval with heritabilities ranging between 0.13 and 0.19. This study provides the first evidence of the suitability of PWPH models for lambing interval analysis, clearly discarding previous parametrizations focused on mixed linear models.

This study aims to evaluate linear models and linear mixed models that were built based on the database of effect Antimicrobial Peptide (AMP) on broiler performance. The method of this research consists of compiling a database, making models, and evaluating models. The database compiled from a comparative study related to the effect of AMP on body weight, average daily gain, daily feed intake, and the feed conversion ratio from broilers reared at 1-21 days. The model consists of five Linear Models (LM) and five Linear Mixed Models (LMM). The best model determined by Bayesian Information Criteria (BIC) and then the correlation value calculated according to actual vs. predicted value. Based on the lowest BIC value, the representative model for body weight, average body weight gain, and average feed consumption are model 9. Besides that, the feed conversion ratio relates to model 2. Model 9 is a linear mixed model with levels as fixed effects and random effects consisting of studies, broiler breeds, type of peptides, and treatment methods. In conclusion, LMM is better than LM in predicting complex problems such as comparative studies of the effects AMP on broiler performance.

A genome-wide association study for susceptibility and infectivity of Holstein Friesian dairy cattle to digital dermatitis

In Chapters 2 and 3, we reviewed linear regression and additive modelling techniques. In Chapters 4–7, we showed how to extend these methods to allow for heterogeneity, nested data, and temporal or spatial correlation structures. The resulting methods were called linear mixed modelling and additive mixed modelling (see the left hand pathway of Fig. 13.1). In Chapter 9, we introduced generalised linear modelling (GLM) and generalised additive modelling (GAM), and applied them to absence–presence data, proportional data, and count data. We used the Bernoulli and binomial distributions for 0–1 data (the 0 stands for absence and the 1 for presence), and proportional data (Y successes out of n independent trials), and we used the Poisson distribution for count data. However, one of the underlying assumptions of theses approaches (GLM and GAM) is that the data are independent, which is not always the case. In this chapter, we take this into account and extend the GLM and GAM models to allow for correlation between the observations, and nested data structures. It should come as no surprise that these methods are called generalised linear mixed modelling (GLMM) and generalised additive mixed modelling (GAMM); see the right hand pathway of Fig. 13.1.KeywordsGeneralise Linear ModellingGeneralise Linear Mixed ModellingGeneralise Additive ModellingGeneralise Linear Modelling ModelResidual Standard ErrorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the crisis in fuel consumption, it is necessary to save fuel consumption by selecting fuel that is quite efficient. The aim of the research is to examine the fuel consumption patterns of vehicles in an area that experiences various weathers so as to determine the level of efficiency of fuel use. both from the factor of fuel type, distance, speed, temperature, to the weather. testing was carried out using the linear model technique, the Linear Mixed effect model, and the Anova mathematical model. The results of the analysis if the different types of fuel have a very large significant effect in influencing the fuel consumption of a vehicle. With all the approaches analyzed from the linear mix model, analysis of variance and linear mix random effect model, the results show that other independent variables such as average speed, distance traveled and ambient temperature do not have much effect on fuel consumption. Based on modeling results that has been done, it can be identified if fuel consumption is strongly influenced by the type of fuel used by the vehicle. Meanwhile, other variables such as vehicle mileage, average speed, and ambient temperature at the time of data collection did not show any significant effect on fuel consumption.

Pharmacokinetic-Pharmacodynamic Modeling and Simulation

In this thesis, we suggest and explore semi-parametric generalized partially linear mixed models for longitudinal data with non-ignorable and non-monotone missing responses. The key subject of our attention is the estimation of mean response parameters and variance components using a semi-parametric Monte Carlo EM method, where the conditional mean response is semi-parametric. We first discuss the penalized regression spline method, which is often referred to as P-splines, for linear mixed model. We investigate the connection between P-splines and linear mixed model through incorporating the non-parametric mean functions into longitudinal linear mixed model. An extensive simulation study using different semi-parametric mean response functions are presented. Our simulation study reports that when the true underlying model is partially linear, the penalized spline method provides unbiased and efficient estimators. On the other hand, when the mean response is a correctly specified linear model, the P-spline still provides reliable estimates of the model parameters. Next, we present semi-parametric generalized partially linear mixed models for longitudinal data with non-ignorable missing responses. In this situation, we introduce a parametric model for non-ignorable missing data and incorporate it into the likelihood function. We obtain the asymptotic variances of the proposed estimators by the method of Louis (cf. [2], [7]). In addition, we propose and explore a semi-parametric Monte Carlo EM (MCEM) algorithm for simultaneous estimation of the regression parameters and variance components in partially linear mixed models with non-ignorable and non-monotone missing responses. In simulations, the empirical properties of the proposed method are evaluated. The simulation study shows that our proposed semi-parametric method performs well even under a large proportion of non-ignorable missing responses. Finally, the proposed semi-parametric MCEM method are applied to some actual longitudinal data obtained from a health survey, referred to as the Health and Retirement Study (HRS). The data showed strong evidence of a non-linear trend in the mean response function. It is evident from this application that our proposed methods can be used to improve the efficiency of the estimates in a partially linear mixed model for longitudinal data with non-ignorable missing responses.

Privileged by rapid increase in available epigenomic data, epigenome‐wide association studies (EWAS) are to make a profound contribution to understand the molecular mechanism of DNA methylation in cognitive aging. Current statistical methods used in EWAS are dominated by models based on multiple assumptions, for example, linear relationship between molecular profiles and phenotype, normal distribution for the methylation data and phenotype. In this study, we applied an assumption‐free method, the generalized correlation coefficient (GCC), and compare it to linear models, namely the linear mixed model and kinship model. We use DNA methylation associated with a cognitive score in 400 and 206 twins as discovery and replication samples respectively. DNA methylation associated with cognitive function using GCC, linear mixed model, and kinship model, identified 65 CpGs (p < 1e‐04) from discovery sample displaying both nonlinear and linear correlations. Replication analysis successfully replicated 9 of these top CpGs. When combining results of GCC and linear models to cover diverse patterns of relationships, we identified genes like KLHDC4, PAPSS2, and MRPS18B as well as pathways including focal adhesion, axon guidance, and some neurological signaling. Genomic region‐based analysis found 15 methylated regions harboring 11 genes, with three verified in gene expression analysis, also the 11 genes were related to top functional clusters including neurohypophyseal hormone and maternal aggressive behaviors. The GCC approach detects valuable methylation sites missed by traditional linear models. A combination of methylation markers from GCC and linear models enriched biological pathways sensible in neurological function that could implicate cognitive performance and cognitive aging.