Identifying drivers of breeding success in a long‐distance migrant using structural equation modelling

In migrant animals, conditions encountered at various times and places throughout their annual cycle may affect breeding success. Yet, most studies so far have only investigated the effect of specific parts of the annual cycle, despite the importance to understand how different stages can interact and how these stages compare to intrinsic quality to properly modulate breeding success. Using a structural equation modelling approach, we investigated drivers of breeding success (migration cycle, individual quality, breeding conditions) in hoopoes Upupa epops, a long‐distant migrant. Our causal framework explained 75% of the variation in breeding success. The effect of the migration schedule was negligible, whereas the previous breeding attempt strongly influenced current breeding success. We suggest that the interplay of individual quality and environmental conditions during both previous and current breeding season may be more important drivers of breeding success than migration schedules, even in a long‐distance migrant. We conclude that structural equation modeling is a promising tool to investigate causal relationships. Applied to hoopoes, we demonstrated that current breeding success is strongly linked to previous breeding success. Complementary analysis integrating weather and climate conditions during migration and the breeding season may provide a deeper and wider overview of the annual cycle of hoopoes and additional insights into the existence of carry‐over effects in breeding success.     

A Bayesian nonparametric model for zero‐inflated outcomes: Prediction,clustering, and causal estimation

Researchers are often interested in predicting outcomes, detecting distinct subgroups of their data, or estimating causal treatment effects. Pathological data distributions that exhibit skewness and zero‐inflation complicate these tasks—requiring highly flexible, data‐adaptive modeling. In this paper, we present a multipurpose Bayesian nonparametric model for continuous, zero‐inflated outcomes that simultaneously predicts structural zeros, captures skewness, and clusters patients with similar joint data distributions. The flexibility of our approach yields predictions that capture the joint data distribution better than commonly used zero‐inflated methods. Moreover, we demonstrate that our model can be coherently incorporated into a standardization procedure for computing causal effect estimates that are robust to such data pathologies. Uncertainty at all levels of this model flow through to the causal effect estimates of interest—allowing easy point estimation, interval estimation, and posterior predictive checks verifying positivity, a required causal identification assumption. Our simulation results show point estimates to have low bias and interval estimates to have close to nominal coverage under complicated data settings. Under simpler settings, these results hold while incurring lower efficiency loss than comparator methods. We use our proposed method to analyze zero‐inflated inpatient medical costs among endometrial cancer patients receiving either chemotherapy or radiation therapy in the SEER‐Medicare database.     

Utilizing systems biology to unravel stomatal function and the hierarchies underpinning its control

Stomata control the concomitant exchange of CO₂ and transpiration in land plants. While a constant supply of CO₂ is need to maintain the rate of photosynthesis, the accompanying water losses must be tightly regulated to prevent dehydration and undesired metabolic changes. The factors affecting stomatal movement are directly coupled with the cellular networks of guard cells. Although the guard cell has been used as a model for characterization of signaling pathways, several important questions about its functioning remain elusive. Current modeling approaches describe the stomatal conductance in terms of relatively few easy‐to‐measure variables being unsuitable for in silico design of genetic manipulation strategies. Here, we argue that a system biology approach, combining modeling and high‐throughput experiments, may be used to elucidate the mechanisms underlying stomata control and to determine targets for modulation of stomatal responses to environment. In support of our opinion, we review studies demonstrating how high‐throughput approaches have provided a systems‐view of guard cells. Finally, we emphasize the opportunities and challenges of genome‐scale modeling and large‐scale data integration for in silico manipulation of guard cell functions to improve crop yields, particularly under stress conditions which are of pertinence both to climate change and water use efficiency.     

Untangling individual variation in natural populations: ecological, genetic and epigenetic correlates of long-term inequality in herbivory

Individual variation in ecologically important features of organisms is a crucial element in ecology and evolution, yet disentangling its underlying causes is difficult in natural populations. We applied a genomic scan approach using amplified fragment length polymorphism (AFLP) markers to quantify the genetic basis of long‐term individual differences in herbivory by mammals at a wild population of the violet Viola cazorlensis monitored for two decades. In addition, methylation‐sensitive amplified polymorphism (MSAP) analyses were used to investigate the association between browsing damage and epigenetic characteristics of individuals, an aspect that has been not previously explored for any wild plant. Structural equation modelling was used to identify likely causal structures linking genotypes, epigenotypes and herbivory. Individuals of V. cazorlensis differed widely in the incidence of browsing mammals over the 20‐year study period. Six AFLP markers (1.6% of total) were significantly related to herbivory, accounting altogether for 44% of population‐wide variance in herbivory levels. MSAP analyses revealed considerable epigenetic variation among individuals, and differential browsing damage was significantly related to variation in multilocus epigenotypes. In addition, variation across plants in epigenetic characteristics was related to variation in several herbivory‐related AFLP markers. Statistical comparison of alternative causal models suggested that individual differences in herbivory are the outcome of a complex causal structure where genotypes and epigenotypes are interconnected and have direct and indirect effects on herbivory. Insofar as methylation states of MSAP markers influential on herbivory are transgenerationally heritable, herbivore‐driven evolutionary changes at the study population will involve correlated changes in genotypic and epigenotypic distributions.     

Contractile injection systems of bacteriophages and related systems

Contractile tail bacteriophages, or myobacteriophages, use a sophisticated biomolecular structure to inject their genome into the bacterial host cell. This structure consists of a contractile sheath enveloping a rigid tube that is sharpened by a spike‐shaped protein complex at its tip. The spike complex forms the centerpiece of a baseplate complex that terminates the sheath and the tube. The baseplate anchors the tail to the target cell membrane with the help of fibrous proteins emanating from it and triggers contraction of the sheath. The contracting sheath drives the tube with its spiky tip through the target cell membrane. Subsequently, the bacteriophage genome is injected through the tube. The structural transformation of the bacteriophage T4 baseplate upon binding to the host cell has been recently described in near‐atomic detail. In this review we discuss structural elements and features of this mechanism that are likely to be conserved in all contractile injection systems (systems evolutionary and structurally related to contractile bacteriophage tails). These include the type VI secretion system (T6SS), which is used by bacteria to transfer effectors into other bacteria and into eukaryotic cells, and tailocins, a large family of contractile bacteriophage tail‐like compounds that includes the P. aeruginosa R‐type pyocins.     

Additional Psychometric Evidence and Construct Validity for a Revised Preferences Scale of Morningness

The Preferences Scale of morningness was originally conceived as a two‐factor, 11‐item model. More recently, a two‐factor, six‐item model has been developed and supported in two independent samples. These competing models were examined using structural equation modeling in a mixed student and working sample (n=120). The results supported the two factor, six‐item model (χ²(8, N=120)=10.84, p>0.05) as best fitting the sample data. The two factors explained 59% of the variance and Cronbach's alpha was 0.71. Significant differences (p<0.01) in self‐reported driving ability between morning and evening types were obtained by time‐of‐day, providing some evidence of construct validity. Morning types rated their performance as better in the morning hours and evening types rated their performance as better at night. We then examined whether self‐rated performance is subject to some degree of bias. For both morning and evening types, there was a tendency for those scoring high on self‐deception to rate their driving ability as better, but these differences were not significant. Overall, these findings suggest that self‐ratings are suitable for use in determining construct validity. Recommendations for future studies are made.     

Estimating linear causality in the presence of latent variables

Learning causality from data is known as the causal discovery problem, and it is an important and relatively new field. In many applications, there often exist latent variables, if such latent variables are completely ignored, which can lead to the estimation results seriously biased. In this paper, a method of combining exploratory factor analysis and path analysis (EFA-PA) is proposed to infer the causality in the presence of latent variables. Our method expands latent variables as well as their linear causal relationships with observed variables, which enhances the accuracy of causal models. Such model can be thought of as the simplest possible causal models for continuous data. The EFA-PA is very similar to that of structural equation model, but the theoretical model established by the structural equation model needs to be modified in the process of data fitting until the ideal model is established.The model gained by EFA-PA not only avoids subjectivity but also reduces estimation complexity. It is found that the EFA-PA estimation model is superior to the other models. EFA-PA can provides a basis for the correct estimation of the causal relationship between the observed variables in the presence of latent variables. The experiment shows that EFA-PA is better than the structural equation model.     

Synthesis of F16 conjugated with 5‐fluorouracil and biophysical investigation of its interaction with bovine serum albumin by a spectroscopic and molecular modeling approach

5‐Fluorouracil (5‐FU) has been widely used as a chemotherapy agent in the treatment of many types of solid tumors. Investigation of its antimetabolites led to the development of an entire class of fluorinated pyrimidines. However, the toxicity profile associated with 5‐FU is significant and includes diarrhea, mucositis, hand–foot syndrome and myelosuppression. In aiming at reducing of the side effects of 5‐FU, we have designed and synthesized delocalized lipophilic cations (DLCs) as a vehicle for the delivery of 5‐FU. DLCs accumulate selectively in the mitochondria of cancer cells because of the high mitochondrial transmembrane potential (ΔΨ_m). Many DLCs exhibited anti‐cancer efficacy and were explored as potential anti‐cancer drugs based on their selective accumulation in the mitochondria of cancer cells. F16, the DLC we used as a vehicle, is a small molecule that selectively inhibits tumor cell growth and dissipates mitochondrial membrane potential. The binding of the conjugate F16–5‐FU to bovine serum albumin (BSA) was investigated using spectroscopic and molecular modeling approaches. Fluorescence quenching constants were determined using the Stern–Volmer equation to provide a measure of the binding affinity between F16–5‐FU and BSA. The activation energy of the interaction between F16–5‐FU and BSA was calculated and the unusually high value was discussed in terms of the special structural block indicated by the molecular modeling approach. Molecular modeling showed that F16–5‐FU binds to human serum albumin in site II, which is consistent with the results of site‐competitive replacement experiments. It is suggested that hydrophobic and polar forces played important roles in the binding reaction, in accordance with the results of thermodynamic experiments. Copyright © 2012 John Wiley & Sons, Ltd.     

Inverse‐probability‐of‐treatment weighted estimation of causal parameters in the presence of error‐contaminated and time‐dependent confounders

Inverse‐probability‐of‐treatment weighted (IPTW) estimation has been widely used to consistently estimate the causal parameters in marginal structural models, with time‐dependent confounding effects adjusted for. Just like other causal inference methods, the validity of IPTW estimation typically requires the crucial condition that all variables are precisely measured. However, this condition, is often violated in practice due to various reasons. It has been well documented that ignoring measurement error often leads to biased inference results. In this paper, we consider the IPTW estimation of the causal parameters in marginal structural models in the presence of error‐contaminated and time‐dependent confounders. We explore several methods to correct for the effects of measurement error on the estimation of causal parameters. Numerical studies are reported to assess the finite sample performance of the proposed methods.     

Phylogenetic structural equation modelling reveals no need for an ‘origin’ of the leaf economics spectrum

The leaf economics spectrum (LES) is a prominent ecophysiological paradigm that describes global variation in leaf physiology across plant ecological strategies using a handful of key traits. Nearly a decade ago, Shipley et al. (2006) used structural equation modelling to explore the causal functional relationships among LES traits that give rise to their strong global covariation. They concluded that an unmeasured trait drives LES covariation, sparking efforts to identify the latent physiological trait underlying the ‘origin’ of the LES. Here, we use newly developed phylogenetic structural equation modelling approaches to reassess these conclusions using both global LES data as well as data collected across scales in the genus Helianthus. For global LES data, accounting for phylogenetic non‐independence indicates that no additional unmeasured traits are required to explain LES covariation. Across datasets in Helianthus, trait relationships are highly variable, indicating that global‐scale models may poorly describe LES covariation at non‐global scales.     

Invasive grass litter facilitates native shrubs through abiotic effects

Questions: Plant invasions are considered one of the top threats to the biodiversity of native taxa, but clearly documenting the causal links between invasions and the decline of native species remains a major challenge of invasion biology. Most studies have focused on impacts of invaders' living biomass, rather than on mechanisms mediated by litter. However, invasive plant litter, which is often of a very different type and quantity than a system's native plant litter, can have multiple important effects on ecosystem processes – such as nitrogen cycling and soil microclimate – that may influence native plants. Location: We studied effects of litter of invasive grass species that are widespread throughout western North America on native shrubs in southern California's semi‐arid habitat of coastal sage scrub. Methods: We combined a 3‐year field manipulation of non‐native litter with structural equation modeling to understand interacting effects on non‐native grasses, native shrubs, soil nitrogen (available and total), and soil moisture. Results: Litter addition facilitated non‐native grass growth, revealing a positive feedback likely to enhance invasion success. Contrary to a major paradigm of invasion biology – that competition with invasive plant species causes declines of native plants – we found that litter also facilitated growth of the native dominant shrub, a result supported by observational trends. Structural equation models indicated that enhanced soil moisture mediated the positive effects of litter on shrub growth. Conclusions: We demonstrate that invasive plants, via their litter, can facilitate dominant native plants by altering soil moisture. Our results highlight that understanding the impacts and mechanisms of plant invasions may be enhanced by considering the role of invasive plant litter on native plants and ecosystem properties.     

Dispersal-mediated selection on plant height in an autochorously dispersed herb

Dispersal ability is an important fitness component in most plant species. Therefore, some phenotypic traits can be selected due to their effect on dispersal. In this study I determine the potential for dispersal-mediated selection on plant height in an autochorous plant, Erysimum mediohispanicum (Brassicaceae). Selection was quantified by selection gradients, structural equation modeling and generalized additive models. I detected significant dispersal-mediated linear selection gradient on plant height, taller plants dispersing seeds farther. Nevertheless, the generalized additive models suggest that the selection on stalk height was non linear. Indeed, it detected a threshold in the effect of stalk height on dispersal ability; plants shorter than that threshold had an extremely short dispersal, whereas plants taller than that threshold dispersed the seeds very far. Furthermore, the structural equation modeling showed that stalk height indirectly affected dispersal distance through its significant effect on one reproduction-related fitness component, taller plants having greater fecundity. Selection on E. mediohispanicum stalk height occurs through two simultaneous paths, one via producing many seeds and the other through increasing probability of dispersing them far away.     

On the genetic basis of face cognition and its relation to fluid cognitive abilities

The oxytocin and the dopaminergic systems have turned out to be highly relevant for social abilities and cognition. Therefore, we examined the association between two functional gene polymorphisms and face cognition (FC) in a multivariate study (N = 250) by applying structural equation modeling. The catechol‐O‐methyltransferase (COMT) val158met polymorphism influences the enzyme activity of COMT, which affects the prefrontal dopamine concentration. The rs226849 is a single‐nucleotide polymorphism located in the promoter region of the oxytocin receptor (OXTR) gene, modulating the mRNA expression. By modeling a general fluid ability factor (defined by working memory and reasoning) and nested FC factors, we tested genetic contributions to FC, after controlling for variance in FC that was also associated with fluid abilities. In line with several previous studies, we found a significant association between the COMT genotype and fluid abilities (Gf) but not with FC. The association between the oxytocin polymorphism and Gf was opposite in direction for men and women. Women with the C⁺ genotype performed better on Gf tasks than those with the C^? genotype. Conversely, men with the C^? genotype performed better than those with the C⁺ genotype. There was no significant association between OXTR and the nested FC factor. Therefore, the relationship between the oxytocin polymorphism and FC can be fully accounted for by Gf. The sex specificity of this relationship is a novel finding and warrants a mechanistic explanation.     

Interpopulation Variations in Behavioral Syndromes of a Jumping Spider from Insecticide‐Treated and Insecticide‐Free Orchards

Variations in environmental conditions can influence behavioral syndromes (correlated tendencies in behaviors), and understanding the factors that shape trait covariation is particularly relevant when species are challenged by environmental changes. We investigated how behavioral syndromes varied at extremes of a gradient of anthropogenic disturbance, using apple orchards with different histories of insecticidal applications as a model system. Eris militaris (Araneae: Salticidae) jumping spiders were sampled from an insecticide‐free orchard and an insecticide‐treated orchard from Southern Québec. Spiders were tested for activity, aggression, boldness, and voracity under standardized conditions. Behavioral syndrome structure was compared between the two populations using Bayesian multiresponse models and structural equation modeling. Syndrome structure differed significantly between the two populations. The insecticide‐free population showed evidence of a syndrome involving all measured traits, while only aggression, boldness and voracity were correlated in the insecticide‐treated population. The insecticide‐free population showed negative correlations between active and voracious behavioral types vs. aggressive and bold types while the insecticide‐treated population showed a negative correlation between aggression‐boldness and voracity. This research is a first step in investigating the impact of anthropogenic disturbances on behavioral syndromes and demonstrates that behavioral syndromes may vary with respect to insecticidal applications.     

Causal modeling in a multi-omic setting: insights from GAW20

Background

Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disease phenotypes. The GAW20 Causal Modeling Working Group has applied complementary approaches (eg, Mendelian randomization, structural equations modeling, Bayesian networks) to discover novel causal effects of genomic and epigenomic variation on lipid phenotypes, as well as to validate prior findings from observational studies.

Results

Two Mendelian randomization studies have applied novel approaches to instrumental variable selection in methylation data, identifying bidirectional causal effects of CPT1A and triglycerides, as well as of RNMT and C6orf42, on high-density lipoprotein cholesterol response to fenofibrate. The CPT1A finding also emerged in a Bayesian network study. The Mendelian randomization studies have implemented both existing and novel steps to account for pleiotropic effects, which were independently detected in the GAW20 data via a structural equation modeling approach. Two studies estimated indirect effects of genomic variation (via DNA methylation and/or correlated phenotypes) on lipid outcomes of interest. Finally, a novel weighted R² measure was proposed to complement other causal inference efforts by controlling for the influence of outlying observations.

Conclusions

The GAW20 contributions illustrate the diversity of possible approaches to causal inference in the multi-omic context, highlighting the promises and assumptions of each method and the benefits of integrating both across methods and across omics layers for the most robust and comprehensive insights into disease processes.

     

The biogeography of avian extinctions on oceanic islands

Aim To test the influences of island area, island isolation, and human‐introduced mammalian predators on avian extinctions that have occurred on oceanic islands worldwide. Location The oceanic islands of the world. Methods We augmented and re‐examined an existing data set for 218 oceanic islands by means of causal modelling using path analysis (a form of structural equation modelling) and a null model. Results The number of extinctions was not a simple function of the number of bird species on the various islands. Whereas introduced mammalian predators had an influence on the number of extinctions, island area (via indirect influences) and isolation (via direct influences) were equally or more important. Main conclusions The multiple influences of physical and biotic factors on past extinctions can be revealed through modelling the causal influences of physical attributes of islands on biological characteristics, and the causal influences of both physical and biological characteristics on extinctions.     

Inferring causal structures and comparing the causal effects among calving difficulty,gestation length and calf size in Japanese Black cattle

The objectives of this study were to infer phenotypic causal networks involving gestation length (GL) and calving difficulty (CD) for the primiparity of 1850 Japanese Black heifers, and the birth weight (BWT), withers height (WH) and chest girth (CHG) of their full blood calves, and to compare the causal effects among them. The inductive causation (IC) algorithm was employed to search for causal links among these traits; it was applied to the posterior distribution of the residual (co)variance matrix of a multiple-trait sire-maternal grand sire (MGS) model. The IC algorithm implemented with 95% and 90% highest posterior density intervals detected only one structure with links between GL and BWT (WH or CHG) and between BWT (WH or CHG) and CD, although their directions were not resolved. Therefore, a possible causal structure based on the networks obtained from the IC algorithm [GL→BWT (WH or CHG)→CD] was fitted using a structural equation model to infer causal structure coefficients between the traits. The structural coefficients of GL on BWT and of BWT on GL on the observable scale showed that an extra day of GL led to a 270-g gain in BWT, and a 1-kg increase in BWT increased the risk for dystocia by 1.1%, in the causal structure. Similarly, an increase in GL by 1 day resulted in a 2.1 (2.0)-mm growth in WH (CHG), and a 1-cm increase in WH (CHG) increased the risk of dystocia by 1.2% (0.9%). The structural equation model was also fitted to alternative causal structures, which involved the addition of a directed link from GL to CD, or GL→CD to the structures described above. The inferred structural coefficients with the alternative structures were almost the same as the corresponding ones that had GL→BWT (WH or CHG)→CD. However, the direct causal effect of the extra link from GL on CD was similar to the indirect causal effect of GL through the mediating effect of BWT (WH or CHG) on CD and significant (P<0.05). This suggest that maternal genetic effects might not be removed completely from the residual variance components in the sire-MGS model, and the application of the IC algorithm to the variances from the model could detect an incorrect structure. Nonetheless, fitting the structural equation model to the causal structure provided useful information such as the magnitude of the causal effects between the traits.     

A comparative structure‐function analysis of active‐site inhibitors of Vibrio cholerae cholix toxin

Cholix toxin from Vibrio cholerae is a novel mono‐ADP‐ribosyltransferase (mART) toxin that shares structural and functional properties with Pseudomonas aeruginosa exotoxin A and Corynebacterium diphtheriae diphtheria toxin. Herein, we have used the high‐resolution X‐ray structure of full‐length cholix toxin in the apo form, NAD⁺ bound, and 10 structures of the cholix catalytic domain (C‐domain) complexed with several strong inhibitors of toxin enzyme activity (NAP, PJ34, and the P‐series) to study the binding mode of the ligands. A pharmacophore model based on the active pose of NAD⁺ was compared with the active conformation of the inhibitors, which revealed a cationic feature in the side chain of the inhibitors that may determine the active pose. Moreover, a conformational search was conducted for the missing coordinates of one of the main active‐site loops (R‐loop). The resulting structural models were used to evaluate the interaction energies and for 3D‐QSAR modeling. Implications for a rational drug design approach for mART toxins were derived. Copyright © 2015 John Wiley & Sons, Ltd.     

Disentangling the pathways of land use impacts on the functional structure of fish assemblages in Amazon streams

Agricultural land use is a primary driver of environmental impacts on streams. However, the causal processes that shape these impacts operate through multiple pathways and at several spatial scales. This complexity undermines the development of more effective management approaches, and illustrates the need for more in‐depth studies to assess the mechanisms that determine changes in stream biodiversity. Here we present results of the most comprehensive multi‐scale assessment of the biological condition of streams in the Amazon to date, examining functional responses of fish assemblages to land use. We sampled fish assemblages from two large human‐modified regions, and characterized stream conditions by physical habitat attributes and key landscape‐change variables, including density of road crossings (i.e. riverscape fragmentation), deforestation, and agricultural intensification. Fish species were functionally characterized using ecomorphological traits describing feeding, locomotion, and habitat preferences, and these traits were used to derive indices that quantitatively describe the functional structure of the assemblages. Using structural equation modeling, we disentangled multiple drivers operating at different spatial scales, identifying causal pathways that significantly affect stream condition and the structure of the fish assemblages. Deforestation at catchment and riparian network scales altered the channel morphology and the stream bottom structure, changing the functional identity of assemblages. Local deforestation reduced the functional evenness of assemblages (i.e. increased dominance of specific trait combinations) mediated by expansion of aquatic vegetation cover. Riverscape fragmentation reduced functional richness, evenness and divergence, suggesting a trend toward functional homogenization and a reduced range of ecological niches within assemblages following the loss of regional connectivity. These results underscore the often‐unrecognized importance of different land use changes, each of which can have marked effects on stream biodiversity. We draw on the relationships observed herein to suggest priorities for the improved management of stream systems in the multiple‐use landscapes that predominate in human‐modified tropical forests.