The effect of unmeasured confounders on the ability to estimate a true performance or selection gradient (and other partial regression coefficients)

Diving into broad-scale and high-resolution population genomics to decipher drivers of structure and climatic vulnerability in a marine invertebrate

Species with widespread distributions play a crucial role in our understanding of climate change impacts on population structure. In marine species, population structure is often governed by both high connectivity potential and selection across strong environmental gradients. Despite the complexity of factors influencing marine populations, studying species with broad distribution can provide valuable insights into the relative importance of these factors and the consequences of climate-induced alterations across environmental gradients. We used the northern shrimp Pandalus borealis and its wide latitudinal distribution to identify current drivers of population structure and predict the species' vulnerability to climate change. A total of 1514 individuals sampled across 24° latitude were genotyped at high geographic (54 stations) and genetic (14,331 SNPs) resolutions to assess genetic variation and environmental correlations. Four populations were identified in addition to finer substructure associated with local adaptation. Geographic patterns of neutral population structure reflected predominant oceanographic currents, while a significant proportion of the genetic variation was associated with gradients in salinity and temperature. Adaptive landscapes generated using climate projections suggest a larger genomic offset in the southern extent of the P. borealis range, where shrimp had the largest adaptive standing genetic variation. Our genomic results combined with recent observations point to further deterioration in southern regions and an impending vulnerable status in the regions at higher latitudes for P. borealis. They also provide rare insights into the drivers of population structure and climatic vulnerability of a widespread meroplanktonic species, which is crucial to understanding future challenges associated with invertebrates essential to ecosystem functioning.

The contribution of mutation and selection to multivariate quantitative genetic variance in an outbred population of Drosophila serrata

Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

Impacts of water quality on Acropora coral settlement: The relative importance of substrate quality and light

Coral larval settlement patterns are influenced by a vast array of factors; however, the relative roles of individual factors are rarely tested in isolation, leading to confusion about which are most crucial for settlement. For example, direct effects of the light environment are often cited as a major factor influencing settlement patterns, yet this has not been demonstrated under environmentally realistic lighting regimes in the absence of confounding factors. Here we apply programmable multispectral lights to create realistic light spectra, while removing correlating (but not obvious) factors that are common in laboratory settlement experiments. Using two common species of Acropora - key framework builders of the Great Barrier Reef - we find little evidence that light intensity or changes in the spectral profile play a substantial role in larval settlement under most environmentally realistic settings but can under more extreme or artificial settings. We alternatively hypothesise and provide evidence that chronic light conditions and recent sediment exposures that impact benthic substrates (e.g., crustose coralline algae) have a greater impact on settlement success. Under these conditions, there was a decrease of up to 74% settlement success. Management of water quality conditions that impact the quality of benthic-settlement substrates therefore should present a priority area of focus for improving coral recruitment.

How to quantify (the response to) sexual selection on traits

Natural selection operates via fitness components like mating success, fecundity, and longevity, which can be understood as intermediaries in the causal process linking traits to fitness. In particular, sexual selection occurs when traits influence mating or fertilization success, which, in turn, influences fitness. We show how to quantify both these steps in a single path analysis, leading to better estimates of the strength of sexual selection. Our model controls for confounding variables, such as body size or condition, when estimating the relationship between mating and reproductive success. Correspondingly, we define the Bateman gradient and the Jones index using partial rather than simple regressions, which better captures how they are commonly interpreted. The model can be applied both to purely phenotypic data and to quantitative genetic parameters estimated using information on relatedness. The phenotypic approach breaks down selection differentials into a sexually selected and a "remainder" component. The quantitative genetic approach decomposes the estimated evolutionary response to selection analogously. We apply our method to analyze sexual selection in male dusky pipefish, Syngnathus floridae, and in two simulated datasets. We highlight conceptual and statistical limitations of previous path-based approaches, which can lead to substantial misestimation of sexual selection.

Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression

Background

Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori. Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R) methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness) on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set). The original analysis of these data used a linear model (GLS) of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression.

Methods

The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS) linear models and generalized estimating equation (GEE) models. The OLS estimates were tested using O’Brien’s OLS test, Anderson’s permutation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${r}_{F}^{2}$\end{document}rF2-test, two permutation F-tests (including GlobalAncova), and a rotation z-test (Roast). The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors) of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset.

Results

GLS estimates are inconsistent between data sets, and, in each dataset, at least one coefficient is large and highly statistically significant. By contrast, effects estimated by OLS or GEE are very small, especially relative to the standard errors. Bootstrap and permutation GLS distributions suggest that the GLS results in downward biased standard errors and inflated coefficients. The Monte Carlo simulation of error rates shows highly inflated Type I error from the GLS test and slightly inflated Type I error from the GEE test. By contrast, Type I error for all OLS tests are at the nominal level. The permutation F-tests have ∼1.9X the power of the other OLS tests. This increased power comes at a cost of high sign error (∼10%) if tested on small effects.

Discussion

The apparently replicated pattern of well-being effects on gene expression is most parsimoniously explained as “correlated noise” due to the geometry of multiple regression. The GLS for fixed effects with correlated error, or any linear mixed model for estimating fixed effects in designs with many repeated measures or outcomes, should be used cautiously because of the inflated Type I and M error. By contrast, all OLS tests perform well, and the permutation F-tests have superior performance, including moderate power for very small effects.

Sexually dimorphic morphology and swimming performance relationships in wild-type zebrafish Danio rerio

This study compared prolonged swimming performance (Ucrit ) between male and female Danio rerio, and characterized how body shape was associated with this performance measure in each sex. When swimming in small (n = 6) mixed-sex groups at 28 °C, males swam, on average, over 10 cm s(-1) faster than females despite being significantly smaller. Body shape was sexually dimorphic, with males and females exhibiting small, but statistically significant differences in most aspects of body shape. Body shape explained 18 and 43% of the variation in Ucrit among males and females. In general, effects of body shape on swimming performance appeared to be sex limited, whereby different aspects of body shape affected performance in each sex, although the contribution of the distance between pelvic and anal fins to swimming performance was weakly sexually antagonistic.

Performance trade-offs and individual quality in decathletes

Many constraints of organismal design at the cell and organ level, including muscle fiber types, musculoskeletal gearing and control-surface geometry, are believed to cause performance trade-offs at the whole-organism level. Contrary to this expectation, positive correlations between diverse athletic performances are frequently found in vertebrates. Recently, it has been proposed that trade-offs between athletic performances in humans are masked by variation in individual quality and that underlying trade-offs are revealed by adjusting the correlations to 'control' quality. We argue that quality is made up of both intrinsic components, due to the causal mapping between morpho-physiological traits and performance, and extrinsic components, due to variation in training intensity, diet and pathogens. Only the extrinsic component should be controlled. We also show that previous methods to estimate 'quality-free' correlations perform poorly. We show that Wright's factor analysis recovers the correct quality-free correlation matrix and use this method to estimate quality-free correlations among the 10 events of the decathlon using a dataset of male college athletes. We found positive correlations between all decathlon events, which supports an axis that segregates 'good athletes' from 'bad athletes'. Estimates of quality-free correlations are mostly very small (<0.1), suggesting large, quality-free independence between events. Because quality must include both intrinsic and extrinsic components, the physiological significance of these adjusted correlations remains obscure. Regardless, the underlying architecture of the functional systems and the physiological explanation of both the un-adjusted and adjusted correlations remain to be discovered.

Linking macrotrends and microrates: Re-evaluating microevolutionary support for Cope's rule

Cope's rule, wherein a lineage increases in body size through time, was originally motivated by macroevolutionary patterns observed in the fossil record. More recently, some authors have argued that evidence exists for generally positive selection on individual body size in contemporary populations, providing a microevolutionary mechanism for Cope's rule. If larger body size confers individual fitness advantages as the selection estimates suggest, thereby explaining Cope's rule, then body size should increase over microevolutionary time scales. We test this corollary by assembling a large database of studies reporting changes in phenotypic body size through time in contemporary populations, as well as studies reporting average breeding values for body size through time. Trends in body size were quite variable with an absence of any general trend, and many populations trended toward smaller body sizes. Although selection estimates can be interpreted to support Cope's rule, our results suggest that actual rates of phenotypic change for body size cannot. We discuss potential reasons for this discrepancy and its implications for the understanding of Cope's rule.