Having recognized that variation around the population-level "Golden Mean" of labile traits contains biologically meaningful information, behavioural ecologists have focused increasingly on exploring ...the causes and consequences of individual variation in behaviour. These are exciting new directions for the field, assisted in no small part by the adoption of mixed-effects modelling techniques that enable the partitioning of among- and within-individual behavioural variation. It has become commonplace to extract predictions of individual random effects from such models for use in subsequent analyses (for example, between a personality trait and other individual traits such as cognition, physiology, or fitness-related measures). However, these predictions are made with large amounts of error that is not carried forward, rendering further tests susceptible to spurious
values from these individual-level point estimates. We briefly summarize the problems with such statistical methods that are used regularly by behavioural ecologists, and highlight the robust solutions that exist within the mixed model framework, providing tutorials to aid in their implementation.
In studies of population ecology, demography and life history evolution, among-individual differences in traits associated with survival and reproduction are often attributed to variation in ...‘individual quality’. However, often intuitive quality is rarely defined explicitly, and we argue that this can result in ambiguity about what quality actually is. Here we consider the various ways in which the concept of quality is currently applied, and show that subtle differences in intended meaning have very important consequences when the goal is to draw evolutionary inferences. We also propose a novel approach that is consistent with all current ecological uses, but also allows the concept of quality to be integrated with existing evolutionary theory.
Several studies over recent decades have reported a lack of contemporary improvement in thoroughbred racehorse speed, despite apparent additive genetic variance and putatively strong selection. More ...recently, it has been shown that some phenotypic improvement is ongoing, but rates are low in general and particularly so over longer distances. Here we used pedigree-based analysis of 692,534 records from 76,960 animals to determine whether these phenotypic trends are underpinned by genetic selection responses, and to evaluate the potential for more rapid improvement. We show that thoroughbred speed in Great Britain is only weakly heritable across sprint (h
= 0.124), middle-distance (h
= 0.122) and long-distance races (h
= 0.074), but that mean predicted breeding values are nonetheless increasing across cohorts born between 1995 and 2012 (and racing from 1997 to 2014). For all three race distance categories, estimated rates of genetic improvement are statistically significant and also greater than can be explained by drift. Taken together our results show genetic improvement for thoroughbred speed is ongoing but slow, likely due to a combination of long generation times and low heritabilities. Additionally, estimates of realised selection intensities raises the possibility that the contemporary selection emerging from the collective actions of horse breeders is weaker than previously assumed, particularly over long distances. We suggest that unmodelled common environment effects may have upwardly biased estimates of heritability, and thus expected selection response, previously.
Individual repeatability (R), defined as the proportion of observed variance attributable to among‐individual differences, is a widely used summary statistic in evolutionarily motivated studies of ...morphology, life history, physiology and, especially, behaviour. Although statistical methods to estimate R are well known and widely available, there is a growing tendency for researchers to interpret R in ways that are subtly, but importantly, different. Some view R as a property of a dataset and a statistic to be interpreted agnostically with respect to mechanism. Others wish to isolate the contributions of ‘intrinsic’ and/or ‘permanent’ individual differences, and draw a distinction between true (intrinsic) and pseudo‐repeatability arising from uncontrolled extrinsic effects. This latter view proposes a narrower, more mechanistic interpretation, than the traditional concept of repeatability, but perhaps one that allows stronger evolutionary inference as a consequence (provided analytical pitfalls are successfully avoided). Neither perspective is incorrect, but if we are to avoid confusion and fruitless debate, there is a need for researchers to recognise this dichotomy, and to ensure clarity in relation to how, and why, a particular estimate of R is appropriate in any case.
Abstract Gut microbiomes are widely hypothesised to influence host fitness and have been experimentally shown to affect host health and phenotypes under laboratory conditions. However, the extent to ...which they do so in free-living animal populations and the proximate mechanisms involved remain open questions. In this study, using long-term, individual-based life history and shallow shotgun metagenomic sequencing data (2394 fecal samples from 794 individuals collected between 2013–2019), we quantify relationships between gut microbiome variation and survival in a feral population of horses under natural food limitation (Sable Island, Canada), and test metagenome-derived predictions using short-chain fatty acid data. We report detailed evidence that variation in the gut microbiome is associated with a host fitness proxy in nature and outline hypotheses of pathogenesis and methanogenesis as key causal mechanisms which may underlie such patterns in feral horses, and perhaps, wild herbivores more generally.
The evolution of flexible parenting Royle, Nick J.; Russell, Andrew F.; Wilson, Alastair J.
Science,
08/2014, Letnik:
345, Številka:
6198
Journal Article
Recenzirano
Odprti dostop
Parenting behaviors, such as the provisioning of food by parents to offspring, are known to be highly responsive to changes in environment. However, we currently know little about how such ...flexibility affects the ways in which parenting is adapted and evolves in response to environmental variation. This is because few studies quantify how individuals vary in their response to changing environments, especially social environments created by other individuals with which parents interact. Social environmental factors differ from nonsocial factors, such as food availability, because parents and offspring both contribute and respond to the social environment they experience. This interdependence leads to the coevolution of flexible behaviors involved in parenting, which could, paradoxically, constrain the ability of individuals to rapidly adapt to changes in their nonsocial environment.
The independent evolution of the sexes may often be constrained if male and female homologous traits share a similar genetic architecture. Thus, cross-sex genetic covariance is assumed to play a key ...role in the evolution of sexual dimorphism (SD) with consequent impacts on sexual selection, population dynamics, and speciation processes. We compiled cross-sex genetic correlations (rMF) estimates from 114 sources to assess the extent to which the evolution of SD is typically constrained and test several specific hypotheses. First, we tested if rMF differed among trait types and especially between fitness components and other traits. We also tested the theoretical prediction of a negative relationship between rMF and SD based on the expectation that increases in SD should be facilitated by sex-specific genetic variance. We show that rMF is usually large and positive but that it is typically smaller for fitness components. This demonstrates that the evolution of SD is typically genetically constrained and that sex-specific selection coefficients may often be opposite in sign due to sub-optimal levels of SD. Most importantly, we confirm that sex-specific genetic variance is an important contributor to the evolution of SD by validating the prediction of a negative correlation between rMF and SD.
In many cooperative societies, including our own, helpers assist with the post-natal care of breeders’ young and may thereby benefit the post-natal development of offspring. Here, we present evidence ...of a novel mechanism by which such post-natal helping could also have beneficial effects on pre-natal development: By lightening post-natal maternal workloads, helpers may allow mothers to increase their pre-natal investment per offspring. We present the findings of a decade-long study of cooperatively breeding white-browed sparrow-weaver,
Plocepasser mahali
, societies. Within each social group, reproduction is monopolized by a dominant breeding pair, and non-breeding helpers assist with nestling feeding. Using a within-mother reaction norm approach to formally identify maternal plasticity, we demonstrate that when mothers have more female helpers, they decrease their own post-natal investment per offspring (feed their nestlings at lower rates) but increase their pre-natal investment per offspring (lay larger eggs, which yield heavier hatchlings). That these plastic maternal responses are predicted by female helper number, and not male helper number, implicates the availability of post-natal helping per se as the likely driver (rather than correlated effects of group size), because female helpers feed nestlings at substantially higher rates than males. We term this novel maternal strategy “maternal front-loading” and hypothesize that the expected availability of post-natal help either allows or incentivizes helped mothers to focus maternal investment on the pre-natal phase, to which helpers cannot contribute directly. The potential for post-natal helping to promote pre-natal development further complicates attempts to identify and quantify the fitness consequences of helping.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
ABSTRACT
While it is universally recognised that environmental factors can cause phenotypic trait variation via phenotypic plasticity, the extent to which causal processes operate in the reverse ...direction has received less consideration. In fact individuals are often active agents in determining the environments, and hence the selective regimes, they experience. There are several important mechanisms by which this can occur, including habitat selection and niche construction, that are expected to result in phenotype–environment correlations (i.e. non‐random assortment of phenotypes across heterogeneous environments). Here we highlight an additional mechanism – intraspecific competition for preferred environments – that may be widespread, and has implications for phenotypic evolution that are currently underappreciated. Under this mechanism, variation among individuals in traits determining their competitive ability leads to phenotype–environment correlation; more competitive phenotypes are able to acquire better patches. Based on a concise review of the empirical evidence we argue that competition‐induced phenotype–environment correlations are likely to be common in natural populations before highlighting the major implications of this for studies of natural selection and microevolution. We focus particularly on two central issues. First, competition‐induced phenotype–environment correlation leads to the expectation that positive feedback loops will amplify phenotypic and fitness variation among competing individuals. As a result of being able to acquire a better environment, winners gain more resources and even better phenotypes – at the expense of losers. The distinction between individual quality and environmental quality that is commonly made by researchers in evolutionary ecology thus becomes untenable. Second, if differences among individuals in competitive ability are underpinned by heritable traits, competition results in both genotype–environment correlations and an expectation of indirect genetic effects (IGEs) on resource‐dependent life‐history traits. Theory tells us that these IGEs will act as (partial) constraints, reducing the amount of genetic variance available to facilitate evolutionary adaptation. Failure to recognise this will lead to systematic overestimation of the adaptive potential of populations. To understand the importance of these issues for ecological and evolutionary processes in natural populations we therefore need to identify and quantify competition‐induced phenotype–environment correlations in our study systems. We conclude that both fundamental and applied research will benefit from an improved understanding of when and how social competition causes non‐random distribution of phenotypes, and genotypes, across heterogeneous environments.
ecologist's guide to the animal model Wilson, Alastair J.; Réale, Denis; Clements, Michelle N. ...
The Journal of animal ecology,
January 2010, Letnik:
79, Številka:
1
Journal Article
Recenzirano
1. Efforts to understand the links between evolutionary and ecological dynamics hinge on our ability to measure and understand how genes influence phenotypes, fitness and population dynamics. ...Quantitative genetics provides a range of theoretical and empirical tools with which to achieve this when the relatedness between individuals within a population is known. 2. A number of recent studies have used a type of mixed-effects model, known as the animal model, to estimate the genetic component of phenotypic variation using data collected in the field. Here, we provide a practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research. 3. We begin by outlining, in simple terms, key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations. 4. We then provide three detailed example tutorials, for implementation in a variety of software packages, for some basic applications of the animal model. We discuss several important statistical issues relating to best practice when fitting different kinds of mixed models. 5. We conclude by briefly summarizing more complex applications of the animal model, and by highlighting key pitfalls and dangers for the researcher wanting to begin using quantitative genetic tools to address ecological and evolutionary questions.
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