Under‐representation of avian studies in landscape genetics

Landscape genetics is a rapidly growing discipline that examines how heterogeneous landscapes and other environmental factors influence population genetic variation. We conducted a systematic review of the landscape genetic literature which demonstrates that birds are severely under‐represented relative to their species diversity and general publication prevalence. Most avian studies were on species that have relatively low dispersal ability, and we suggest that this reflects an assumed high vagility of birds that precludes spatial genetic variation relatable to landscape heterogeneity. However, spatial genetic variation exists in several bird species with very high dispersal ability, but this has not been considered in the context of landscape features. Genetic patterns may also relate to landscape due to breeding habitat selection and territorial behaviour, despite the fact that species may be able to move throughout different landscape elements with minimal movement costs. Habitat loss and fragmentation are continuing globally and are strongly related to declines in bird populations. Landscape genetic studies provide a means to understand, predict and mitigate the effects of anthropogenic landscape change on birds. This review promotes the need for landscape genetic studies of birds, such that a greater understanding of the drivers of their genetic structuring can be developed and generalizations can be made from landscape genetic studies that apply more broadly across taxa.     

Goodness‐of‐fit tests for disorder detection in NGS experiments

Next‐generation sequencing (NGS) experiments are often performed in biomedical research nowadays, leading to methodological challenges related to the high‐dimensional and complex nature of the recorded data. In this work we review some of the issues that arise in disorder detection from NGS experiments, that is, when the focus is the detection of deletion and duplication disorders for homozygosity and heterozygosity in DNA sequencing. A statistical model to cope with guanine/cytosine bias and phasing and prephasing phenomena at base level is proposed, and a goodness‐of‐fit procedure for disorder detection is derived. The method combines the proper evaluation of local p‐values (one for each DNA base) with suitable corrections for multiple comparisons and the discrete nature of the p‐values. A global test for the detection of disorders in the whole DNA region is proposed too. The performance of the introduced procedures is investigated through simulations. A real data illustration is provided.     

Quantification of length‐bias in screening trials with covariate‐dependent test sensitivity

Length‐biased sampling exists in screening programs where longer duration disease is detected during the preclinical stage because a longer sojourn time (preclinical duration) has a higher probability of being screen detected. By modeling the course of disease, we quantify the effect of length‐biased sampling on clinical duration when cases are subject to periodic screening with variable test sensitivity. We use the highly flexible bivariate lognormal density to jointly model preclinical and clinical durations, and we model screening test sensitivity as a function of the sojourn time and number of previous false negative screens. We show that the mean clinical duration among screen‐detected cases can be up to 40% higher, with shrinking standard deviation, than those among nonscreen‐detected cases, due to biased sampling alone, irrespective of any possible benefit (increased survival time arising from earlier detection or reduction in mortality). These findings will aid in the design and interpretation of screening trials.     

Social constraint and an absence of sex‐biased dispersal drive fine‐scale genetic structure in white‐winged choughs

This study used eight polymorphic microsatellite loci to examine the relative effects of social organization and dispersal on fine‐scale genetic structure in an obligately cooperative breeding bird, the white‐winged chough (Corcorax melanorhamphos). Using both individual‐level and population‐level analyses, it was found that the majority of chough groups consisted of close relatives and there was significant differentiation among groups (F_ST = 0.124). However, spatial autocorrelation analysis revealed strong spatial genetic structure among groups up to 2 km apart, indicating above average relatedness among neighbours. Multiple analyses showed a unique lack of sex‐biased dispersal. As such, choughs may offer a model species for the study of the evolution of sex‐biased dispersal in cooperatively breeding birds. These findings suggest that genetic structure in white‐winged choughs reflects the interplay between social barriers to dispersal resulting in large family groups that can remain stable over long periods of times, and short dispersal distances which lead to above average relatedness among neighbouring groups.     

Detecting and avoiding likely false‐positive findings – a practical guide

Recently there has been a growing concern that many published research findings do not hold up in attempts to replicate them. We argue that this problem may originate from a culture of ‘you can publish if you found a significant effect’. This culture creates a systematic bias against the null hypothesis which renders meta‐analyses questionable and may even lead to a situation where hypotheses become difficult to falsify. In order to pinpoint the sources of error and possible solutions, we review current scientific practices with regard to their effect on the probability of drawing a false‐positive conclusion. We explain why the proportion of published false‐positive findings is expected to increase with (i) decreasing sample size, (ii) increasing pursuit of novelty, (iii) various forms of multiple testing and researcher flexibility, and (iv) incorrect P‐values, especially due to unaccounted pseudoreplication, i.e. the non‐independence of data points (clustered data). We provide examples showing how statistical pitfalls and psychological traps lead to conclusions that are biased and unreliable, and we show how these mistakes can be avoided. Ultimately, we hope to contribute to a culture of ‘you can publish if your study is rigorous’. To this end, we highlight promising strategies towards making science more objective. Specifically, we enthusiastically encourage scientists to preregister their studies (including a priori hypotheses and complete analysis plans), to blind observers to treatment groups during data collection and analysis, and unconditionally to report all results. Also, we advocate reallocating some efforts away from seeking novelty and discovery and towards replicating important research findings of one's own and of others for the benefit of the scientific community as a whole. We believe these efforts will be aided by a shift in evaluation criteria away from the current system which values metrics of ‘impact’ almost exclusively and towards a system which explicitly values indices of scientific rigour.     

A cellular perspective of bias at G protein‐coupled receptors

G protein‐coupled receptors (GPCRs) modulate cell function over short‐ and long‐term timescales. GPCR signaling depends on biochemical parameters that define the what, when, and where of receptor function: what proteins mediate and regulate receptor signaling, where within the cell these interactions occur, and how long these interactions persist. These parameters can vary significantly depending on the activating ligand. Collectivity, differential agonist activity at a GPCR is called bias or functional selectivity. Here we review agonist bias at GPCRs with a focus on ligands that show dramatically different cellular responses from their unbiased counterparts.     

Codon usage bias: causative factors,quantification methods and genome‐wide patterns: with emphasis on insect genomes

Codon usage bias refers to the phenomenon where specific codons are used more often than other synonymous codons during translation of genes, the extent of which varies within and among species. Molecular evolutionary investigations suggest that codon bias is manifested as a result of balance between mutational and translational selection of such genes and that this phenomenon is widespread across species and may contribute to genome evolution in a significant manner. With the advent of whole‐genome sequencing of numerous species, both prokaryotes and eukaryotes, genome‐wide patterns of codon bias are emerging in different organisms. Various factors such as expression level, GC content, recombination rates, RNA stability, codon position, gene length and others (including environmental stress and population size) can influence codon usage bias within and among species. Moreover, there has been a continuous quest towards developing new concepts and tools to measure the extent of codon usage bias of genes. In this review, we outline the fundamental concepts of evolution of the genetic code, discuss various factors that may influence biased usage of synonymous codons and then outline different principles and methods of measurement of codon usage bias. Finally, we discuss selected studies performed using whole‐genome sequences of different insect species to show how codon bias patterns vary within and among genomes. We conclude with generalized remarks on specific emerging aspects of codon bias studies and highlight the recent explosion of genome‐sequencing efforts on arthropods (such as twelve Drosophila species, species of ants, honeybee, Nasonia and Anopheles mosquitoes as well as the recent launch of a genome‐sequencing project involving 5000 insects and other arthropods) that may help us to understand better the evolution of codon bias and its biological significance.     

Dominance style of female white‐faced capuchins

Dominance style, the level of tolerance displayed by dominant individuals toward subordinate ones, is exhibited along a continuum from despotic to relaxed. It is a useful concept to describe the nature of dominance relationships in macaque species and it bridges among multiple features of dominance hierarchies, aggression, kinship and conflict resolution. Capuchins share many behavioral similarities with Old World monkeys and like macaques, may exhibit a suite of covarying characteristics related to dominance. Here, we provide an assessment of dominance style by examining measures of aggression and kin bias in 22 adult female white‐faced capuchin monkeys (Cebus capucinus) in three social groups at Santa Rosa Sector, Costa Rica. We found that bidirectionality of aggression was low (mean = 6.9% ± SE 1.6). However, there were few significant correlations between kin relatedness and social behavior (approaching, grooming, proximity, and co‐feeding), even though the intensity of kin bias in grooming was moderate and higher in the larger group. We conclude that patterns of aggression and kin‐biased behavior in our study animals are dissimilar to the patterns of covariation observed in macaque species. While unidirectional aggression suggests a despotic dominance style, the moderate expression of kin bias suggests an intermediate to relaxed classification when compared with results from an analysis of 19 macaque species. Additional studies of capuchin species and behaviors associated with dominance style (i.e., conciliatory tendencies) would help to create a comparative framework for the genus Cebus, and allow for more detailed cross‐species comparison of dominance relationships across all primates. Am J Phys Anthropol 150:591–601, 2013. © 2013 Wiley Periodicals, Inc.     

Allele frequency‐free inference of close familial relationships from genotypes or low‐depth sequencing data

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.     

Operator bias in software‐aided bat call identification

Software-aided identification facilitates the handling of large sets of bat call recordings, which is particularly useful in extensive acoustic surveys with several collaborators. Species lists are generated by “objective” automated classification. Subsequent validation consists of removing any species not believed to be present. So far, very little is known about the identification bias introduced by individual validation of operators with varying degrees of experience. Effects on the quality of the resulting data may be considerable, especially for bat species that are difficult to identify acoustically. Using the batcorder system as an example, we compared validation results from 21 volunteer operators with 1–26 years of experience of working on bats. All of them validated identical recordings of bats from eastern Austria. The final outcomes were individual validated lists of plausible species. A questionnaire was used to enquire about individual experience and validation procedures. In the course of species validation, the operators reduced the software''s estimate of species richness. The most experienced operators accepted the smallest percentage of species from the software''s output and validated conservatively with low interoperator variability. Operators with intermediate experience accepted the largest percentage, with larger variability. Sixty-six percent of the operators, mainly with intermediate and low levels of experience, reintroduced species to their validated lists which had been identified by the automated classification, but were finally excluded from the unvalidated lists. These were, in many cases, rare and infrequently recorded species. The average dissimilarity of the validated species lists dropped with increasing numbers of recordings, tending toward a level of ˜20%. Our results suggest that the operators succeeded in removing false positives and that they detected species that had been wrongly excluded during automated classification. Thus, manual validation of the software''s unvalidated output is indispensable for reasonable results. However, although application seems easy, software-aided bat call identification requires an advanced level of operator experience. Identification bias during validation is a major issue, particularly in studies with more than one participant. Measures should be taken to standardize the validation process and harmonize the results of different operators.     

Adaptive Sex‐Specific Cognitive Bias in Predation Behaviours of Japanese Pygmy Squid

Errors in decision‐making in animals can be partially explained by adaptive evolution, and error management theory explains that cognitive biases result from the asymmetric costs of false‐positive and false‐negative errors. Error rates that result from the cognitive bias may differ between sexes. In addition, females are expected to have higher feeding rates than males because of the high energy requirements of gamete production. Thus, females may suffer relatively larger costs from false‐negative errors (i.e. non‐feeding) than males, and female decisions would be biased to reduce these costs if the costs of false‐positive errors are not as high. Females would consequently overestimate their capacity in relation to the probability of predation success. We tested this hypothesis using the Japanese pygmy squid Idiosepius paradoxus. Our results show that size differences between the squid and prey shrimp affected predatory attacks, and that predatory attacks succeeded more often when the predator was relatively larger than the prey. Nevertheless, compared to male predatory attacks, female squid frequently attacked even if their size was relatively small compared to the prey, suggesting that the females overestimated their probability of success. However, if the females failed in the first attack, they subsequently adjusted their attack threshold: squid did not attack again if the prey size was relatively larger. These results suggest a sex‐specific cognitive bias, that is females skewed judgment in decision‐making for the first predation attack, but they also show that squid can modify their threshold to determine whether they should attack in subsequent encounters.     

Estimating multiple time‐fixed treatment effects using a semi‐Bayes semiparametric marginal structural Cox proportional hazards regression model

Marginal structural models for time‐fixed treatments fit using inverse‐probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite‐sample bias when data are sparse, as is typical for large‐sample procedures. Here we propose a semi‐Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite‐sample performance. This approach uses simple symmetric data‐augmentation priors. Limited simulation experiments indicate that the proposed approach reduces finite‐sample bias and improves confidence‐interval coverage when the true values lie within the central “hill” of the prior distribution. We illustrate the approach with data from a nonexperimental study of HIV treatments.     

Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities

1. Close‐kin mark–recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population‐level inference relative to traditional monitoring programs.
2. One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity.
3. We used individual‐based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long‐lived mammal species subject to lethal sampling.
4. Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov–Smirnov tests.
5. Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program.