Comparing single- and mixed-species groups in fruit flies: differences in group dynamics, but not group formation.

Mixed-species groups describe active associations among individuals of 2 or more species at the same trophic level. Mixed-species groups are important to key ecological and evolutionary processes such as competition and predation, and research that ignores the presence of other species risks ignoring a key aspect of the environment in which social behavior is expressed and selected. Despite the defining emphasis of active formation for mixed-species groups, surprisingly little is known about the mechanisms by which mixed-species groups form. Furthermore, insects have been almost completely ignored in the study of mixed-species groups, despite their taxonomic importance and relative prominence in the study of single-species groups. Here, we measured group formation processes in Drosophila melanogaster and its sister species, Drosophila simulans. Each species was studied alone, and together, and one population of D. melanogaster was also studied both alone and with another, phenotypically distinct D. melanogaster population, in a nested-factorial design. This approach differs from typical methods of studying mixed-species groups in that we could quantitatively compare group formation between single-population, mixed-population, and mixed-species treatments. Surprisingly, we found no differences between treatments in the number, size, or composition of groups that formed, suggesting that single- and mixed-species groups form through similar mechanisms of active attraction. However, we found that mixed-species groups showed elevated interspecies male-male interactions, relative to interpopulation or intergenotype interactions in single-species groups. Our findings expand the conceptual and taxonomic study of mixed-species groups while raising new questions about the mechanisms of group formation broadly.

Basic reversal-learning capacity in flies suggests rudiments of complex cognition.

The most basic models of learning are reinforcement learning models (for instance, classical and operant conditioning) that posit a constant learning rate; however many animals change their learning rates with experience. This process is sometimes studied by reversing an existing association between cues and rewards, and measuring the rate of relearning. Augmented reversal-learning, where learning rates increase with practice, can be an important component of behavioral flexibility; and may provide insight into higher cognition. Previous studies of reversal-learning in Drosophila have not measured learning rates, but have tended to focus on measuring gross deficits in reversal-learning, as the ratio of two timepoints. These studies have uncovered a diversity of mechanisms underlying reversal-learning, but natural genetic variation in this trait has yet to be assessed. We conducted a reversal-learning regime on a diverse panel of Drosophila melanogaster genotypes. We found highly significant genetic variation in their baseline ability to learn. We also found that they have a consistent, and strong (1.3×), increase in their learning speed with reversal. We found no evidence, however, that there was genetic variation in their ability to increase their learning rates with experience. This may suggest that Drosophila have a hitherto unrecognized ability to integrate acquired information, and improve their decision making; but that their mechanisms for doing so are under strong constraints.

Sure enough: efficient Bayesian learning and choice.

Probabilistic decision-making is a general phenomenon in animal behavior, and has often been interpreted to reflect the relative certainty of animals' beliefs. Extensive neurological and behavioral results increasingly suggest that animal beliefs may be represented as probability distributions, with explicit accounting of uncertainty. Accordingly, we develop a model that describes decision-making in a manner consistent with this understanding of neuronal function in learning and conditioning. This first-order Markov, recursive Bayesian algorithm is as parsimonious as its minimalist point-estimate, Rescorla-Wagner analogue. We show that the Bayesian algorithm can reproduce naturalistic patterns of probabilistic foraging, in simulations of an experiment in bumblebees. We go on to show that the Bayesian algorithm can efficiently describe the behavior of several heuristic models of decision-making, and is consistent with the ubiquitous variation in choice that we observe within and between individuals in implementing heuristic decision-making. By describing learning and decision-making in a single Bayesian framework, we believe we can realistically unify descriptions of behavior across contexts and organisms. A unified cognitive model of this kind may facilitate descriptions of behavioral evolution.

Veteran-Centered Exemplars in a Prelicensure Nursing Program Curriculum.

Veterans have unique health care needs that must be addressed. Seventy-five percent of veterans are treated outside Veteran Health Administration facilities, so all health care providers should be educated on how to better serve veterans and their families. Nurses are in a prime position to lead the way in these efforts, beginning in prelicensure nursing education. In this article, the authors describe a model and exemplars for integrating veteran-centered care into an existing curriculum, from beginning through advanced levels.

A Bayesian Approach to Social Structure Uncovers Cryptic Regulation of Group Dynamics in Drosophila melanogaster.

Understanding the mechanisms that give rise to social structure is central to predicting the evolutionary and ecological outcomes of social interactions. Modeling this process is challenging, because all individuals simultaneously behave in ways that shape their social environments--a process called social niche construction (SNC). In earlier work, we demonstrated that aggression acts as an SNC trait in fruit flies (Drosophila melanogaster), but the mechanisms of that process remained cryptic. Here, we analyze how individual social group preferences generate overall social structure. We use a combination of agent-based simulation and approximate Bayesian computation to fit models to empirical data. We confirm that genetic variation in aggressive behavior influences social group structure. Furthermore, we find that female decamping due to male behavior may play an underappreciated role in structuring social groups. Male-male aggression may sometimes destabilize groups, but it may also be an SNC behavior for shaping desirable groups for females. Density intensifies female social preferences; thus, the role of female behavior in shaping group structure may become more important at high densities. Our ability to model the ontogeny of group structure demonstrates the utility of the Bayesian model-based approach in social behavioral studies.

Simulation-based Bayesian Analysis of Complex Data.

Our ability to collect large datasets is growing rapidly. Such richness of data offers great promise in terms of addressing detailed scientific questions in great depth. However, this benefit is not without scientific difficulty: many traditional analysis methods become computationally intractable for very large datasets. However, one can frequently still simulate data from scientific models for which direct calculation is no longer possible. In this paper we propose a Bayesian perspective for such analyses, and argue for the advantage of a simulation-based approximate Bayesian method that remains tractable when tractability of other methods is lost. This method, which is known as "approximate Bayesian computation" [ABC], has now been used in a variety of contexts, such as the analysis of tumor data (a tumor being a complex population of cells), and the analysis of human genetic variation data (which arise from a population of individual people). We review a number of ABC methods, with specific attention to the use of ABC in agent-based models, and give pointers to software that allows straightforward implementation of the ABC approach. In this way we demonstrate the utility of simulation-based analyses of large datasets within a rigorous statistical framework.

A gene-based SNP resource and linkage map for the copepod Tigriopus californicus.

BACKGROUND: As yet, few genomic resources have been developed in crustaceans. This lack is particularly evident in Copepoda, given the extraordinary numerical abundance, and taxonomic and ecological diversity of this group. Tigriopus californicus is ideally suited to serve as a genetic model copepod and has been the subject of extensive work in environmental stress and reproductive isolation. Accordingly, we set out to develop a broadly-useful panel of genetic markers and to construct a linkage map dense enough for quantitative trait locus detection in an interval mapping framework for T. californicus--a first for copepods. RESULTS: One hundred and ninety Single Nucleotide Polymorphisms (SNPs) were used to genotype our mapping population of 250 F2 larvae. We were able to construct a linkage map with an average intermarker distance of 1.8 cM, and a maximum intermarker distance of 10.3 cM. All markers were assembled into linkage groups, and the 12 linkage groups corresponded to the 12 known chromosomes of T. californicus. We estimate a total genome size of 401.0 cM, and a total coverage of 73.7%. Seventy five percent of the mapped markers were detected in 9 additional populations of T. californicus. Of available model arthropod genomes, we were able to show more colocalized pairs of homologues between T. californicus and the honeybee Apis mellifera, than expected by chance, suggesting preserved macrosynteny between Hymenoptera and Copepoda. CONCLUSIONS: Our study provides an abundance of linked markers spanning all chromosomes. Many of these markers are also found in multiple populations of T. californicus, and in two other species in the genus. The genomic resource we have developed will enable mapping throughout the geographical range of this species and in closely related species. This linkage map will facilitate genome sequencing, mapping and assembly in an ecologically and taxonomically interesting group for which genomic resources are currently under development.

High-dimensional variance partitioning reveals the modular genetic basis of adaptive divergence in gene expression during reproductive character displacement.

Although adaptive change is usually associated with complex changes in phenotype, few genetic investigations have been conducted on adaptations that involve sets of high-dimensional traits. Microarrays have supplied high-dimensional descriptions of gene expression, and phenotypic change resulting from adaptation often results in large-scale changes in gene expression. We demonstrate how genetic analysis of large-scale changes in gene expression generated during adaptation can be accomplished by determining high-dimensional variance partitioning within classical genetic experimental designs. A microarray experiment conducted on a panel of recombinant inbred lines (RILs) generated from two populations of Drosophila serrata that have diverged in response to natural selection, revealed genetic divergence in 10.6% of 3762 gene products examined. Over 97% of the genetic divergence in transcript abundance was explained by only 12 genetic modules. The two most important modules, explaining 50% of the genetic variance in transcript abundance, were genetically correlated with the morphological traits that are known to be under selection. The expression of three candidate genes from these two important genetic modules was assessed in an independent experiment using qRT-PCR on 430 individuals from the panel of RILs, and confirmed the genetic association between transcript abundance and morphological traits under selection.

Natural genetic variation in social niche construction: social effects of aggression drive disruptive sexual selection in Drosophila melanogaster.

Social niche construction (SNC) occurs when animals actively shape their social environments. Currently the fitness consequences of SNC are poorly understood, and no study has examined whether variation in SNC has a genetic basis. Here we report the first instance of genetic variation in SNC by showing that Drosophila male aggression shapes the social environment. We allowed flies of different genotypes to interact in complex arenas; we measured the number and sex of individuals in the groups that formed and counted instances of mating. Arenas containing more aggressive male genotypes formed groups with fewer males, demonstrating that aggressive male genotypes experienced different social environments than nonaggressive genotypes. Further, genotypes with highest mating success were those whose SNC behavior generated the social environment in which they were most adept at mating: genotypes who mate most often after winning aggressive encounters benefit from aggressive SNC, while genotypes who mate most often after losing achieve high mating rates by forgoing aggression. The presence of these alternative strategies-which were robust across eight population densities-revealed that selection on aggression and context-dependent mating was disruptive, consistent with the hypothesis that SNC can maintain genetic variation in multiple behaviors.

Does segregating variation in sexual or microhabitat preferences lead to non-random mating within a population of Drosophila melanogaster?

Variation in female choice for mates has implications for the maintenance of genetic variation and the evolution of male traits. Yet, estimates of population-level variation in male mating success owing to female genotype are rare. Here, we used a panel of recombinant inbred lines to estimate the strength of selection at many genetic loci in a single generation and attempt to assess differences between females with respect to the males they mated with. We performed selection assays in a complex environment to allow differences in habitat or social group preference to be expressed. We detected directional selection at loci across the genome, but are unable to provide support for differential male success because of variation in female genotype.

Does sex trade with violence among genotypes in Drosophila melanogaster?

The evolutionary forces shaping the ability to win competitive interactions, such as aggressive encounters, are still poorly understood. Given a fitness advantage for competitive success, variance in aggressive and sexual display traits should be depleted, but a great deal of variation in these traits is consistently found. While life history tradeoffs have been commonly cited as a mechanism for the maintenance of variation, the variability of competing strategies of conspecifics may mean there is no single optimum strategy. We measured the genetically determined outcomes of aggressive interactions, and the resulting effects on mating success, in a panel of diverse inbred lines representing both natural variation and artificially selected genotypes. Males of one genotype which consistently lost territorial encounters with other genotypes were nonetheless successful against males that were artificially selected for supernormal aggression and dominated all other lines. Intransitive patterns of territorial success could maintain variation in aggressive strategies if there is a preference for territorial males. Territorial success was not always associated with male mating success however and females preferred 'winners' among some male genotypes, and 'losers' among other male genotypes. This suggests that studying behaviour from the perspective of population means may provide limited evolutionary and genetic insight. Overall patterns of competitive success among males and mating transactions between the sexes are consistent with mechanisms proposed for the maintenance of genetic variation due to nonlinear outcomes of competitive interactions.

Natural genetic variation in cuticular hydrocarbon expression in male and female Drosophila melanogaster.

Cuticular hydrocarbons (CHCs) act as contact pheromones in Drosophila melanogaster and are an important component of several ecological traits. Segregating genetic variation in the expression of CHCs at the population level in D. melanogaster is likely to be important for mate choice and climatic adaptation; however, this variation has never been characterized. Using a panel of recombinant inbred lines (RILs) derived from a natural population, we found significant between-line variation for nearly all CHCs in both sexes. We identified 25 QTL in females and 15 QTL in males that pleiotropically influence CHC expression. There was no evidence of colocalization of QTL for homologous traits across the sexes, indicating that sexual dimorphism and low intersex genetic correlations between homologous CHCs are a consequence of largely independent genetic control. This is consistent with a pattern of divergent sexual and natural selection between the sexes.

Inversion frequencies in Drosophila serrata along an eastern Australian transect.

Clinal patterns over broad geographic regions provide a way of identifying characteristics of species under selection and are increasingly being used in quantitative trait locus mapping of adaptive genetic variation in Drosophila. However, interpretations of clinal patterns can be complicated by inversions that also vary clinally and reduce recombination in some parts of the genome. Drosophila serrata (Malloch) is an Australian endemic species being used to investigate the genetic basis of geographic variation in climatic adaptation and mate recognition. Here we describe inversions in D. serrata populations from the east coast of Australia, covering tropical and temperate regions. Seven autosomal paracentric inversions and 1 apparently complex X chromosome arrangement were identified from these populations. All inverted arrangements were relatively more common in tropical populations; 2 common inversions showed clinal patterns over part of the range of D. serrata. Inversion polymorphism was relatively higher in tropical populations and almost absent in populations near the cooler southern border, in agreement with findings on other Drosophila species. While these patterns will complicate mapping of adaptive variation in D. serrata, they suggest that this species will be useful in investigatingthe dynamics of inversion-trait associations in natural populations.