Neural Circuit Mechanisms Underlying Behavioral Evolution in Drosophila

Courtship rituals serve to reinforce reproductive barriers between closely related species. Several species in the Drosophila melanogaster subgroup exhibit pre-mating isolation due, in part, to the fact that D. melanogaster females produce 7,11-heptacosadiene (7,11-HD), a pheromone that promotes courtship in D. melanogaster males but suppresses it in D. simulans, D. yakuba, and D. erecta males. Here we compare pheromone-processing pathways across species to define how males endow 7,11-HD with the opposite behavioral valence to underlie species discrimination. We first show that D. melanogaster and D. simulans males detect 7,11-HD using the homologous peripheral sensory neurons, but this signal is differentially propagated to the P1 neurons that control courtship behavior. A change in the balance of excitation and inhibition onto courtship-promoting neurons transforms an excitatory pheromonal cue in D. melanogaster into an inhibitory one in D. simulans. Our results reveal how species-specific pheromone responses can emerge from conservation of peripheral detection mechanisms and diversification of central circuitry and suggest how evolution can exploit flexible circuit nodes to generate behavioral variation. To investigate if changes in the balance of excitation and inhibition at this node evolved repeatedly, we began characterizing the pheromone processing pathways in D. yakuba and D. erecta, two species we believe derived their aversion to 7,11-HD independently from D. simulans. This comparison provides a rare opportunity to explore the neural basis for parallel behavioral evolution. Finally, we observed differences in the olfactory and gustatory pathways D. melanogaster and D. simulans males use for sex discrimination. In males of both species, the male-specific volatile pheromone, cVA, activates a conserved sensory pathways and suppresses male courtship. However, 7-T, the major cuticular pheromone produced by all males in the D. melanogaster subgroup and by D. simulans females, plays a differential role in regulating male courtship across species – 7-T suppresses courtship in D. melanogaster males, but neither promotes nor inhibits courtship in D. simulans males. A difference in either detection of 7-T by peripheral sensory neurons or propagation of this signal to higher brain regions results in this pheromone activating courtship-suppressing mAL neurons in D. melanogaster males, but not D. simulans males. Together, these studies represent the first systematic comparison of neural circuits across Drosophila species and mark a new advance in the study of behavioral evolution by revealing how changes in central circuitry can alter discrete behaviors

Patterns of Diversification in a Neotropical Radiation of Birds (Aves: Furnariidae)

Ecology and the role of natural selection in lineage diversification has been a central topic in evolutionary biology since Darwin. At the macroevolutionary scale, this idea is embodied in the ecological theory of adaptive radiation, which posits that rapid diversification is driven by ecological adaptive radiation in which speciation is coupled with niche divergence. Within species, the theory of ecological speciation proposes that local adaptation drives speciation by reducing gene flow among populations occupying different environments either by directly reducing migration or by reducing the fitness of migrants. Much progress has been made testing these predictions in a multitude of organisms, but there remains a lack of studies addressing the role of ecology in diversification at multiple evolutionary scales within the same lineage. Herein, I use the Neotropical bird radiation of ovenbirds (Passeriformes: Furnariidae) as a model system to examine the role of ecology in speciation and lineage diversification. I show that, across furnariid subclades, rates of lineage diversification are best predicted by the rate of climatic-niche evolution rather than ecomorphological evolution, although both are clearly important. This result is consistent with a role for environmental gradients in driving speciation through the process of isolation-by-adaptation (IBA). I then compared the relative support for IBA against the null model of isolation-by-distance (IBD) in a species of furnariid, Cranioleuca antisiensis, that shows signs of incipient speciation and is distributed across a broad environmental gradient. Using genetic, phenotypic, and environmental data from across its distribution, I found evidence of local adaptation in body size. However, I found that IBD was the best explanation for genetic differentiation along the cline, suggesting a limited role for the environmental gradient in reducing gene flow among populations of C. antisiensis. Finally, I explore the properties of the speciation mechanism ‘speciation-by-extinction’. Speciation-by-extinction (SBE) is an alternative to the standard model of allopatric speciation where speciation results from divergence accrued following the isolation of two undifferentiated populations. SBE, in contrast, proposes that speciation can result from the partitioning of standing phenotypic or genetic variation through the local extinction of intermediate populations

Periodic vs. intermittent adaptive cycles in quasispecies co-evolution

We study an abstract model for the co-evolution between mutating viruses and the adaptive immune system. In sequence space, these two populations are localized around transiently dominant strains. Delocalization or error thresholds exhibit a novel interdependence because immune response is conditional on the viral attack. An evolutionary chase is induced by stochastic fluctuations and can occur via periodic or intermittent cycles. Using simulations and stochastic analysis, we show how the transition between these two dynamic regimes depends on mutation rate, immune response, and population size.Comment: 5 pages, 3 figures, 11 pages supplementary material; updated formatting; accepted at Phys. Rev. Let

Niche evolution and diversification in a Neotropical radiation of birds (Aves: Furnariidae)

© 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution. Rapid diversification may be caused by ecological adaptive radiation via niche divergence. In this model, speciation is coupled with niche divergence and lineage diversification is predicted to be correlated with rates of niche evolution. Studies of the role of niche evolution in diversification have generally focused on ecomorphological diversification but climatic-niche evolution may also be important. We tested these alternatives using a phylogeny of 298 species of ovenbirds (Aves: Furnariidae). We found that within Furnariidae, variation in species richness and diversification rates of subclades were best predicted by rate of climatic-niche evolution than ecomorphological evolution. Although both are clearly important, univariate regression and multivariate model averaging more consistently supported the climatic-niche as the best predictor of lineage diversification. Our study adds to the growing body of evidence, suggesting that climatic-niche divergence may be an important driver of rapid diversification in addition to ecomorphological evolution. However, this pattern may depend on the phylogenetic scale at which rate heterogeneity is examined

Continuous attractor working memory and provenance of channel models

The brain is a complex biological system composed of a multitude of microscopic processes, which together give rise to computational abilities observed in everyday behavior. Neuronal modeling, consisting of models of single neurons and neuronal networks at varying levels of biological detail, can synthesize the gaps currently hard to constrain in experiments and provide mechanistic explanations of how these computations might arise. In this thesis, I present two parallel lines of research on neuronal modeling, situated at varying levels of biological detail. First, I assess the provenance of voltage-gated ion channel models in an integrative meta-analysis that investigates a backlog of nearly 50 years of published research. To cope with the ever-increasing volume of research produced in the field of neuroscience, we need to develop methods for the systematic assessment and comparison of published work. As we demonstrate, neuronal models offer the intriguing possibility of performing automated quantitative analyses across studies, by standardized simulated experiments. We developed protocols for the quantitative comparison of voltage-gated ion channels, and applied them to a large body of published models, allowing us to assess the variety and temporal development of different models for the same ion channels over the time scale of years of research. Beyond a systematic classification of the existing body of research made available in an online platform, we show that our approach extends to large-scale comparisons of ion channel models to experimental data, thereby facilitating field-wide standardization of experimentally-constrained modeling. Second, I investigate neuronal models of working memory (WM). How can cortical networks bridge the short time scales of their microscopic components, which operate on the order of milliseconds, to the behaviorally relevant time scales of seconds observed in WM experiments? I consider here a candidate model: continuous attractor networks. These can implement WM for a continuum of possible spatial locations over several seconds and have been proposed for the organization of prefrontal cortical networks. I first present a novel method for the efficient prediction of the network-wide steady states from the underlying microscopic network properties. The method can be applied to predict and tune the "bump" shapes of continuous attractors implemented in networks of spiking neuron models connected by nonlinear synapses, which we demonstrate for saturating synapses involving NMDA receptors. In a second part, I investigate the computational role of short-term synaptic plasticity as a synaptic nonlinearity. Continuous attractor models are sensitive to the inevitable variability of biological neurons: variable neuronal firing and heterogeneous networks decrease the time that memories are accurately retained, eventually leading to a loss of memory functionality on behaviorally relevant time scales. In theory and simulations, I show that short-term plasticity can control the time scale of memory retention, with facilitation and depression playing antagonistic roles in controlling the drift and diffusion of locations in memory. Finally, we place quantitative constraints on the combination of synaptic and network parameters under which continuous attractors networks can implement reliable WM in cortical settings