RESUMO
Sexual dimorphism is a major component of morphological variation across the tree of life, but the mechanisms underlying phenotypic differences between sexes of a single species are poorly understood. We examined the population genomics and biogeography of the common palmfly Elymnias hypermnestra, a dual mimic in which female wing colour patterns are either dark brown (melanic) or bright orange, mimicking toxic Euploea and Danaus species, respectively. As males always have a melanic wing colour pattern, this makes E. hypermnestra a fascinating model organism in which populations vary in sexual dimorphism. Population structure analysis revealed that there were three genetically distinct E. hypermnestra populations, which we further validated by creating a phylogenomic species tree and inferring historical barriers to gene flow. This species tree demonstrated that multiple lineages with orange females do not form a monophyletic group, and the same is true of clades with melanic females. We identified two single nucleotide polymorphisms (SNPs) near the colour patterning gene WntA that were significantly associated with the female colour pattern polymorphism, suggesting that this gene affects sexual dimorphism. Given WntA's role in colour patterning across Nymphalidae, E. hypermnestra females demonstrate the repeatability of the evolution of sexual dimorphism.
Assuntos
Borboletas , Animais , Borboletas/genética , Feminino , Masculino , Filogenia , Polimorfismo Genético , Caracteres Sexuais , Asas de AnimaisRESUMO
Season length and its associated variables can influence the expression of social behaviors, including the occurrence of eusociality in insects. Eusociality can vary widely across environmental gradients, both within and between different species. Numerous theoretical models have been developed to examine the life history traits that underlie the emergence and maintenance of eusociality, yet the impact of seasonality on this process is largely uncharacterized. Here, we present a theoretical model that incorporates season length and offspring development time into a single, individual-focused model to examine how these factors can shape the costs and benefits of social living. We find that longer season lengths and faster brood development times are sufficient to favor the emergence and maintenance of a social strategy, while shorter seasons favor a solitary one. We also identify a range of season lengths where social and solitary strategies can coexist. Moreover, our theoretical predictions are well-matched to the natural history and behavior of two flexibly-eusocial bee species, suggesting our model can make realistic predictions about the evolution of different social strategies. Broadly, this work reveals the crucial role that environmental conditions can have in shaping social behavior and its evolution and underscores the need for further models that explicitly incorporate such variation to study evolutionary trajectories of eusociality.
RESUMO
1. Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. 2. To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). 3. We provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group. 4. Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.