Vallejo, E. E. | Search bioRxiv

bioRxiv2019

Females don't always sing in response to male song, but when they do, they sing to males with higher pitched songs

The long-held view that bird song is exclusively a male trait has been challenged recently by a number of studies and reviews highlighting the prevalence of female song. In spite of that, there remains a lack of knowledge on the function of female song, with most evidence thus far focusing on females performing duets with males in courtship displays, typically for joint territory defence or mate guarding purposes. Here we show in a tracheophone suboscine passerine Formicarius moniliger, a sexually monomorphic species in which both sexes sing, that females may participate in both intrasexual and intersexual territory defence. Females sing more in response to females than to males, suggesting they consider females more of a threat to their territory. Yet, females also demonstrate an unexpected pattern of singing back to playback of males singing higher frequency song than themselves. Unlike males, who respond indiscriminately to playback of any song performed by either sex, females appear to discern not only the sex, but perhaps also the size of the presumed intruder. There is a strong negative relationship between body mass and frequency, and females responding only to higher frequency male song suggests they will only engage in territory defence with males when they expect those males to be weaker than they are. While our results are consistent with expectations of a shared ancestral function of song in territory defence, they also suggest females may suffer greater costs in engaging in territorial disputes and thus limit their vocal contribution according to the perceived threat.

animal behavior and cognition↗

bioRxiv2019

Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking

BackgroundMachine learning models have proven to be useful tools for the analysis of genetic data. However, with the availability of a wide variety of such methods, model selection has become increasingly difficult, both from the human and computational perspective.\n\nResultsWe present the R package FRESA.CAD Binary Classification Benchmarking that performs systematic comparisons between a collection of representative machine learning methods for solving binary classification problems on genetic datasets.\n\nConclusionsFRESA.CAD Binary Benchmarking demonstrates to be a useful tool over a variety of binary classification problems comprising the analysis of genetic data showing both quantitative and qualitative advantages over similar packages.

bioinformatics↗

bioRxiv2017

Effects of Spatial Heterogeneity on Transmission Potential in Vectorial-Contact Networks: A Comparison of Three Aedes aegypti Control Strategies

Dengue, chikungunya and zika are all transmitted by the Aedes aegypti mosquito. Despite the strong influence of host spatial distribution and movement patterns on the ability of mosquito vectors to transmit pathogens, there is little understanding how these complex interactions modify the spread of disease in spatially heterogeneous populations. In light of present fears of a worldwide zika epidemic, and failures to eradicate dengue and chikungunya; there is a pressing need to get a better picture of how high-resolution details such as human movement in a small landscape, modify the patterns of transmission of these diseases and how different mosquito-control interventions could be affected by these movements.\n\nIn this work we use a computational agent-based model (ABM) to simulate mosquito-human interactions in two different levels of spatial heterogeneity, with human movement, and in the presence of three mosquito-control interventions (spatial spraying, the release of Wolbachia-infected mosquitoes and release of insects with dominant lethal gene). To analyse the results from each of these experiments we examined mosquito population dynamics and host to host contact networks that emerged from the distribution of consecutive bites across humans. We then compared results across experiments to understand the differential effectiveness of different interventions in both the presence and absence of spatial heterogeneities, and analysed network measures of epidemiological relevance (degree probability distributions, mean path length, network density and small-worldness).\n\nFrom our experiments we conclude that spatial heterogeneity greatly influences how a pathogen may spread in a host population when mediated by a mosquito vector, and that these important heterogeneities also strongly affect effectiveness of interventions. Finally, we demonstrate that these host to host vectorial-contact networks can provide operationally important information to inform selection of optimal vector-control strategies.\n\nAuthor SummaryMosquito-borne diseases transmission patterns arise from the complex interactions between hosts and vector. Because these interactions are influenced by host and vector behaviour, spatial constraints, and other factors they are amongst the most difficult to understand. In this work, we use our computational agent-based model: SoNA3BS; to simulate two spatially different settings in the presence and absence of three different mosquito-control interventions: fogging, the release of Wolbachia-infected mosquitoes and the release of insects with dominant lethal gene. Throughout these simulations, we record mosquito population dynamics and mosquito bites on persons. We then compare mosquito population dynamics to the vectorial-contact networks (that emerge from subsequent mosquito bites between humans) and, after performing these comparisons, we proceeded to show that even when mosquito population sizes are almost equal in both spatial settings, the resulting vectorial-contact networks are radically different. This has profound implications in our understanding of how mosquito-borne diseases spread in human populations and is relevant to the effective use of resources allocated to stop these pathogens from causing more harm in human populations.

bioinformatics↗