Brood size, sibling competition, and the cost of begging in great tits (Parus major)

Evolutionary theory of parent-offspring conflict explains begging displays of nestling birds as selfish attempts to influence parental food allocation. Models predict that this conflict may be resolved by honest signaling of offspring need to parents, or by competition among nestmates, leading to escalated begging scrambles. Although the former type of models has been qualitatively supported by experimental studies, the potential for a begging component driven by scramble competition cannot be excluded by the evidence. In a brood-size manipulation experiment with great tits, Parus major, we explored the scramble component in the begging activity of great tit nestlings by investigating the mechanisms of sibling competition in relation to brood size. While under full parental compensation, the feeding rate per nestling will remain constant over all brood sizes for both types of models; the scramble begging models alone predict an increase in begging intensity with brood size, if begging costs do not arise exclusively through predation. Great tit parents adjusted feeding rates to brood size and fed nestlings at similar rates and with similar prey sizes in all three brood-size categories. Despite full parental compensation, the begging and food solicitation activities increased with experimental brood size, whereas nestling body condition deteriorated. These findings support a scramble component in begging and suggest that the competition-induced costs of food solicitation behavior play an important role in the evolution of parent-offspring communicatio

Offspring social network structure predicts fitness in families.

addresses: Centre for Ecology and Conservation, Biosciences, College of Life and Environmental Sciences, University of Exeter, Cornwall Campus, Penryn, Cornwall TR10 9EZ, UK. [email protected]: PMCID: PMC3497231types: Journal Article; Research Support, Non-U.S. Gov'tSocial structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively

LONGO: An R package for interactive gene length dependent analysis for neuronal identity

Motivation: Reprogramming somatic cells into neurons holds great promise to model neuronal development and disease. The efficiency and success rate of neuronal reprogramming, however, may vary between different conversion platforms and cell types, thereby necessitating an unbiased, systematic approach to estimate neuronal identity of converted cells. Recent studies have demonstrated that long genes (\u3e100 kb from transcription start to end) are highly enriched in neurons, which provides an opportunity to identify neurons based on the expression of these long genes. Results: We have developed a versatile R package, LONGO, to analyze gene expression based on gene length. We propose a systematic analysis of long gene expression (LGE) with a metric termed the long gene quotient (LQ) that quantifies LGE in RNA-seq or microarray data to validate neuronal identity at the single-cell and population levels. This unique feature of neurons provides an opportunity to utilize measurements of LGE in transcriptome data to quickly and easily distinguish neurons from non-neuronal cells. By combining this conceptual advancement and statistical tool in a user-friendly and interactive software package, we intend to encourage and simplify further investigation into LGE, particularly as it applies to validating and improving neuronal differentiation and reprogramming methodologies. Availability and implementation: LONGO is freely available for download at https://github.com/biohpc/longo. Supplementary information: Supplementary data are available at Bioinformatics online