Blood Parasite Infection Intensity Covaries with Risk‐Taking Personality in Male Carpetan Rock Lizards (Iberolacerta cyreni)

Identifying evolutionary and developmental mechanisms underlying consistent between‐individual differences in behaviour is the main goal in ‘animal personality studies’. Here, we explored whether activity and risk‐taking varied consistently between individuals and correlated to various – potentially fitness linked – male traits in Carpetan rock lizards (Iberolacerta cyreni). Lizards showed significant consistency within both behaviours, implying the presence of activity and risk‐taking personalities. However, there were no correlation between activity and risk‐taking, neither on the between‐ nor on the within‐individual levels, implying the absence of a behavioural syndrome. We found a strong link between the intensity of blood parasite (Haemogregarinidae) infection and risk‐taking: lizards with higher infection intensity took more risk. While we cannot distinguish cause from causative in the parasite intensity – risk‐taking correlation – our results are in line with the asset protection hypothesis predicting that individuals with lower future reproductive value should focus on the current reproductive event and take higher risk.     

Gatekeeper role of brain antigen‐presenting CD11c+ cells in neuroinflammation

Multiple sclerosis is the most frequent chronic inflammatory disease of the CNS. The entry and survival of pathogenic T cells in the CNS are crucial for the initiation and persistence of autoimmune neuroinflammation. In this respect, contradictory evidence exists on the role of the most potent type of antigen‐presenting cells, dendritic cells. Applying intravital two‐photon microscopy, we demonstrate the gatekeeper function of CNS professional antigen‐presenting CD11c⁺ cells, which preferentially interact with Th17 cells. IL‐17 expression correlates with expression of GM‐CSF by T cells and with accumulation of CNS CD11c⁺ cells. These CD11c⁺ cells are organized in perivascular clusters, targeted by T cells, and strongly express the inflammatory chemokines Ccl5, Cxcl9, and Cxcl10. Our findings demonstrate a fundamental role of CNS CD11c⁺ cells in the attraction of pathogenic T cells into and their survival within the CNS. Depletion of CD11c⁺ cells markedly reduced disease severity due to impaired enrichment of pathogenic T cells within the CNS.     

Evidential statistics as a statistical modern synthesis to support 21st century science

During the 20th century, population ecology and science in general relied on two very different statistical paradigms to solve its inferential problems: error statistics (also referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great deal of good science was done using these tools, but both schools suffer from technical and philosophical difficulties. At the turning of the 21st century (Royall in Statistical evidence: a likelihood paradigm. Chapman & Hall, London, 1997 ; Lele in The nature of scientific evidence: statistical, philosophical and empirical considerations. The University of Chicago Press, Chicago, pp 191–216, 2004a ), evidential statistics emerged as a seriously contending paradigm. Drawing on and refining elements from error statistics, likelihoodism, Bayesian statistics, information criteria, and robust methods, evidential statistics is a statistical modern synthesis that smoothly incorporates model identification, model uncertainty, model comparison, parameter estimation, parameter uncertainty, pre-data control of error, and post-data strength of evidence into a single coherent framework. We argue that evidential statistics is currently the most effective statistical paradigm to support 21st century science. Despite the power of the evidential paradigm, we think that there is no substitute for learning how to clarify scientific arguments with statistical arguments. In this paper we sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential statistics. We also discuss a number of misconceptions about the paradigms that have hindered practitioners, as well as some real problems with the error and Bayesian statistical paradigms solved by evidential statistics.