Phenology varies with phylogeny but not by trophic level with climate change.

Shifts in phenology with climate change can lead to asynchrony between interacting species, with cascading impacts on ecosystem services. Previous meta-analyses have produced conflicting results on whether asynchrony has increased in recent decades, but the underlying data have also varied-including in species composition, interaction types and whether studies compared data grouped by trophic level or compared shifts in known interacting species pairs. Here, using updated data from previous studies and a Bayesian phylogenetic model, we found that species have advanced an average of 3.

An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying.

Background: Prospective malaria public health interventions are initially tested for entomological impact using standardised experimental hut trials. In some cases, data are collated as aggregated counts of potential outcomes from mosquito feeding attempts given the presence of an insecticidal intervention. Comprehensive data i.

Workflow techniques for the robust use of bayes factors.

Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses.

Analysing the distance decay of community similarity in river networks using Bayesian methods.

The distance decay of community similarity (DDCS) is a pattern that is widely observed in terrestrial and aquatic environments. Niche-based theories argue that species are sorted in space according to their ability to adapt to new environmental conditions. The ecological neutral theory argues that community similarity decays due to ecological drift.

Toward a principled Bayesian workflow in cognitive science.

Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's domain expertise.

Synergy in anti-malarial pre-erythrocytic and transmission-blocking antibodies is achieved by reducing parasite density.

Anti-malarial pre-erythrocytic vaccines (PEV) target transmission by inhibiting human infection but are currently partially protective. It has been posited, but never demonstrated, that co-administering transmission-blocking vaccines (TBV) would enhance malaria control. We hypothesized a mechanism that TBV could reduce parasite density in the mosquito salivary glands, thereby enhancing PEV efficacy.

A novel model fitted to multiple life stages of malaria for assessing efficacy of transmission-blocking interventions.

Background: Transmission-blocking interventions (TBIs) aim to eliminate malaria by reducing transmission of the parasite between the host and the invertebrate vector. TBIs include transmission-blocking drugs and vaccines that, when given to humans, are taken up by mosquitoes and inhibit parasitic development within the vector. Accurate methodologies are key to assess TBI efficacy to ensure that only the most potent candidates progress to expensive and time-consuming clinical trials.

Stan: A Probabilistic Programming Language.

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.

Does quantum uncertainty have a place in everyday applied statistics?

We are sympathetic to the general ideas presented in the article by Pothos & Busemeyer (P&B): Heisenberg's uncertainty principle seems naturally relevant in the social and behavioral sciences, in which measurements can affect the people being studied. We propose that the best approach for developing quantum probability models in the social and behavioral sciences is not by directly using the complex probability-amplitude formulation proposed in the article, but rather, more generally, to consider marginal probabilities that need not be averages over conditionals.