Xavier A. Harrison

TL;DR: This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

TL;DR: Simulations show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdisPersion is simply ignored, and that their ability to minimise bias is not uniform across all types of over Dispersion and must be applied judiciously.

TL;DR: Carry-over effects are likely to be far more widespread than currently indicated, and they could feasibly be responsible for a large amount of the observed variation in performance among individuals, and warrant a wealth of new research designed specifically to decompose components of variation in fitness attributes related to processes across and within seasons.

TL;DR: Simulation results suggest that OLRE are a useful tool for modelling overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.

TL;DR: 50 research questions of fundamental importance to the science or application of microbial ecology are identified, with the intention of summarising the field and bringing focus to new research avenues.