Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Building useful models of species distributions requires attention to several important issues,
one being imperfect detection of species. Data sets of species detections are likely to suffer …

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the
most vulnerable to biodiversity loss. Mitigation measures, including wastewater treatment …

The acute decline in global biodiversity includes not only the loss of rare species, but also
the rapid collapse of common species across many different taxa. The loss of pollinating …

Uncertainty remains regarding the role of anthropogenic climate change in declining insect
populations, partly because our understanding of biotic response to climate is often …

We describe NIMBLE, a system for programming statistical algorithms for general model
structures within R. NIMBLE is designed to meet three challenges: flexible model …

Agriculture today places great strains on biodiversity, soils, water and the atmosphere, and
these strains will be exacerbated if current trends in population growth, meat and energy …

Environmental DNA (eDNA) metabarcoding is increasingly used to study the present and
past biodiversity. eDNA analyses often rely on amplification of very small quantities or …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

Bayesian modeling has become an indispensable tool for ecological research because it is
uniquely suited to deal with complexity in a statistically coherent way. This textbook provides …