Previous surveys of the literature have shown that reports of statistical analyses often lack
important information, causing lack of transparency and failure of reproducibility. Editors and …

During the past decades, multiple remote sensing data sources, including nighttime light
images, high spatial resolution multispectral satellite images, unmanned drone images, and …

Recent case studies showing substantial declines of insect abundances have raised alarm,
but how widespread such patterns are remains unclear. We compiled data from 166 long …

There is increasing availability and use of unstructured and semi‐structured citizen science
data in biodiversity research and conservation. This expansion of a rich source of 'big …

During the last glacial–interglacial cycle, Arctic biotas experienced substantial climatic
changes, yet the nature, extent and rate of their responses are not fully understood …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Popular frameworks for studying habitat selection include resource‐selection functions
(RSFs) and step‐selection functions (SSFs), estimated using logistic and conditional logistic …

Coming up with Bayesian models for spatial data is easy, but performing inference with them
can be challenging. Writing fast inference code for a complex spatial model with realistically …

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

After the cases of COVID-19 skyrocketed, showing that it was no longer possible to contain
the spread of the disease, the governments of many countries launched mitigation …