Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …

Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …

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 …

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

The study of animal movement has always been a key element in ecological science,
because it is inherently linked to critical processes that scale from individuals to populations …

The INLA approach for approximate Bayesian inference for latent Gaussian models has
been shown to give fast and accurate estimates of posterior marginals and also to be a …

Written by a prominent statistician and author, the first edition of this bestseller broke new
ground in the then emerging subject of spatial statistics with its coverage of spatial point …

Data integration is a statistical modeling approach that incorporates multiple data sources
within a unified analytical framework. Macrosystems ecology–the study of ecological …

Indices of abundance are the bedrock for stock assessments or empirical management
procedures used to manage fishery catches for fish populations worldwide, and are …

Ecological citizen science data are rapidly growing in availability and use in ecology and
conservation. Many citizen science projects have the flexibility for participants to select …