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 …

Ecological systems can often be characterised by changes among a finite set of underlying
states pertaining to individuals, populations, communities or entire ecosystems through time …

State–space models (SSMs) are an important modeling framework for analyzing ecological
time series. These hierarchical models are commonly used to model population dynamics …

How can we track population trends when monitoring data are sparse? Population declines
can go undetected, despite ongoing threats. For example, only one of every 200 harvested …

Ecologists develop species-habitat association (SHA) models to understand where species
occur, why they are there and where else they might be. This knowledge can be used to …

State-space models (SSMs) are increasingly used in ecology to model time-series such as
animal movement paths and population dynamics. This type of hierarchical model is often …

Models of population dynamics are frequently used for purposes such as testing hypotheses
about density dependence and predicting species' responses to future environmental …

Estimation of population abundances in the absence of good observational data are
notoriously difficult, yet urgently needed for biodiversity conservation and sustainable use of …

State‐space models are an increasingly common and important tool in the quantitative
ecologists' armoury, particularly for the analysis of time‐series data. This is due to both their …

In an era of rapid change, ecologists are increasingly asked to provide answers to big,
urgent 15 questions of global concern (Solé and Levin, 2022; Yates et al., 2018; Sutherland …