The scientific rigor and computational methods of causal inference have had great impacts
on many disciplines but have only recently begun to take hold in spatial applications. Spatial …

Spatial statistics is concerned with the analysis of data that have spatial locations associated
with them, and those locations are used to model statistical dependence between the data …

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 …

Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation.
Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are …

The world is becoming increasingly complex, with larger quantities of data available to be
analyzed. It so happens that much of these" big data" that are available are spatio-temporal …

Background Over the past 20 years, schistosomiasis control has been scaled up. Preventive
chemotherapy with praziquantel is the main intervention. We aimed to assess the effect of …

Background The capacity of forest fuel treatments to moderate the behavior and severity of
subsequent wildfires depends on weather and fuel conditions at the time of burning …

The concept of spatial confounding is closely connected to spatial regression, although no
general definition has been established. A generally accepted idea of spatial confounding in …

Species distribution models (SDM s) are widely used in ecology and related fields. They are
frequently adopted to predict the expected occurrence (presence/absence) or abundance …

Biologists routinely fit novel and complex statistical models to push the limits of our
understanding. Examples include, but are not limited to, flexible Bayesian approaches (eg …