Publications

Working across space and time: nonstationarity in ecological research and application

Ecological research increasingly considers integrative relationships among phenomena at broad spatial and temporal domains. However, …

Over half of western United States' most abundant tree species in decline

Changing forest disturbance regimes and climate are driving accelerated tree mortality across temperate forests. However, it remains …

Integrating automated acoustic vocalization data and point count surveys for efficient estimation of bird abundance

Data integration is a statistical modeling approach that incorporates multiple data sources within a unified analytical framework. …

R package for Nearest Neighbor Gaussian Process models

This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models …

Beyond counts and averages: Relating geodiversity to dimensions of biodiversity

Abstract Aim We may be able to buffer biodiversity against the effects of ongoing climate change by prioritizing the protection of …

Bayesian spatially varying coefficient models in the spBayes R package

This paper describes and illustrates new functionality for fitting spatially varying coefficients models in the spBayes (version 0.4–2) …

A Case Study Competition Among Methods for Analyzing Large Spatial Data

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the big data” era, however, has lead to …

Hierarchical Bayesian models for small area estimation of forest variables using LiDAR

Light detection and ranging (LiDAR) data have become almost ubiquitous as a remote sensing tool in forestry estimation and mapping …

spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models

In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on …