Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

1. Predicting and explaining the distribution and density of species is one of the oldest concerns in ecology. Species distributions can be estimated using geostatistical methods, which estimate a latent spatial variable explaining observed variation in densities, but geostatistical methods may be imprecise for species with low densities or few observations. Additionally, simple geostatistical methods fail to account for correlations in distribution among species and generally estimate such cross-correlations as a post hoc exercise. 2. We therefore present spatial factor analysis (SFA), a spatial model for estimating a low-rank approximation to multivariate data, and use it to jointly estimate the distribution of multiple species simultaneously. We also derive an analytic estimate of cross-correlations among species from SFA parameters. 3. As a first example, we show that distributions for 10 bird species in the breeding bird survey in 2012 can be parsimoniously represented using only five spatial factors. As a second case study, we show that forward prediction of catches for 20 rockfishes (Sebastes spp.) off the U.S. West Coast is more accurate using SFA than analysing each species individually. Finally, we show that single-species models give a different picture of cross-correlations than joint estimation using SFA. 4. Spatial factor analysis complements a growing list of tools for jointly modelling the distribution of multiple species and provides a parsimonious summary of cross-correlation without requiring explicit declaration of habitat variables. We conclude by proposing future research that would model species cross-correlations using dissimilarity of species' traits, and the development of spatial dynamic factor analysis for a low-rank approximation to spatial time-series data

Stress-Induced Reinstatement of Drug Seeking: 20 Years of Progress

In human addicts, drug relapse and craving are often provoked by stress. Since 1995, this clinical scenario has been studied using a rat model of stress-induced reinstatement of drug seeking. Here, we first discuss the generality of stress-induced reinstatement to different drugs of abuse, different stressors, and different behavioral procedures. We also discuss neuropharmacological mechanisms, and brain areas and circuits controlling stress-induced reinstatement of drug seeking. We conclude by discussing results from translational human laboratory studies and clinical trials that were inspired by results from rat studies on stress-induced reinstatement. Our main conclusions are (1) The phenomenon of stress-induced reinstatement, first shown with an intermittent footshock stressor in rats trained to self-administer heroin, generalizes to other abused drugs, including cocaine, methamphetamine, nicotine, and alcohol, and is also observed in the conditioned place preference model in rats and mice. This phenomenon, however, is stressor specific and not all stressors induce reinstatement of drug seeking. (2) Neuropharmacological studies indicate the involvement of corticotropin-releasing factor (CRF), noradrenaline, dopamine, glutamate, kappa/dynorphin, and several other peptide and neurotransmitter systems in stress-induced reinstatement. Neuropharmacology and circuitry studies indicate the involvement of CRF and noradrenaline transmission in bed nucleus of stria terminalis and central amygdala, and dopamine, CRF, kappa/dynorphin, and glutamate transmission in other components of the mesocorticolimbic dopamine system (ventral tegmental area, medial prefrontal cortex, orbitofrontal cortex, and nucleus accumbens). (3) Translational human laboratory studies and a recent clinical trial study show the efficacy of alpha-2 adrenoceptor agonists in decreasing stress-induced drug craving and stress-induced initial heroin lapse