The enemy release hypothesis proposes that invasion by exotic plant species is driven by their release from natural enemies (i.e. herbivores and pathogens) in their introduced ranges. However, in many cases, natural enemies, which may be introduced or managed to regulate invasive species, may fail to impact target host populations. Landscape heterogeneity, which can affect both the population dynamics of the pathogen and the susceptibility and the density of hosts, may contribute to why pathogens fail to control hosts despite established negative disease impacts. We explored patterns of post‐fire infection of the fungal head‐smut pathogen Ustilago bullata on the invasive annual cheatgrass Bromus tectorum, which has caused the notorious grass‐fire cycle and ecosystem degradation across Western North America. We asked whether infection level was a driver of host density or vice‐versa, and how weather affected infection and how spatial patterns of infection varied with time since fire, using a combination of structural equation modelling (SEM), proportional odds modelling and entropy‐based local indicator of spatial association (ELSA) on data from >700 plots spanning >100,000 ha remeasured annually for 4 years. Observed infection levels increased with greater prior‐year cheatgrass cover, and disease severity did not suppress cheatgrass populations. Warm, humid fall/winters and proximity to fire refugia (unburned patches) were associated with more infections. Infection clustering was most evident 2–3 years following fire with warm‐wet fall–winter conditions and decreased after two drier, colder winters. Synthesis. Severity of fungal disease did not result in measurable reductions of populations of a non‐native, invasive host species, cheatgrass, which suggests that natural enemies may not strongly regulate cheatgrass in its introduced range. Landscape heterogeneity associated with disturbance and weather limited population‐level infection of hosts by the fungal pathogen. Disturbance (specifically wildfire) and variable weather are key components of this and similar invasion systems, and likely need to be considered when evaluating disease dynamics and potential for natural enemies to influence invasion potential.

Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long-term vegetation trajectories were better explained by initial post-fire weather conditions or by general five-year antecedent weather, and if weather effects helped predict post-fire vegetation abundances at a new site. We parameterized models using a 30-yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well-sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post-fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5-yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post-fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post-fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse-scale gridded weather data are considered.

Background: The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots. Results: Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods. Conclusions: While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations. Keywords: Change point detection, National Land Cover Database, Post-fire monitoring, Sagebrush

This data release provides inputs needed to run the LANDIS-II landscape change model, NECN and Base Fire extensions for the Greater Yellowstone Ecosystem (GYE), USA, and simulation results that underlie figures and analysis in the accompanying publication. We ran LANDIS-II simulations for 112 years, from 1988-2100, using interpolated weather station data for 1988-2015 and downscaled output from 5 general circulation models (GCMs) for 2016-2100. We also included a control future scenario with years drawn from interpolated weather station data from 1980-2015. Model inputs include raster maps (250 × 250 m grid cells) of climate regions and tables of monthly temperature and precipitation for each climate region. We provide initial conditions in 1987 as rasters and tables (i.e., species-age cohorts, aboveground biomass, soil carbon and nitrogen in surface litter 3 soil layers, soil percent sand, soil percent clay, soil wilting point, soil field capacity, soil drainage, soil storm flow and base flow fractions, and soil depth), historical fire data for model calibration, climate-inferred lognormal fire size distributions for each simulation year, and LANDIS-II control files including parameters for species and functional groups. Outputs from 10 replicates for each of 5 GCMs and the control scenario are provided as rasters and tables. Tables include spatially-weighted mean annual temperature and precipitation of the GYE for each GCM and the control scenario, summarize annual area burned by scenario, summarize biomass pools, and summarize changes in mean stand age. Rasters include annual simulated fire severity for 2015-2100, simulated total aboveground biomass in 4-year timesteps, aboveground biomass of all species in 4-year timesteps, stand age in 4-year timesteps, maximum and minimum cohort age for three dominant species (Pinus contorta, Picea engelmannii, and Pseudotsuga menziesii) in 4-year timesteps, forest type in 1988 and 2100, net ecosystem exchange in 2040 and 2100, and total ecosystem carbon in 4-year timesteps.