INHABIT: A web-based decision support tool for invasive plant species habitat visualization and assessment across the contiguous United States.

Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT's credibility, utility, and relevance.

Semiarid grasslands and extreme precipitation events: do experimental results scale to the landscape?

The frequency and magnitude of deluges (extremely large rain events) are increasing globally as the atmosphere warms. Small-scale experiments suggest that semiarid grasslands are particularly sensitive to both the timing and size of deluge events. However, the assumption that plot-scale results can be extrapolated across landscapes with variable soil textures, plant communities, and grazing regimes has seldom been tested, despite being key to forecasting regional consequences of precipitation extremes. We used precipitation data from an extensive rain gauge network to identify natural deluges (mean size = 60 ± 31 mm, 1984-2012) that occurred across a ˜60-km2 heterogeneous native shortgrass steppe landscape in Colorado. We then related spatial variation in deluge precipitation to postdeluge responses in canopy greenness (normalized difference vegetation index, NDVI) via satellite imagery. Consistent with results from experiments, this semiarid grassland was most sensitive to mid-growing-season deluges, and postdeluge canopy greenness usually increased linearly (67% of the time) with increasing deluge size. This suggests that aboveground productivity in these semiarid systems will likely increase, rather than asymptote, with forecasted increases in deluge size. Importantly, differences in grazing regime did not significantly alter deluge responses, indicating that these patterns are robust to this widespread management practice.