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

Abstract

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.

Shifting hotspots: Climate change projected to drive contractions and expansions of invasive plant abundance habitats

“…We selected eight environmental predictor variables from a candidate set of 78 variables created by Engelstad et al. (2022) that encompassed a suite of temperature and precipitation metrics that are known to influence the establishment and spread of invasive plant taxa. We based our environmental variable selection on the following criteria: (a) availability of future climate projections for the variable and (b) importance for explaining the spatial distributions of 62 invasive plants on our candidate list that were also examined in recent models based on invasive species occurrence (Engelstad et al., 2022).…”

“…(2022) that encompassed a suite of temperature and precipitation metrics that are known to influence the establishment and spread of invasive plant taxa. We based our environmental variable selection on the following criteria: (a) availability of future climate projections for the variable and (b) importance for explaining the spatial distributions of 62 invasive plants on our candidate list that were also examined in recent models based on invasive species occurrence (Engelstad et al., 2022). The final eight environmental variables included in our models were as follows: Minimum winter temperature, Mean diurnal temperature range, Maximum summer temperature, Precipitation seasonality, Mean summer potential water deficit, Mean evapotranspiration between April and October, Isothermality, and Mean annual precipitation (Appendix S2).…”

“…We selected eight environmental predictor variables from a candidate set of 78 variables created by Engelstad et al (2022) that encompassed a suite of temperature and precipitation metrics that are known to influence the establishment and spread of invasive plant taxa. We based our environmental variable selection on the following criteria: (a) availability of future climate projections for the variable and (b) importance for explaining the spatial distributions of 62 invasive plants on our candidate list that were also examined in recent models based on invasive species occurrence (Engelstad et al, 2022) For each variable, we downloaded +2°C future climate projections from TerraClimate (Abatzoglou et al, 2018) and used the 'terra' package (v. 1.5-21) (Hijmans et al, 2022) in R to create our future environmental variable output rasters. TerraClimate integrates 23 CMIP5 global climate models to create future projections (see Qin et al, 2020).…”

Regional models do not outperform continental models for invasive species

“…We began with a national library of 49 predictors representing climate (water deficit, actual evapotranspiration, precipitation, and temperature average from available years 1981-2018 [see Suppl. material 1: Table S4]), human disturbances, soils, water presence / recurrence, fire history, and land cover created by Young et al (2020) using the Albers equal area projection with a 90 m 2 resolution and modified by Engelstad et al (2022) (Suppl. material 1: Table S4).…”

“…This list includes predictors thought to be important for determining the distribution of different types of plant species within the continental U.S. For this analysis, we developed a ranking of predictors a priori to guide predictor selection for each species based on its natural history, such as winter annual species which use overwinter and spring moisture. We first grouped predictors into ten broad categories and ranked those categories based on our experience developing models for > 140 invasive plants in the continental U.S. (Young et al 2020;Engelstad et al 2022) and what environmental characteristics are important for different plant life forms in general. Next, we ranked the predictors within each of these broad categories for each species based on natural history knowledge of each individual species.…”