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• Geospatial models in weed risk assessments need to be accessible and reproducible. • The Proto4 model provides accurate yet simple to use suitability distributions. • This model will help invasive species prevention and management. "Proto3" is a geospatial model used by the United States Department of Agriculture (USDA) Plant Protection and Quarantine to predict the potential distribution of non-native weed species in the continental U.S. as part of routine weed risk assessments (WRA). While performing as well as other methods, this tool has the benefit of being simple to produce, expanding accessibility and reproducibility. However, it has the tendency to overestimate potential distributions. To address this shortcoming, this paper introduces the "Proto4" model and compares it with the established and mechanistically similar "Proto3" model currently used. Both models overlay Plant Hardiness Zones, precipitation, and Köppen-Geiger climate classes with global distribution of a plant species and rely on semi-qualitative assessments of a plant's affinity for each of the climate categories. However, Proto4 uses more detailed layers of the Plant Hardiness Zones and Köppen-Geiger climate classes, adds elevation as a fourth predictive variable to increase the precision of predictive maps. Additionally, we incorporate uncertainty to spatially distinguish regions of different potential suitability. We compared the performance of both models by estimating the predicted distributions of 30 broadly distributed, invasive plants in the U.S. with Proto3 and Proto4. We found that on average, the Proto4 model produces predicted distributions that are nearly 780,000 square kilometers (an area larger than the state of Texas) smaller than the Proto3, while only failing to capture a median of fewer than 0.5% more georeferenced points. Furthermore, the inclusion of uncertainty classes adds to the utility of Proto4 by distinguishing areas with greater and lesser degrees of evidence that a particular area is suitable for an invasive species, providing more information to help select invasive species prevention and management prioritization strategies. [ABSTRACT FROM AUTHOR]

• Soil processes are often governed by feedback mechanisms that are poorly understood • A coupled soil-water-plant-nutrient model using system dynamics (SD) is described • The SD model was effectively calibrated to four different soils and climates • Sensitivity analyses corroborate observations seen in common management schemes • Complex connections between decision-making factors and soil processes are discussed Soils form the foundation of terrestrial ecosystems that regulate the processes and functions driving ecosystem goods and services provisions that humans rely on, including agriculture. Pressing agricultural resource challenges persist, including those involving irrigation, fertilization, and salinization, due to the complex, coupled, and feedback-driven connectivity of various soil processes that are difficult to manage. Soil moisture dynamics cross-cut these processes and is a logical integration point for generating understanding and new insights for improved agroecosystem management. This paper presents an integrated soil-water-nutrient-plant interaction model (built within a system dynamics framework) with the purpose of replicating soil moisture evolution for a set of unique soils and climates, examining model performance given common irrigation (e.g., frequency and application rates) and crop management considerations (e.g., fertilization, tillage, cover cropping), and evaluating via sensitivity analysis model robustness and quantifying influential management parameters effect on core bio-physical feedbacks at the soil-level. The model has four main state variables (soil moisture, soil nitrogen, soil sodium, and plant canopy cover) that interact dynamically via feedback processes (formulated as coupled partial differential equations) between them. Exogenous variables included precipitation time-series data and required climatic parameters to determine reference (potential) evapotranspiration. The time-unit used from simulation was 1 day (time-step = 0.25) with a simulation horizon of 365 days. The model was calibrated using a variety of sources in the literature and with comparison to observed soil moisture data from four sites in Texas, USA, and evaluated statistically for accuracy (mean bias), precision, (coefficient of determination), and overall fit (Theil inequality statistics). Sensitivity analyses were conducted for a variety of hydroclimate forcing and irrigation, fertilization, and crop management decisions to examine the impacts to soil moisture evolution, soil salinity, and cropping profitability, among other variables. Calibration results showed high degrees of agreement between observed and predicted values (mean r2 = 0.67, mean bias = 0.008%). Sensitivity results demonstrated that precipitation frequency was more influential than precipitation depth in regulating soil moisture, that irrigation threshold (i.e., the soil moisture level inducing irrigation) was the variable most influential to crop profitability (which was maximized at the lowest irrigation threshold value), and that several conservation management strategies (i.e., no-till with residue management or cover cropping) improved soil moisture and crop profitability, contrary to common management perceptions. Several other tests for alternative fertilization, irrigation, and tile drain installation strategies produced results that corroborate common observations of agroecosystem management (i.e., despite environmental risks, crop profitability was enhanced). Future model extensions include expansion of the irrigation, fertilization, and crop management decision making factors to better capture how decisions that respond to economic and policy signals influence resource use and soil system dynamics. Modeling these complex, feedback driven agroecosystems processes remains an arena for future modeling innovations that will support important resource management improvements. [ABSTRACT FROM AUTHOR]