Dataset Information

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales.

ABSTRACT: Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

SUBMITTER: Young NE

PROVIDER: S-EPMC7062246 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales.

Young Nicholas E NE Jarnevich Catherine S CS Sofaer Helen R HR Pearse Ian I Sullivan Julia J Engelstad Peder P Stohlgren Thomas J TJ

PloS one 20200309 3

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed ...[more]

PMID: 32150554

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Project description:In public health research, it has been well established that geographic location plays an important role in influencing health outcomes. In recent years, there has been an increased emphasis on the impact of neighborhood or contextual factors as potential risk factors for childhood obesity. Some neighborhood factors relevant to childhood obesity include access to food sources, access to recreational facilities, neighborhood safety, and socioeconomic status (SES) variables. It is common for neighborhood or area-level variables to be available at multiple spatial scales (SS) or geographic units, such as the census block group and census tract, and selection of the spatial scale for area-level variables can be considered as a model selection problem. In this paper, we model the variation in body mass index (BMI) in a study of pediatric patients of the Virginia Commonwealth University (VCU) Medical Center, while considering the selection of spatial scale for a set of neighborhood-level variables available at multiple spatial scales using four recently proposed spatial scale selection algorithms: SS forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso. For pediatric BMI, we found evidence of significant positive associations with visit age and black race at the individual level, percent Hispanic white at the census block group level, percent Hispanic black at the census tract level, and percent vacant housing at the census tract level. We also found significant negative associations with population density at the census tract level, median household income at the census tract level, percent renter at the census tract level, and exercise equipment expenditures at the census block group level. The SS algorithms selected covariates at different spatial scales, producing better goodness-of-fit in comparison to traditional models, where all area-level covariates were modeled at the same scale. These findings underscore the importance of considering spatial scale when performing model selection.

Project description:The natural recolonization of native plant communities following invasive species management is notoriously challenging to predict, since outcomes can be contingent on a variety of factors including management decisions, abiotic factors, and landscape setting. The spatial scale at which the treatment is applied can also impact management outcomes, potentially influencing plant assembly processes and treatment success. Understanding the relative importance of each of these factors for plant community assembly can help managers prioritize patches where specific treatments are likely to be most successful. Here, using effects size analyses, we evaluate plant community responses following four invasive Phragmites australis management treatments (1: fall glyphosate herbicide spray, 2: summer glyphosate herbicide spray, 3: summer imazapyr herbicide spray, 4: untreated control) applied at two patch scales (12,000 m2 and 1,000 m2) and monitored for 5 years. Using variation partitioning, we then evaluated the independent and shared influence of patch scale, treatment type, abiotic factors, and landscape factors on plant community outcomes following herbicide treatments. We found that Phragmites reinvaded more quickly in large patches, particularly following summer herbicide treatments, while native plant cover and richness increased at a greater magnitude in small patches than large. Patch scale, in combination with abiotic and landscape factors, was the most important driver for most plant responses. Compared with the small plots, large patches commonly had deeper and more prolonged flooding, and were in areas with greater hydrologic disturbance in the landscape, factors associated with reduced native plant recruitment and greater Phragmites cover. Small patches were associated with less flooding and landscape disturbance, and more native plants in the surrounding landscape than large patches, factors which promoted higher native plant conservation values and greater native plant cover and richness. Herbicide type and timing accounted for very little of the variation in native plant recovery, emphasizing the greater importance of patch selection for better management outcomes. To maximize the success of treatment programs, practitioners should first manage Phragmites patches adjacent to native plant species and in areas with minimal hydrologic disturbance.

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Project description:ImportanceLow- and middle-income countries have a high burden of respiratory syncytial virus lower respiratory tract infections. A monoclonal antibody administered monthly is licensed to prevent these infections, but it is cost-prohibitive for most low- and middle-income countries. Long-acting monoclonal antibodies and maternal vaccines against respiratory syncytial virus are under development.ObjectiveWe estimated the likelihood of respiratory syncytial virus preventive interventions (current monoclonal antibody, long-acting monoclonal antibody, and maternal vaccine) being cost-effective in Mali.DesignWe modeled age-specific and season-specific risks of respiratory syncytial virus lower respiratory tract infections within monthly cohorts of infants from birth to six months. We parameterized with respiratory syncytial virus data from Malian cohort studies, as well as product efficacy from clinical trials. Integrating parameter uncertainty, we simulated health and economic outcomes for status quo without prevention, intra-seasonal monthly administration of licensed monoclonal antibody, pre-seasonal birth dose administration of a long-acting monoclonal antibody, and maternal vaccination. We then calculated the incremental cost-effectiveness ratio of each intervention compared to status quo from the perspectives of the government, donor, and society.ResultsAt a price of $3 per dose and from the societal perspective, current monoclonal antibody, long-acting monoclonal antibody, and maternal vaccine would have incremental cost-effectiveness ratios of $4280 (95% CI $1892 to $122,434), $1656 (95% CI $734 to $9091), and $8020 (95% CI $3501 to $47,047) per disability-adjusted life-year averted, respectively.Conclusions and relevanceIn Mali, long-acting monoclonal antibody is likely to be cost-effective from both the government and donor perspectives at $3 per dose. Maternal vaccine would need higher efficacy over that measured by a recent trial in order to be considered cost-effective.

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A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales.

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A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales.

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