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

Autor: Helen R. Sofaer, Ian S. Pearse, Thomas J. Stohlgren, Nicholas E. Young, Peder Engelstad, Julia Sullivan, Catherine S. Jarnevich
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0106 biological sciences
Computer science
Invasive Species
Marine and Aquatic Sciences
Introduced species
computer.software_genre
01 natural sciences
Invasive species
Workflow
Machine Learning
Automation
Data Management
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Eukaryota
Plants
Habitats
Habitat suitability
Variable (computer science)
Habitat
Physical Sciences
Plant species
Engineering and Technology
Medicine
Algorithms
Research Article
Freshwater Environments
Computer and Information Sciences
Conservation of Natural Resources
Pennisetum
Science
Research and Analysis Methods
Machine learning
Risk Assessment
010603 evolutionary biology
Decision Support Techniques
Set (abstract data type)
Machine Learning Algorithms
Species Colonization
Artificial Intelligence
Humans
Grasses
Models
Statistical
business.industry
010604 marine biology & hydrobiology
Ecology and Environmental Sciences
Organisms
Biology and Life Sciences
Aquatic Environments
Bodies of Water
Control Engineering
Field (geography)
Lakes
Earth Sciences
Artificial intelligence
Introduced Species
business
computer
Mathematics
Apiaceae
Zdroj: PLoS ONE, Vol 15, Iss 3, p e0229253 (2020)
PLoS ONE
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
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