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

Sustainable synthesis of bionanomaterials using non-native plant extracts for maintaining ecological balance: A computational bibliography analysis

Biological approaches via biomolecular extracts of bacteria, fungi, or plants have recently been introduced as an alternative approach to synthesizing less or nontoxic nanomaterials, compared to conventional physical and chemical approaches. Among these biological methods, plant-mediated approaches (phytosynthesis) are reported to be highly beneficial for large-scale, nontoxic nanomaterial synthesis. However, plant-mediated synthesis of nanomaterials using native plant extract can lead to bioprospecting issues and deforestation challenges. On the other hand, non-native or invasive plants are non-indigenous to a particular geographic location that can grow and spread rapidly, ultimately disrupting the local and endogenous plant communities or ecosystems. Thus, controlling or eradicating these non-native plants before they damage the ecosystem is necessary. Even though mechanical, chemical, and biological approaches are available to control non-native plants, all these methods possess certain limitations, such as environmental toxicity, disturbance in the nutrient cycle, and loss of genetic integrity. Therefore, non-native plants were recently proposed as a novel sustainable source of phytochemicals for preparing nanomaterials via green chemistry, mainly metallic nanoparticles, as an alternative to native, agriculture-based, or medicinal plants. This work aims to cover a literature gap on plant-mediated bionanomaterial synthesis with an overview and bibliography analysis of non-native plants via novel data mining and advanced visualization tools. In addition, the potential of non-native plants as a sustainable, green chemistry-based alternative for bionanomaterial preparation for maintaining ecological balance, the mechanism of formation via phytochemicals, and their possible applications to promote their control and spread were also discussed. The bibliography analysis revealed that only an average of 4 articles have been published in the last 10 years (2013-2023) on non-native/invasive plants for nanomaterial synthesis, which shows the significance of this article.

Regional models do not outperform continental models for invasive species

Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents.

Location: Continental United States.

Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions.

Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response.

Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.

A framework to integrate innovations in invasion science for proactive management

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

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.

Identifying Policy-relevant Indicators for Assessing Landscape Vegetation Patterns to Inform Planning and Management on Multiple-use Public Lands

Understanding the structure and composition of landscapes can empower agencies to effectively manage public lands for multiple uses while sustaining land health. Many landscape metrics exist, but they are not often used in public land decision-making. Our objectives were to (1) develop and (2) apply a process for identifying a core set of indicators that public land managers can use to understand landscape-level resource patterns on and around public lands. We first developed a process for identifying indicators that are grounded in policy, feasible to quantify using existing data and resources, and useful for managers. We surveyed landscape monitoring efforts by other agencies, gathered science and agency input on monitoring goals, and quantified the prevalence of potential indicators in agency land health standards to identify five landscape indicators: amount, distribution, patch size, structural connectivity, and diversity of vegetation types. We then conducted pilot applications in four bureau of land management (BLM) field offices in Arizona, California, and Colorado to refine procedures for quantifying the indicators and assess the utility of the indicators for managers. Results highlighted the dominance of upland and the limited extent of riparian/wetland vegetation communities, moderate connectivity of priority vegetation patches, and lower diversity of native vegetation types on BLM compared to non-BLM lands. Agency staff can use the indicators to inform the development of quantitative resource management objectives in land use plans, evaluate progress in meeting those objectives, quantify potential impacts of proposed actions, and as a foundation for an all-lands approach to landscape-level management across public lands.