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Invasive alien plant species threaten agriculture and biodiversity globally and require ongoing management to minimise impacts. However, the large number of invasive species means that a risk‐based approach to prioritisation is needed, taking into account the spatial scale of management decisions and myriad of available information. Here, we developed a risk‐based inventory of invasive plants in Queensland, Australia, using both current species distribution/abundance and the severity of their impacts. Our assessment followed a comprehensive data collection process including a scoping of local government pest management plans, herbarium records, the published literature and structured elicitation of expert knowledge during a series of regional stakeholder workshops. From ~300 plant species that were identified as established and/or emerging invaders in the State, only one‐third were considered by practitioners to pose significant risks across regions to be considered management priorities. We aggregated regional species lists into a statewide priority list and analysed the data set (107 species) for historical, geographical, floristic and ecological patterns. Regions on the mainland eastern seaboard of the State share similar invasive plant communities, suggesting that these regions may form a single management unit, unlike the western/inland and the extreme far north (Torres Strait Islands) regions, which share fewer invasive plant species. Positive correlations were detected between invasiveness and time since introduction for some but not all plant life forms. Stakeholders identified research and management priorities for the invasive plant list, including biological control options, public awareness/education, effective herbicide use, ecology/taxonomy and risk analysis. In the course of the exercise, a statewide invasive plant priority list of high‐, medium‐ and low‐impact scores for policy, research and management was compiled. Finally, our approach to invasive plant species prioritisation highlighted that planning and policy documents are not necessarily reflected at the grass‐root level in terms of species identity and management priorities. [ABSTRACT FROM AUTHOR]

Managing and monitoring invasive alien species (IAS) is costly, and because resources are limited, prioritization decisions are required for planning and management. We present findings on plant pest prioritization for 63 established invader species of natural and grazing ecosystems of Queensland, Australia. We used an expert elicitation approach to assess risk (species occurrence, spread, and impact) and feasibility of control for each IAS. We elicit semi-quantitative responses from diverse expert stakeholders to score IAS on three management approaches (biocontrol, chemical and mechanical) in relation to cost, effectiveness and practicality, and incorporate uncertainty in expert inputs and model outputs. In the process, we look for promising management opportunities as well as seek general trends across species' ecological groups and management methods. Stakeholders were cautiously optimistic about the feasibility of managing IAS. Taking into consideration all factors, the overall feasibility of control was uncorrelated with the stakeholders' level of confidence. However, within individual management criterion, positive trend was observed for the same bivariate traits for chemical control, and negative trends for biocontrol and mechanical controls. Utility and confidence in IAS management options were in the order: chemical > biocontrol = mechanical, with practicality and effectiveness being the main driver components. Management feasibility differed significantly between IAS life forms but not between habitats invaded. Lastly, we combined IAS risk assessment and management feasibility scores to create a risk matrix to guide policy goals (i.e. eradication, spread containment, protection of sensitive sites, targeted control, site management, monitoring, and limited action). The matrix identifies promising species to target for each of these policy outcomes. Overall, our general approach illustrates (i) the importance of understanding the feasibility of IAS control actions and the factors that drive it, and (ii) demonstrates how quantifying management feasibility can be used to enhance traditional risk assessment rankings to improve policy outcomes. • Managing invasive alien species (e.g. weeds) is costly, so prioritization is needed. • Experts assessed feasibility of control methods for weeds of Queensland, Australia. • Chemical control was most effective, followed by biological and mechanical controls. • Practicality and effectiveness were the drivers of control feasibility, not the cost. • Results are linked to on-ground decision actions using a risk assessment matrix. [ABSTRACT FROM AUTHOR]

Invasive wildlife often causes serious damage to the economy and agriculture as well as environmental, human and animal health. Habitat models can fill knowledge gaps about species distributions and assist planning to mitigate impacts. Yet, model accuracy and utility may be compromised by small study areas and limited integration of species ecology or temporal variability. Here we modelled seasonal habitat suitability for wild pigs, a widespread and harmful invader, in northern Australia. We developed a resource-based, spatially-explicit and regional-scale approach using Bayesian networks and spatial pattern suitability analysis. We integrated important ecological factors such as variability in environmental conditions, breeding requirements and home range movements. The habitat model was parameterized during a structured, iterative expert elicitation process and applied to a wet season and a dry season scenario. Model performance and uncertainty was evaluated against independent distributional data sets. Validation results showed that an expert-averaged model accurately predicted empirical wild pig presences in northern Australia for both seasonal scenarios. Model uncertainty was largely associated with different expert assumptions about wild pigs’ resource-seeking home range movements. Habitat suitability varied considerably between seasons, retracting to resource-abundant rainforest, wetland and agricultural refuge areas during the dry season and expanding widely into surrounding grassland floodplains, savanna woodlands and coastal shrubs during the wet season. Overall, our model suggested that suitable wild pig habitat is less widely available in northern Australia than previously thought. Mapped results may be used to quantify impacts, assess risks, justify management investments and target control activities. Our methods are applicable to other wide-ranging species, especially in data-poor situations. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are usually based on a single data type, such as presence‐only (PO), presence‐absence (PA) or abundance (AA). Results from SDMs using single sources of data will suffer from inherent biases and limitations to that data type. For example, PO data contain sampling‐bias and PA/AA data are often less expansive and more sparse. Integrated SDMs (ISDMs) combine multiple data types and have recently emerged as a way to leverage strengths and minimise weaknesses of the different data types. They pose a common (distribution) model and separate observation models for each of the data types. The 'RISDM' package for the R environment (www.r‐project.org) provides access to this modelling framework using functions for preparation, fitting, interpreting and diagnosing models. The functionality of the package is demonstrated here using synthetic data sets. [ABSTRACT FROM AUTHOR]