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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]

When an invasive alien 'weed' emerges in a previously uninhabited landscape, land managers must respond quickly to facilitate effective eradication or containment, and minimize long‐term negative impacts. However, on‐ground management decisions are often made under time, knowledge and capacity constraints. Spatially explicit tools for assessing invasion risk rapidly and under uncertainty would help land managers to better target interventions.We developed a generic methodology that integrates (a) interactions between ecological risk factors and invasion processes affecting both the potential suitability for population growth and the actual susceptibility to propagule introduction from source populations with (b) spatially explicit data in (c) a probabilistic Bayesian network modelling framework. Our methods focused on the operational needs of land mangers responding to weed incursions, streamlining data and knowledge collection, simplifying model calibration, and facilitating adoption via a collection of user‐friendly web apps called riskmapr.We tested the generality of our methodology on two contrasting weeds (the rainforest tree Cecropia spp. and the cactus Cylindropuntia rosea) that are targeted for containment and local eradication in Queensland, Australia. Case study models were calibrated from published knowledge about abiotic and biotic factors affecting suitability for, and susceptibility to, weed invasion. Validation of annual risk maps against weed detections in subsequent years showed that models accurately predicted the field‐observed progression of each invasion to date.We developed a rapid spatial risk modelling methodology that is theoretically comprehensive and practically simple. Our streamlined methods and open access implementation using riskmapr facilitate adoption by land managers. Models and risk maps can be used to target interventions or improve spatially explicit understanding of the risk factors and processes driving early weed invasions. [ABSTRACT FROM AUTHOR]

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]

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]