Distribution, demography and dispersal model of spatial spread of invasive plant populations with limited data

Abstract

Summary1. Invasive weeds are a major cause of biodiversity loss and economic damage world-wide. There is often a limited understanding of the biology of emerging invasive species, but delay in action may result in escalating costs of control, reduced economic returns from management actions and decreased feasibility of management. Therefore, spread models that inform and facilitate on-ground control of invasions are needed. 2. We developed a spatially explicit, individual-based spread model that can be applied to both data-poor and data-rich situations to model future spread and inform effective management of the invasion. The model is developed using a minimum of two mapped distributions for the target species at different times, together with habitat suitability variables and basic population data. We present a novel method for internally calibrating the reproduction and dispersal distance parameters. We use a sensitivity analysis to identify variables that should be prioritized in future research to increase robustness of model predictions. 3. We apply the model to two case studies, gamba grass and para grass, to provide management advice on emerging weed priorities in northern Australia. For both species, we find that the current extent of invasion in our study regions is expected to double in the next 10 years in the absence of management actions. The predicted future distribution identifies priority areas for eradication, control and containment to reduce the predicted increase in infestation. 4. The model was built for managers and policymakers in northern Australia working on species where expert knowledge and environmental data are often lacking, but is flexible and can be easily adapted for other situations, for example where good data are available. The model provides predicted probability of occurrence over a user-specified, typically short-term, time horizon. This output can be used to direct surveillance and management actions to areas that have the highest likelihood of rapid invasion and spread. Directing efforts to these areas provides the greatest likelihood of management success and maximizes the return on investment in management response.

Pervasive human‐mediated large‐scale invasion: analysis of spread patterns and their underlying mechanisms in 17 of China's worst invasive plants

“…Common to most theoretical and empirical studies is the basic assumption portraying invasive spread as a spatially continuous process, characterized by the (very) low probabilities of extreme LDD (or jump dispersal) events at the dispersal kernel tail . This basic assumption prevails in both classic (Skellam 1951;Okubo et al 1989;Allen et al 1991) and contemporary literature (Adams et al 2015;Uden et al 2015;Teller, Zhang & Shea 2016). However, the findings of the current study suggest that Fig.…”

Conserving biodiversity and Indigenous bush tucker: Practical application of the strategic foresight framework to invasive alien species management planning

“…For SF Stage 3, we used historical records and weed mapping to develop a model of the future distribution of the two weeds (Figure and Adams et al., ). This revealed areas highly suitable for invasion downstream of current infestations, and on floodplains currently free of large infestations (e.g., South Alligator).…”

“…We developed a generalized, spatially explicit cellular automata approach to link an existing dynamic spread model (Adams et al., ) to weed growth and management models (Table and Supplementary Materials). The model has a user‐friendly interface, is easily parameterized for any invasive plant species, and can accommodate an unlimited number of species for management.…”

Incorporating movement in species distribution models

“…However, with the emergence of lifetime telemetry data (Kays et al 2015), and the potential of individual-based models to simulate the inherent relationship between movement and the environment, spatial simulation models are increasingly being used to understand animal movement, and are beginning to be observed within the SDM framework (Martinez et al 2012;Adams et al 2015). For example, Martinez et al…”

“…Similarly, Adams et al (2015) used an individual-based cellular automata model based on dispersal constraints to model invasive spread of a non-native weed in Australia over a period of five to ten years.…”