Simple network models integrate global change, social dynamics and management interventions in biosecurity scenario analysis

Global change and public participation are both areas of considerable uncertainty in estimating the success of biosecurity response strategies but are poorly integrated in most available models. We introduce INApest() , a novel network simulation method which integrates social and global change factors, as well as pest biology and multiple management variables in scenario analyses of biosecurity responses. INApest() separates the management response into four key parameters: probability of detection; management adoption; eradication of local populations; spread reduction (e.g. through movement restrictions or hygiene measures). It also permits simulation of biosecurity responses which evolve organically as new incidences of the pest are detected and information about the pest and management technologies spread through the network. We demonstrate selected functionality of INApest() using Nassella neesiana (Chilean Needle Grass; CNG), a slow-spreading pasture weed that impacts animal health, as a case-study. Realistic historical CNG spread rates are reproduced under a no management scenario using dispersal kernels derived from known natural and human mediated spread mechanisms. Scenario analyses comparing over 15,000 parameter combinations reveal that communication of invasive threat to farms neighbouring known infestations significantly reduces the management efficacy (farm-scale eradication probability and spread reduction) required for successful containment. We use targeted simulation experiments to show how INApest() permits assessment of cross-border consequences of local management decisions, and the effect of communication between landowners on management success. INApest() has the potential to be used at multiple scales and to explore a wide range of management, global change and social scenarios.