Every widespread, damaging invasive plant species in the world exists across property boundaries. As a result management of damaging invasive plants (i.e. weeds) is often undertaken by multiple decision makers, each managing only a small part of the invader’s population. This makes it imperative to understand how human behavior affects coordination of local management efforts, and subsequently persistence and spread of weed species at landscape scales. Using a spatially explicit agent based simulation we determined how individual manager behavior, in concert with weed population ecology, determined weed prevalence at landscape scales. We use two wide spread invasive grass species, Nassella trichotoma (serrated tussock) and Eragrostis curvula (African love grass) as examples. Both are weeds in south eastern Australian grazing systems, where weed control is generally undertaken by each property manager only on their own property.
Results/Conclusions
A strong response to benefits by agents synchronized their actions, greatly reducing the pool of infested agents available to spread the weed, and thus reducing weed prevalence. As N. trichotoma was more damaging than E. curvula and had more effective control methods, agents chose to manage it more often resulting in its lower prevalence. A relatively low number of agents that were intrinsically less motivated to control weeds could lead to the whole landscape being infested, even when local control stopped new infestations, particularly when there was a lot of long distance dispersal. Social pressure was important, but only if it was independent of weed prevalence, suggesting that early access to information, and incentives to act on that information, may be crucial in stopping a weed infesting large areas. Our results show that the behavior of individual land mangers could have a large effect on a weeds extent at the landscape scale, even if each individual property manager only had access to a small part of that landscape. Further, the response of our model to both behavioral and ecological parameters was highly non-linear, suggesting that a small change in either could have a disproportionally large effect.