Communicating vegetation management science to land managers and other stakeholders

Science communication. including extension services. plays a key role in achieving sustainable native vegetation management. One of the pivotal aspects of the debate on sustainable vegetation management is the scientific information underpinning policy-making. In recent years. extension services have Shifted their focus from top-down technology transfer to bottom-up participation and empowerment. I here has also been a broadening of communication strategies to recognise the range of stakeholders involved in native vegetation management and to encompass environmental concerns. This paper examines the differences between government approaches to extension services to deliver policy and the need for effective communication to address broader science issues that underpin native vegetation management. The importance of knowing the learning styles of the stakeholders involved in native vegetation management is discussed at a time of increasing reliance on mass communication for information exchange and the importance of personal communication to achieve on-ground sustainable management. Critical factors for effective science-management communication are identified Such as: (i) undertaking scientific studies (research) with community involvement, acceptance and agreed understanding of project objectives (ii) realistic community consultation periods: (iii) matching communication channels with stakeholder needs; (iv) combining scientific with local knowledge in in holistic (biophysical and social) approach to understanding in issued and (v) regional partnerships. These communication factors are considered to be essential to implementing on-ground natural resource management strategics and actions, including those concerned with native vegetation management.

Input-driven Language Learning

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.