Policy-makers and managers in natural resource management (NRM) often complain that researchers are out of touch. Researchers often complain that policy-makers and managers make poorly informed decisions. In this article, we report on a meeting between researchers, policy-makers and managers convened to identify practical solutions to improve engagement between these camps. A necessary starting point is that every researcher and policy-maker should understand, and tap into, the motivations and reward systems of the other when seeking engagement. For example, researchers can be motivated to engage in policy development if there is a promise of outputs that align with their reward systems such as co-authored publications. Successful research-policy partnerships are built around personal relationships. As a researcher, you cannot therefore expect your results to inform policy by only publishing in journals. As a policy-maker, you cannot guarantee engagement from researchers by publicly inviting comment on a document. Actively building and maintaining relationships with key individuals through discussions, meetings, workshops or field days will increase the likelihood that research outcomes will inform policy decisions. We identified secondments, sabbaticals, fellowships and 'buddies', an annual national NRM conference and 'contact mapping' (a Facebook-type network) as forums that can catalyse new relationships between researchers and policy-makers. We challenge every researcher, policy-maker and manager in NRM to build one new cross-cultural relationship each year.

Effective biodiversity monitoring is critical to evaluate, learn from, and ultimately improve conservation practice. Well conceived, designed and implemented monitoring of biodiversity should: (i) deliver information on trends in key aspects of biodiversity (e.g. population changes); (ii) provide early warning of problems that might otherwise be difficult or expensive to reverse; (iii) generate quantifiable evidence of conservation successes (e.g. species recovery following management) and conservation failures; (iv) highlight ways to make management more effective; and (v) provide information on return on conservation investment. The importance of effective biodiversity monitoring is widely recognized (e.g. Australian Biodiversity Strategy). Yet, while everyone thinks biodiversity monitoring is a good idea, this has not translated into a culture of sound biodiversity monitoring, or widespread use of monitoring data. We identify four barriers to more effective biodiversity monitoring in Australia. These are: (i) many conservation programmes have poorly articulated or vague objectives against which it is difficult to measure progress contributing to design and implementation problems; (ii) the case for long-term and sustained biodiversity monitoring is often poorly developed and/or articulated; (iii) there is often a lack of appropriate institutional support, co-ordination, and targeted funding for biodiversity monitoring; and (iv) there is often a lack of appropriate standards to guide monitoring activities and make data available from these programmes. To deal with these issues, we suggest that policy makers, resource managers and scientists better and more explicitly articulate the objectives of biodiversity monitoring and better demonstrate the case for greater investments in biodiversity

AimStrongly interacting species have disproportionately large ecological effects relative to their abundances or biomass. We previously developed two conceptual models that described how one such strong interactor, the Australian bird the noisy miner Manorina melanocephala: (1) establishes resident high‐density and hyperaggressive colonies and (2) in doing so, affects other biota and ecosystem processes. Here, we evaluate parts of those models relating to noisy miner habitat preferences and effects on bird assemblages using data from across the geographical range of the miner.LocationEastern Australia.MethodsAvian‐assemblage data were compiled for 2 128 survey transects (distributed over > 1.3 × 106 km2) and were linked to variables reflecting productivity, local habitat structure and landscape context. Predictors were chosen based on the models, although detailed data for some variables were unavailable at such large scales. We used hierarchical Bayesian models that included observation models to account for different survey effort coupled with potentially nonlinear, spatially‐explicit process models.ConclusionsNoisy miner densities increased with proximity to forest edges (higher densities on forest edges and open sites), in low rainfall areas, and in vegetation dominated by trees with blade‐shaped rather than needle‐shaped leaves. The presence of noisy miners at even relatively small densities (> 0.6 individuals ha−1) depressed both species richness and the abundances of smaller (< 63 g) bird species, by 50% on average. There were positive associations between densities of noisy miners and the abundance and richness of larger‐bodied (> 63 g) bird species. In areas with higher mean rainfall, the associations between noisy miners and small‐ and large‐bird species were more negative and less positive, respectively.

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