The United Nations 2030 Agenda for Sustainable Development calls for urgent actions to reduce global biodiversity loss. Here, we synthesize >44,000 articles published in the past decade to assess the research focus on global drivers of loss. Relative research efforts on different drivers are not well aligned with their assessed impact, and multiple driver interactions are hardly considered. Research on drivers of biodiversity loss needs urgent realignment to match predicted severity and inform policy goals.
Modelling and monitoring pollutants entering into the Great Barrier Reef (GBR) lagoon remain important priorities for the Australian and Queensland governments. Uncertainty analysis of pollutant load delivery to the GBR would: (1) inform decision makers on their ability to meet environmental targets; (2) identify whether additional measurements are required to make confident decisions; and (3) determine whether investments into remediation activities are actually making a difference to water quality and the health of the GBR. Using a case study from the Upper Burdekin catchment where sediment concentrations are the focus, herein we explore and demonstrate different ways of communicating uncertainty to a decision maker. In particular, we show how exceedance probabilities can identify hot spots for future monitoring or remediation activities and how they can be used to inform target setting activities. We provide recommendations for water quality specialists that allow them to make more informed and scientifically defensible decisions that consider uncertainty in both the monitoring and modelling data, as well as allowing the calculation of exceedances from a threshold.
aMany environmental spatio-temporal processes are best characterized by nonlinear dynamical evolution. Recently, it has been shown that general quadratic nonlinear models provide a very flexible class of parametric models for such processes. However, such models have a very large potential parameter space that must be reduced for most practical applications, even when one considers a reduced rank state process. We provide a parameterization for such models, which is motivated by physical arguments of wave mode interactions in which medium scales influence the evolution of large-scale modes. This parameterization has the potential to improve forecasts in addition to reducing the parameter space. The methodology is illustrated on real-world forecasting problems associated with Pacific sea surface temperature anomalies and mid-latitude sea level pressure.
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