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.

Past and present Missouri River management practices have resulted in native fishes being identified as in jeopardy. In 1995, the Missouri River Benthic Fishes Study was initiated to provide improved information on Missouri River fish populations and how alterations might affect them. The study produced a baseline against which to evaluate future changes in Missouri River operating criteria. The objective was to evaluate population structure and habitat use of benthic fishes along the entire mainstem Missouri River, exclusive of reservoirs. Here we use the data from this study to provide a recent‐past baseline for on‐going Missouri River fish population monitoring programmes along with a more powerful method for analysing data containing large percentages of zero values. This is carried out by describing the distribution and habitat use of 21 species of Missouri River benthic fishes based on catch‐per‐unit area data from multiple gears. We employ a Bayesian zero‐inflated Poisson model expanded to include continuous measures of habitat quality (i.e. substrate composition, depth, velocity, temperature, turbidity and conductivity). Along with presenting the method, we provide a relatively complete picture of the Missouri River benthic fish community and the relationship between their relative population numbers and habitat conditions. We demonstrate that our single model provides all the information that is often obtained by a myriad of analytical techniques. An important advantage of the present approach is reliable inference for patterns of relative abundance using multiple gears without using gear efficiencies. Copyright © 2011 John Wiley & Sons, Ltd.

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.