Global Biodiversity Target Missed: In 2002, the Convention on Biological Diversity (CBD) committed to a significant reduction in the rate of biodiversity loss by 2010. There has been widespread conjecture that this target has not been met. Butchart et al. (p. 1164 , published online 29 April) analyzed over 30 indicators developed within the CBD's framework. These indicators include the condition or state of biodiversity (e.g., species numbers, population sizes), the pressures on biodiversity (e.g., deforestation), and the responses to maintain biodiversity (e.g., protected areas) and were assessed between about 1970 and 2005. Taken together, the results confirm that we have indeed failed to meet the 2010 targets.

Developing countries share many common challenges in addressing current and future climate risks. A key barrier to managing these risks is the limited availability of accessible, reliable and relevant weather and climate information. Despite continued investments in Earth System Modelling, and the growing provision of climate services across Africa and India, there often remains a mismatch between available information and what is needed to support on-the-ground decision-making. In this paper, we outline the range of currently available information and present examples from Africa and India to demonstrate the challenges in meeting information needs in different contexts. A review of literature supplemented by interviews with experts suggests that externally provided weather and climate information has an important role in building on local knowledge to shape understanding of climate risks and guide decision-making across scales. Moreover, case studies demonstrate that successful decision-making can be achieved with currently available information. However, these successful examples predominantly use daily, weekly and seasonal climate information for decision-making over short time horizons. Despite an increasing volume of global and regional climate model simulations, there are very few clear examples of long-term climate information being used to inform decisions at sub-national scales. We argue that this is largely because the information produced and disseminated is often ill-suited to inform decision-making at the local scale, particularly for farmers, pastoralists and sub-national governments. Even decision-makers involved in long-term planning, such as national government officials, find it difficult to plan using decadal and multi-decadal climate projections because of issues around uncertainty, risk averseness and constraints in justifying funding allocations on prospective risks. Drawing on lessons learnt from recent successes and failures, a framework is proposed to help increase the utility and uptake of both current and future climate information across Africa and India.

In recent years there has been a growing number of academic reviews discussing the theme of transformation and its association with adaptation to climate change. On the one hand this has stimulated exchange of ideas and perspectives on the parameters of transformation, but it has also given rise to confusion in terms of identifying what constitutes a non-incremental form of adaptation on the ground. What this article aims to do instead is help researchers and practitioners relate different interpretations of transformation to practice by proposing a typological framework for categorising forms of change that focuses on mechanisms and objectives. It then discusses how these categorisations link to the broader conceptions and critiques noted above, with the idea that this will enable those who seek to analyse or plan adaptation to better analyse what types of action are potentially constitutive of transformation. In doing so, it should equally assist in the identification and specification of critical questions that need to be asked of such activity in relation to issues of sustainability and equity.

Lists of invasive alien species (IAS) are essential for preventing, controlling, and reporting on the state of biological invasions. However, these lists suffer from a range of errors, with serious consequences for their use in science, policy, and management. Here we (1) collated and classified errors in IAS listing using a taxonomy of uncertainty; and (2) estimated the size of these errors using data from a completed listing exercise, with the purpose of better understanding, communicating, and dealing with them. Ten errors were identified. Most result from a lack of knowledge or measurement error (epistemic uncertainty), although two were a result of context dependence and vagueness (linguistic uncertainty). Estimates of the size of the effects of these errors were substantial in a number of cases and unknown in others. Most errors, and those with the largest estimated effect, result in underestimates of IAS numbers. However, there are a number of errors where the size and direction of the effect remains poorly understood.The effect of differences in opinion between specialists is potentially large, particularly for data‐poor taxa and regions, and does not have a clearly directional or consistent effect on the size and composition of IAS lists. Five tactics emerged as important for reducing uncertainty in IAS lists, and while uncertainty will never be removed entirely, these approaches will significantly improve the transparency, repeatability, and comparability of IAS lists. Understanding the errors and uncertainties that occur during the process of listing invasive species, as well as the potential size and nature of their effects on IAS lists, is key to improving the value of these lists for governments, management agencies, and conservationists. Such understanding is increasingly important given positive trends in biological invasion and the associated risks to biodiversity and biosecurity.