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      Making Robust Policy Decisions Using Global Biodiversity Indicators


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          In order to influence global policy effectively, conservation scientists need to be able to provide robust predictions of the impact of alternative policies on biodiversity and measure progress towards goals using reliable indicators. We present a framework for using biodiversity indicators predictively to inform policy choices at a global level. The approach is illustrated with two case studies in which we project forwards the impacts of feasible policies on trends in biodiversity and in relevant indicators. The policies are based on targets agreed at the Convention on Biological Diversity (CBD) meeting in Nagoya in October 2010. The first case study compares protected area policies for African mammals, assessed using the Red List Index; the second example uses the Living Planet Index to assess the impact of a complete halt, versus a reduction, in bottom trawling. In the protected areas example, we find that the indicator can aid in decision-making because it is able to differentiate between the impacts of the different policies. In the bottom trawling example, the indicator exhibits some counter-intuitive behaviour, due to over-representation of some taxonomic and functional groups in the indicator, and contrasting impacts of the policies on different groups caused by trophic interactions. Our results support the need for further research on how to use predictive models and indicators to credibly track trends and inform policy. To be useful and relevant, scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change.

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          Overexploitation threatens the future of many large vertebrates. In the ocean, tunas and sea turtles are current conservation concerns because of this intense pressure. The status of most shark species, in contrast, remains uncertain. Using the largest data set in the Northwest Atlantic, we show rapid large declines in large coastal and oceanic shark populations. Scalloped hammerhead, white, and thresher sharks are each estimated to have declined by over 75% in the past 15 years. Closed-area models highlight priority areas for shark conservation, and the need to consider effort reallocation and site selection if marine reserves are to benefit multiple threatened species.
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            Human-mediated environmental changes have resulted in appropriate concern for the conservation of ecological systems and have led to the development of many ecological monitoring programs worldwide. Many programs that are identified with the purpose of 'surveillance' represent an inefficient use of conservation funds and effort. Here, we revisit the 1964 paper by Platt and argue that his recommendations about the conduct of science are equally relevant to the conduct of ecological monitoring programs. In particular, we argue that monitoring should not be viewed as a stand-alone activity, but instead as a component of a larger process of either conservation-oriented science or management. Corresponding changes in monitoring focus and design would lead to substantial increases in the efficiency and usefulness of monitoring results in conservation.
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              Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time.

              The growing need for baseline data against which efforts to reduce the rate of biodiversity loss can be judged highlights the importance of long-term datasets, some of which are as old as ecology itself. We review methods of evaluating change in biodiversity at the community level using these datasets, and contrast whole-community approaches with those that combine information from different species and habitats. As all communities experience temporal turnover, one of the biggest challenges is distinguishing change that can be attributed to external factors, such as anthropogenic activities, from underlying natural change. We also discuss methodological issues, such as false alerts and modifications in design, of which users of these data sets need to be aware. Copyright © 2010 Elsevier Ltd. All rights reserved.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                18 July 2012
                : 7
                : 7
                : e41128
                [1 ]Department of Life Sciences, Imperial College London, Berkshire, United Kingdom
                [2 ]School of Botany, the University of Melbourne, Victoria, Australia
                [3 ]Institute of Zoology, Zoological Society of London, London, United Kingdom
                [4 ]Environmental Systems Analysis Lab, University of Padova, Padova, Italy
                [5 ]Department of Animal & Plant Sciences, University of Sheffield, Sheffield, United Kingdom
                [6 ]Department of Mathematics and Statistics, University of Reading, United Kingdom
                [7 ]International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
                [8 ]School of Environment, Natural Resources and Geography, Bangor University, Bangor, United Kingdom
                [9 ]The Australian Research Council Centre of Excellence for Environmental Decisions, The University of Queensland, St Lucia, Queensland, Australia
                University of Durham, United Kingdom
                Author notes

                Conceived and designed the experiments: EN BC EJMG HPP JLB. Performed the experiments: EN BC AB BTC KMES. Analyzed the data: EN BC AB BTC KMES JLB LM. Wrote the paper: EN BC AB BTC KMES FMU HPP EJMG. Conceptual model: EN JLB FMU RWB SF JPGJ HPP EJMG.

                Nicholson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                : 13 December 2011
                : 19 June 2012
                Page count
                Pages: 10
                Research Article
                Ecosystem Modeling
                Conservation Science
                Marine Biology
                Fisheries Science
                Marine Conservation



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