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      Protecting Important Sites for Biodiversity Contributes to Meeting Global Conservation Targets

      1 , 2 , * , 2 , 1 , 3 , 4 , 5 , 1 , 1 , 2 , 2 , 6 , 7 , 8 , 9 , 1 , 10 , 11 , 12 , 13 , 1 , 14 , 15 , 13 , 16 , 17 , 1 , 2 , 18 , 1 , 19 , 2 , 15 , 20 , 21 , 15 , 18 , 11 , 1 , 22 , 23 , 23 , 23 , 24 , 25 , 26 , 24 , 2 , 15 , 20 , 27 , 28 , 15 , 29
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          Protected areas (PAs) are a cornerstone of conservation efforts and now cover nearly 13% of the world's land surface, with the world's governments committed to expand this to 17%. However, as biodiversity continues to decline, the effectiveness of PAs in reducing the extinction risk of species remains largely untested. We analyzed PA coverage and trends in species' extinction risk at globally significant sites for conserving birds (10,993 Important Bird Areas, IBAs) and highly threatened vertebrates and conifers (588 Alliance for Zero Extinction sites, AZEs) (referred to collectively hereafter as ‘important sites’). Species occurring in important sites with greater PA coverage experienced smaller increases in extinction risk over recent decades: the increase was half as large for bird species with>50% of the IBAs at which they occur completely covered by PAs, and a third lower for birds, mammals and amphibians restricted to protected AZEs (compared with unprotected or partially protected sites). Globally, half of the important sites for biodiversity conservation remain unprotected (49% of IBAs, 51% of AZEs). While PA coverage of important sites has increased over time, the proportion of PA area covering important sites, as opposed to less important land, has declined (by 0.45–1.14% annually since 1950 for IBAs and 0.79–1.49% annually for AZEs). Thus, while appropriately located PAs may slow the rate at which species are driven towards extinction, recent PA network expansion has under-represented important sites. We conclude that better targeted expansion of PA networks would help to improve biodiversity trends.

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          High and Far: Biases in the Location of Protected Areas

          Background About an eighth of the earth's land surface is in protected areas (hereafter “PAs”), most created during the 20th century. Natural landscapes are critical for species persistence and PAs can play a major role in conservation and in climate policy. Such contributions may be harder than expected to implement if new PAs are constrained to the same kinds of locations that PAs currently occupy. Methodology/Principal Findings Quantitatively extending the perception that PAs occupy “rock and ice”, we show that across 147 nations PA networks are biased towards places that are unlikely to face land conversion pressures even in the absence of protection. We test each country's PA network for bias in elevation, slope, distances to roads and cities, and suitability for agriculture. Further, within each country's set of PAs, we also ask if the level of protection is biased in these ways. We find that the significant majority of national PA networks are biased to higher elevations, steeper slopes and greater distances to roads and cities. Also, within a country, PAs with higher protection status are more biased than are the PAs with lower protection statuses. Conclusions/Significance In sum, PAs are biased towards where they can least prevent land conversion (even if they offer perfect protection). These globally comprehensive results extend findings from nation-level analyses. They imply that siting rules such as the Convention on Biological Diversity's 2010 Target [to protect 10% of all ecoregions] might raise PA impacts if applied at the country level. In light of the potential for global carbon-based payments for avoided deforestation or REDD, these results suggest that attention to threat could improve outcomes from the creation and management of PAs.
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            Measuring the effectiveness of protected area networks in reducing deforestation.

            Global efforts to reduce tropical deforestation rely heavily on the establishment of protected areas. Measuring the effectiveness of these areas is difficult because the amount of deforestation that would have occurred in the absence of legal protection cannot be directly observed. Conventional methods of evaluating the effectiveness of protected areas can be biased because protection is not randomly assigned and because protection can induce deforestation spillovers (displacement) to neighboring forests. We demonstrate that estimates of effectiveness can be substantially improved by controlling for biases along dimensions that are observable, measuring spatial spillovers, and testing the sensitivity of estimates to potential hidden biases. We apply matching methods to evaluate the impact on deforestation of Costa Rica's renowned protected-area system between 1960 and 1997. We find that protection reduced deforestation: approximately 10% of the protected forests would have been deforested had they not been protected. Conventional approaches to evaluating conservation impact, which fail to control for observable covariates correlated with both protection and deforestation, substantially overestimate avoided deforestation (by over 65%, based on our estimates). We also find that deforestation spillovers from protected to unprotected forests are negligible. Our conclusions are robust to potential hidden bias, as well as to changes in modeling assumptions. Our results show that, with appropriate empirical methods, conservation scientists and policy makers can better understand the relationships between human and natural systems and can use this to guide their attempts to protect critical ecosystem services.
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              A standard lexicon for biodiversity conservation: unified classifications of threats and actions.

              An essential foundation of any science is a standard lexicon. Any given conservation project can be described in terms of the biodiversity targets, direct threats, contributing factors at the project site, and the conservation actions that the project team is employing to change the situation. These common elements can be linked in a causal chain, which represents a theory of change about how the conservation actions are intended to bring about desired project outcomes. If project teams want to describe and share their work and learn from one another, they need a standard and precise lexicon to specifically describe each node along this chain. To date, there have been several independent efforts to develop standard classifications for the direct threats that affect biodiversity and the conservation actions required to counteract these threats. Recognizing that it is far more effective to have only one accepted global scheme, we merged these separate efforts into unified classifications of threats and actions, which we present here. Each classification is a hierarchical listing of terms and associated definitions. The classifications are comprehensive and exclusive at the upper levels of the hierarchy, expandable at the lower levels, and simple, consistent, and scalable at all levels. We tested these classifications by applying them post hoc to 1191 threatened bird species and 737 conservation projects. Almost all threats and actions could be assigned to the new classification systems, save for some cases lacking detailed information. Furthermore, the new classification systems provided an improved way of analyzing and comparing information across projects when compared with earlier systems. We believe that widespread adoption of these classifications will help practitioners more systematically identify threats and appropriate actions, managers to more efficiently set priorities and allocate resources, and most important, facilitate cross-project learning and the development of a systematic science of conservation.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                21 March 2012
                : 7
                : 3
                [1 ]BirdLife International, Cambridge, United Kingdom
                [2 ]United Nations Environment Programme World Conservation Monitoring Centre, Cambridge, United Kingdom
                [3 ]National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
                [4 ]United Nations Educational, Scientific and Cultural Organization, Paris, France
                [5 ]BirdLife International Africa Partnership Secretariat, Nairobi, Kenya
                [6 ]The Nature Conservancy, Arlington, Virginia, United States of America
                [7 ]NatureServe, Arlington, Virginia, United States of America
                [8 ]World Agroforestry Center, International Center for Research in Agroforestry, University of the Philippines, Los Baños, Philippines
                [9 ]School of Geography and Environmental Studies, University of Tasmania, Hobart, Australia
                [10 ]Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen, Copenhagen, Denmark
                [11 ]Conservation Science Program, World Wildlife Fund, Washington, District of Columbia, United States of America
                [12 ]BirdLife International Asia Regional Office, Tokyo, Japan
                [13 ]BirdLife International Americas Secretariat, Quito, Ecuador
                [14 ]Secretariat of the Ramsar Convention on Wetlands, Gland, Switzerland
                [15 ]Conservation International, Arlington, Virginia, United States of America
                [16 ]Birds Australia, Carlton, Australia
                [17 ]Aves y Conservación, Quito, Ecuador
                [18 ]National Fish and Wildlife Foundation, Washington, District of Columbia, United States of America
                [19 ]School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, Australia
                [20 ]Species Survival Commission, International Union for Conservation of Nature, Gland, Switzerland
                [21 ]The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
                [22 ]BirdLife International Pacific Partnership Secretariat, Suva, Fiji
                [23 ]Royal Society for the Protection of Birds, Sandy, United Kingdom
                [24 ]American Bird Conservancy, Washington, District of Columbia, United States of America
                [25 ]The Gund Institute for Ecological Economics, University of Vermont, Burlington, Vermont, United States of America
                [26 ]Department of Zoology, Oxford, United Kingdom
                [27 ]Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom
                [28 ]Al Ain Wildlife Park and Resort, Abu Dhabi, United Arab Emirates
                [29 ]Natural Resources Branch, Parks Canada, Hull, Quebec, Canada
                University of Kent, United Kingdom
                Author notes

                Analyzed the data: JPWS SQ SHMB CB BB NDB DM PM ME MB IM CF NS. Wrote the paper: SHMB. Developed IBA data: JA LAB IJB SC RPC MJC CD GCLD DFDF LDCF JM. Developed AZE data: T. Brooks T. Boucher SHMB NDB MF M. Hoffmann DK JFL FL CL MP THR BS AU NDS. Interpretation of results: NCD M. Heath SW. Contributed to interpretation of results: SA M. Hockings SS. Contributed to the design of Red List Index analyses & developed AZE data: T. Brooks.


                Current address: Nature Conservation Foundation, Mysore, India


                Current address: Division of Biology and Conservation Ecology, School of Science and the Environment, Manchester Metropolitan University, Manchester, United Kingdom

                Butchart 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.
                Page count
                Pages: 8
                Research Article
                Ecological Metrics



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