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Averting biodiversity collapse in tropical forest protected areas
Name: Averting biodiversity collapse in tropical forest protected areas
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Year: 2012
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unclassified reserves between our sample of 60 reserves and all 16,038 reserves found in the same tropical nations (Supplemen tary Fig. 2). We also found no significant difference (P50.08) in the geographical isolation of our reserves (travel time to the nearest city with greater than 50,000 residents) relative to a random sample of 60 protected areas stratified across the same 36 nations (Supplementary Fig. 3). We critically assessed the validity of our interview data by compar ing them to 59 independent time series data sets in which change in a single guild or environmental driver was assessed for one of our protected areas. Collectively, our meta analysis included some data on 15 of the guilds, 13 of the drivers and 27 of the protected areas in our study (Supplementary Table 1). Most (86.4%) of the independent data sets supported our interview results, and in no case did an independenttestreportatrendoppositeinsigntoourinterview based findings. Our analyses suggest that the most sensitive guilds in tropical pro tected areas include apex predators, large non predatory vertebrates, bats, stream dwelling amphibians, terrestrial amphibians, lizards and larger reptiles, non venomous snakes, freshwater fish, large seeded old growth trees, epiphytes and ecological specialists (allP,0.0056, witheffectsizesrangingfrom20.36to21.05;SupplementaryTable2). Several other groups were somewhat less vulnerable, including primates, understory insectivorous birds, large frugivorous birds,raptorial birds, venomous snakes, species that require tree cavities, and migratory species (allP,0.05, with effect sizes from20.27 to 20.53). In addition, five groups increased markedly in abundance in the reserves, including pioneer and generalist trees, lianas and vines, invasive animals, invasive plants and human diseases (allP,0.0056, with effect sizes from 0.44 to 1.17). To integrate these disparate data, we generated a 'reserve health index" that focused on 10 of the best studied guilds (data for each available at$80% of reserves), all of which seem to be sensitive to environmental changes in protected areas. Six of these are generally 'disturbanceavoiders"(apexpredators,largenon predatoryvertebrates, primates, understory insectivorous birds, large frugivorous birds and large seeded old growth trees) and the remainder seem to be 'disturbance favouring" groups (pioneer and generalist trees, lianas and vines, exotic animals and exotic plants). For each protected area, we averaged the mean values for each group, using negative values to indicate increases in abundance of the disturbance favouring guilds. The reserve health index varied greatly among the different pro tected areas (Fig. 1). About four fifths of the reserves had negative values, indicating some decline in reserve health. For 50% of all reserves this decline was relatively serious (mean score,20.25), with the affected organisms being remarkable for their high functional and taxonomic diversity (Fig. 2). These included plants with varying growthformsandlife historystrategies,andfaunathatdifferedwidely in body size, trophic level, foraging strategies, area needs, habitat use and other attributes. The remaining reserves generally exhibited much more positive outcomes for biodiversity (Fig. 2), although a few disturbance favouring guilds, such as exotic plants and pioneer and generalist trees, often increased even within these areas. An important predictor of reserve health was improving reserve management. According to our experts, reserves in which actual, on the ground protection efforts (see Supplementary Information) had increased over the past 20 to 30years generally fared better than those in which protection had declined; a relationship that was con sistent across all three of the world"s major tropical regions (Fig. 3). Indeed, on the ground protection has increased in more than half of the reserves over the past 20 to 30years, and this is assisting efforts to limit threats such as deforestation, logging, fires and hunting within these reserves (Supplementary Table 3), relative to areas immediately outside (Supplementary Table 4). However, our findings show that protecting biodiversity involves more than just safeguarding the reserves themselves. In many instances, the landscapes and habitats surrounding reserves are under imminent threat 5,6,15 (Fig. 4 and Supplementary Tables 3 and 4). For example, 85% of our reserves suffered declines in surrounding forest cover in the last 20 to 30years, whereas only 2% gained surrounding forest. As shown by general linear models (Supplementary Table 5), such changes can seriously affect reserve biodiversity. Among the 'Suffering"reserves'Succeeding"reserves 1.0 0.500.51.0 WorseningImproving 0246810121416 Number of reserves Figure 1|Distribution of the 'reserve health index" for 60 protected areas spanningtheworld"smajortropicalforestregions.Thisrelative indexaverages changesin10well studiedguildsofanimalsandplants,includingdisturbance avoiding and disturbance favouring groups, over the past 20 to 30years. Freshwater shCavity requiring spp.Understory insectivorous birdsLarger frugivorous birdsHuman diseasesLarger game birdsRaptorial birdsLianas and vinesLarge seeded old growth treesExotic animalsApex predatorsPioneer and generalist treesPrimatesLarge non predatory spp.Exotic plants Suffering reserves Worsening (%) Succeeding reserves Improving (%) 80 60 40 20 0 20 40 60 80 80 60 40 20 0 20 40 60 80 Worsenin g (%) Improving (%) Figure 2|Percentages of reserves that are worsening versusimprovingfor key disturbance sensitive guilds, contrasted between 'suffering" and 'succeeding" reserves (which are distinguished by having lower (,20.25) versus higher ($20.25) values for the reserve health index, respectively). For disturbance favouring organisms such as exotic plants and animals, pioneer and generalisttrees, lianas and vines, and human diseases, the reserve is considered to beworsening if the group increased in abundance. For any particular guild,reserves with missing or zero values (no trend) are not included. LETTERRESEARCH 13 SEPTEMBER 2012 | VOL 489 | NATURE | 291 Macmillan Publishers Limited. All rights reserved 2012

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potential drivers of declining reserve health, threeof the most import ant predictors involved ecological changes outside reserves (declining forest cover, increasing logging and increasing fires outside reserves; Supplementary Fig. 6). The remainder involved changes within reserves (particularly declining forest cover and increasing hunting, as well as increasing logging and harvests of non timber forest pro ducts; Supplementary Table 5). Thus, changes both inside and outside reserves determine their ecological viability, with forest disruption (deforestation, logging and fires), and overexploitation of wildlife and forest resources (huntingand harvests of non timber forest products) having the greatest direct negative impacts. Other environmental changes, suchas air and water pollution, increases in human population densities and climatic change (changes in total rainfall, ambient temperature, droughts and windstorms)generallyhadweakerormoreindirecteffectsoverthelast 20 to 30years (Supplementary Table 5). Environmental degradation occurring around a protected area could affect biodiversity in many ways, such as by increasing reserve isolation, area and edge effects 15 19 . However, we discovered that its effects are also more insidious: they strongly predispose the reserve itself to similar kinds of degradation. Nearly all (19 of 21) of the environmental drivers had positive slopes when comparing their direction and magnitude inside versus outside reserves (Fig. 5). Among these, 13 were significant even with stringent Bonferroni cor rections (P,0.0071) and 17 would have been significant if tested individually (P,0.05). As expected, the associations were strongest forclimateparametersbutwerealsostrongforvariablesdescribingair and water pollution, stream sedimentation, hunting, mining, harvests of non timber forest products and fires. To a lesser extent, trends in forestcover,humanpopulations,roadexpansionandautomobiletraffic inside reserves also mirror those occurring outside reserves (Fig. 5). Our findings signal that the fates of tropical protected areas will be determined by environmental changes both within and around the reserves, and that pressures inside reserves often closely reflect those occurring around them. For many reasons, larger reserves should be more resilient to such changes 15 22 , although we found that removing the effects of reserve area statistically did not consistently weaken the correlations between changes inside versus outside protected areas (Supplementary Table 6). Our study reveals marked variability in the health of tropical pro tected areas. It indicates that the best strategy for maintaining biodi versity within tropical reserves is to protect them against their major proximate threats, particularly habitat disruption and overharvesting. However, it is not enough to confine such efforts to reserve interiors while ignoring their surrounding landscapes, which are often being rapidlydeforested,degradedandoverhunted 5,6,13,15 (Fig.5).Afailureto limit interrelated internal and external threats could predispose reservestoecologicaldecay,includingataxonomicallyandfunctionally Inside reserve Outside reserve Population growth Forest cover Logging Fires Soil erosion Stream sedimentation Water pollution Road expansion Automobile traf c Population growth Forest cover Logging Fires Soil erosion Stream sedimentation Water pollution Road expansion Automobile traf c 100 50 0 50 100 Worsening (%) Improving (%) Inside reserves Outside reserves Figure 4|Comparison of ecological changes inside versus outside protected areas, for selected environmental drivers. The image is an example of the strong distinction in disturbance inside versus outside a reserve. The bars show the percentages of reserves with improving versus worsening conditions. 1 0.8 0.6 0.4 Africa Americas Asia Paci c Reserve health Worsening Chan ge in reserve protection 1 0.500.51 Improving Figure 3|Effectsofimprovingon the groundprotectiononarelativeindexof reserve health. This positive relationship held across all three tropical continents (a general linear model showed that the protection term was the most effective predictor of reserve health (Akaike"s information criterion weight, 0.595; deviance explained, 11.4%), with the addition of 'continent" providing only a small improvement in model fit (Akaike"s information criterion weight,0.317; deviance explained,16.3%). RESEARCHLETTER 292 | NATURE | VOL 489 | 13 SEPTEMBER 2012 Macmillan Publishers Limited. All rights reserved 2012

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sweeping array of changes in species communities (Fig. 2) and an erosion of fundamental ecosystem processes 16,18,23 . Protected areas are a cornerstone of efforts to conserve tropical biodiversity 3,4,13,21 . It is not our intent to diminish their crucial role but to highlight growing challenges that could threaten their success. Thevitalecologicalfunctionsofwildlifehabitatssurroundingprotected areas create an imperative, wherever possible, to establish sizeable bufferzonesaroundreserves,maintainsubstantialreserveconnectivity tootherforestareasandpromotelower impactlandusesnearreserves byengagingandbenefitinglocalcommunities 4,15,24 27 .Afocusonman aging both external and internal threats should also increase the resi lience of biodiversity in reserves to potentially serious climatic change 28 30 in the future. METHODS SUMMARY Our interview protocol, rationale, questionnaire and data analyses are detailed in the Supplementary Information. We selected protected areas broadly to span the African, American and Asia Pacific tropics (Supplementary Fig. 1), focusing on sites with mostly tropical or subtropical forest that had at least 10 refereed pub lications and 4 5 researchers with long term experience who could be identified and successfully interviewed. Wedevisedarobustandrelativelysimplestatisticalapproachtoassesstemporal changesintheabundanceofeachguildandineachpotentialenvironmentaldriver acrossourreservenetwork(seeSupplementaryInformation).Inbrief,thisinvolved asking eachexpert whether eachvariable had markedly increased, remained stable ormarkedlydeclinedforeachreserve.Theseresponseswerescoredas1,0and21, respectively. For each response, the expert was also asked to rank their degree of confidence in their knowledge. After discarding responses with lower confidence, scoresfromtheindividualexpertsateachsitewerepooledtogenerateameanvalue (ranging from21.0 to 1.0) to estimate the long term trend for each variable. Themeansforeachvariableacrossall60siteswerethenpooledintoasingledata distribution. We used bootstrapping (resampling with replacement; 100,000 itera tions)togenerateconfidenceintervalsfortheoverallmeanofthedatadistribution.If the confidence intervals did not overlap zero, then we interpreted the trend as being non random.Becausewetestedmanydifferentguilds,weusedastringentBonferroni correction (P#0.0056) to reduce the likelihood of Type I statistical errors, although wealsoidentified guildsthatshowed evidenceoftrends(P#0.05)iftestedindividu ally. For comparison, we estimated effectsizes (bootstrapped mean divided by s.d., with negative values indicating declines) for changes in guild abundances and for potential drivers inside and outside reserves (Supplementary Tables 2 4). Received 24 February; accepted 14 June 2012. Published online 25 July; correctedonline 12 September 2012 (seefull text HTML version for details). 1. Pimm, S. L. & Raven, P. R. 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Supplementary Informationis linked to the online version of the paper at AcknowledgementsThe study was supported by James Cook University, the Smithsonian Tropical Research Institute, an Australian Laureate Fellowship (to W.F.L.) and NSF grant RCN 0741956. We thank A. Bruner, R. A. Butler, G. R. Clements, R. Condit, C. N. Cook, S. Goosem, J. Geldmann, L. Joppa, S. L. Pimm and O. Venter for comments. Author ContributionsW.F.L. conceived the study and coordinated its design, analysis and manuscript preparation. D.C.U., J.R. and M.K. conducted the interviews; C.J.A.B. assistedwithdataanalysisandsomewriting;andS.P.S.,S.G.L.,M.C.andW.L.organized data or collected metadata. The remaining authors provided detailed interviews on protected areas and offered feedback on the manuscript. Author InformationReprints and permissions information is available at The authors declare no competing financial interests. Readers are welcome to comment on the online version of this article at Correspondence and requests for materials should be addressed to W.F.L. ([email protected]). William F. Laurance 1,2 , D. Carolina Useche 2 , Julio Rendeiro 2 , Margareta Kalka 2 , Corey J. A. Bradshaw 3 , Sean P. Sloan 1 , Susan G. Laurance 1 , Mason Campbell 1 , Kate Livestock grazingExotic tree plantationsSelective loggingSoil erosionRoad expansionPopulation growthForest coverAutomobile traf?cFiresNTFP harvestsIllegal miningRiver ?owsHuntingStream sedimentationWater pollutionAir pollutionDroughtsFloodingWindstorm disturbanceRainfallTemperature Nonsigni?cant P < 0.05 P < 0.0071 Correlation coef?cient (r) 0.2 0 0.2 0.4 0.6 0.8 Figure 5|Pearson correlations comparing the direction and strength of 21 environmental drivers inside versus outside tropical protected areas. NTFP, non timber forest products. LETTERRESEARCH 13 SEPTEMBER 2012 | VOL 489 | NATURE | 293 Macmillan Publishers Limited. All rights reserved 2012

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Abernethy 4 , Patricia Alvarez 5 , Victor Arroyo Rodriguez 6 , Peter Ashton 7 , Julieta Ben tez Malvido 6 , Allard Blom 8 , Kadiri S. Bobo 9 , Charles H. Cannon 10 , Min Cao 10 , Richard Carroll 8 , Colin Chapman 11 , Rosamond Coates 12 , Marina Cords 13 , Finn Danielsen 14 , BartDeDijn 15 , EricDinerstein 8 , MaureenA. Donnelly 16 , David Edwards 1 , Felicity Edwards 1 , Nina Farwig 17 , Peter Fashing 18 , Pierre Michel Forget 19 , Mercedes Foster 20 , George Gale 21 , David Harris 22 , Rhett Harrison 10 , John Hart 23 , Sarah Karpanty 24 , W. John Kress 25 , Jagdish Krishnaswamy 26 , Willis Logsdon 1 , Jon Lovett 27 , William Magnusson 28 , Fiona Maisels 4,29 , Andrew R. Marshall 30 , Deedra McClearn 31 , Divya Mudappa 32 , Martin R. Nielsen 33 , RichardPearson 34 , Nigel Pitman 5 , Janvander Ploeg 35 , Andrew Plumptre 36 , John Poulsen 37 , Mauricio Quesada 6 , Hugo Rainey 29 , Douglas Robinson 38 , Christiane Roetgers 1 , Francesco Rovero 39 , Frederick Scatena 40 , Christian Schulze 41 , Douglas Sheil 42 , Thomas Struhsaker 5 , John Terborgh 5 , Duncan Thomas 38 ,RobertTimm 43 ,J.NicolasUrbina Cardona 44 ,KarthikeyanVasudevan 45 ,S. Joseph Wright 2 , Juan Carlos Arias G. 46 , Luzmila Arroyo 47 , Mark Ashton 48 , Philippe Auzel 11 , Dennis Babaasa 49 , Fred Babweteera 50 , Patrick Baker 51 , Olaf Banki 52 , Margot Bass 53 , Inogwabini Bila Isia 54 , Stephen Blake 29 , Warren Brockelman 55 , Nicholas Brokaw 56 , Carsten A. Bru hl 57 , Sarayudh Bunyavejchewin 58 , Jung Tai Chao 59 , Jerome Chave 60 , Ravi Chellam 61 , Connie J. Clark 5 , Jose Clavijo 62 , Robert Congdon 34 , Richard Corlett 63 , H. S. Dattaraja 64 , Chittaranjan Dave 65 , Glyn Davies 66 , Beatriz de Mello Beisiegel 67 , Rosa de Nazare Paes da Silva 68 , Anthony Di Fiore 69 , Arvin Diesmos 70 , Rodolfo Dirzo 71 , Diane Doran Sheehy 72 , Mitchell Eaton 73 , Louise Emmons 25 , Alejandro Estrada 12 , Corneille Ewango 74 , Linda Fedigan 75 , Fran ois Feer 19 , Barbara Fruth 76 , Jacalyn Giacalone Willis 77 , Uromi Goodale 78 , Steven Goodman 79 , Juan C. Guix 80 , Paul Guthiga 81 , William Haber 82 , Keith Hamer 83 , Ilka Herbinger 84 , Jane Hill 30 , Zhongliang Huang 85 , I Fang Sun 86 , Kalan Ickes 87 , Akira Itoh 88 , Nata lia Ivanauskas 89 , Betsy Jackes 34 , JohnJanovec 90 , Daniel Janzen 40 , Mo Jiangming 91 , Chen Jin 10 , Trevor Jones 92 , Hermes Justiniano 93 , Elisabeth Kalko 94 {, Aventino Kasangaki 95 , Timothy Killeen 96 , Hen biau King 97 , Erik Klop 98 , Cheryl Knott 99 , Inza Kone 100 , Enoka Kudavidanage 63 , Jose Lahoz da Silva Ribeiro 101 , John Lattke 102 , Richard Laval 103 , Robert Lawton 104 , Miguel Leal 105 , Mark Leighton 106 , Miguel Lentino 107 , Cristiane Leonel 108 , Jeremy Lindsell 109 , Lee Ling Ling 110 , K. Eduard Linsenmair 111 , Elizabeth Losos 112 ,ArielLugo 113 ,JeremiahLwanga 114 ,AndrewL.Mack 115 ,MarluciaMartins 116 , W. Scott McGraw 117 , Roan McNab 118 , Luciano Montag 119 , Jo Myers Thompson 120 , Jacob Nabe Nielsen 121 , Michiko Nakagawa 122 , Sanjay Nepal 123 , Marilyn Norconk 124 , Vojtech Novotny 125 , Sean O"Donnell 126 , Muse Opiang 127 , Paul Ouboter 128 , Kenneth Parker 129 , N. Parthasarathy 130 ,Ka tia Pisciotta 131 , Dewi Prawiradilaga 132 , Catherine Pringle 133 , Subaraj Rajathurai 134 , Ulrich Reichard 135 , Gay Reinartz 136 , Katherine Renton 137 , Glen Reynolds 138 , Vernon Reynolds 139 , Erin Riley 140 , Mark Oliver Ro del 141 , Jessica Rothman 142 , Philip Round 143 , Shoko Sakai 144 , Tania Sanaiotti 28 , Tommaso Savini 21 , Gertrud Schaab 145 , John Seidensticker 146 , Alhaji Siaka 147 , Miles R. Silman 148 , Thomas B. Smith 149 , Samuel Soares de Almeida 150 {, Navjot Sodhi 63 {, Craig Stanford 151 , Kristine Stewart 152 , Emma Stokes 29 , Kathryn E. Stoner 153 , Raman Sukumar 154 , Martin Surbeck 76 , Mathias Tobler 90 , Teja Tscharntke 155 , Andrea Turkalo 156 , Govindaswamy Umapathy 157 , Merlijn van Weerd 35 , Jorge Vega Rivera 137 , Meena Venkataraman 158 , Linda Venn 159 , Carlos Verea 160 , Carolina Volkmer de Castilho 161 , Matthias Waltert 155 , Benjamin Wang 149 , David Watts 48 , William Weber 29 , PaigeWest 13 ,DavidWhitacre 162 ,KenWhitney 163 ,DavidWilkie 29 ,StephenWilliams 34 , Debra D. Wright 115 , Patricia Wright 164 , Lu Xiankai 91 , Pralad Yonzon 165 {& Franky Zamzani 166 1 Centre for Tropical Environmental and Sustainability Science (TESS) and School of MarineandTropicalBiology,JamesCook University,Cairns,Queensland4878,Australia. 2 Smithsonian Tropical Research Institute, Balboa, Anco n, Panama. 3 School of Earth and EnvironmentalSciences,UniversityofAdelaide,Adelaide,SouthAustralia5005,Australia. 4 Stirling University, Stirling FK9 4LA, UK. 5 Duke University, Durham, North Carolina 27705, USA. 6 Universidad Nacional Auto noma de Me xico (UNAM), Morelia, Mexico. 7 Royal Botanic Gardens, Kew, Richmond TW9 3AB, UK. 8 World Wildlife Fund (WWF), Washington DC 20037, USA. 9 University of Dschang, Dschang, Cameroon. 10 Xishuangbanna Tropical Botanical Garden, Yunnan 666303, People"s Republic of China. 11 McGill University, Montreal H3A 2T7, Canada. 12 Estacio n de Biologia Tropical Los Tuxtlas, Universidad Nacional Auto noma de Me xico, Veracruz 95701, Mexico. 13 Columbia University, New York, New York 10027, USA. 14 Nordic Foundation for Development and Ecology, DK 1159 Copenhagen, Denmark. 15 Bart De Dijn Environmental Consultancy, Paramaribo, Suriname. 16 Florida International University, Miami, Florida 33199, USA. 17 Philipps Universita t Marburg, Marburg 35043, Germany. 18 California State University, Fullerton, California 92834, USA. 19 Museum Natural d"Histoire Naturelle, 91800 Brunoy, France. 20 US Geological Survey, Smithsonian Institution, Washington DC 20013, USA. 21 King Mongkut"s University of Technology Thonburi, Bangkok 10150, Thailand. 22 Royal Botanic Garden, Edinburgh, Scotland EH3 5LR, UK. 23 Tshuapa Lomami Lualaba Project, Kinshasa, Democratic Republic of Congo. 24 Virginia Tech University, Blacksburg, Virginia 24061, USA. 25 National Museum of Natural History,Smithsonian Institution,WashingtonDC20013,USA. 26 AshokaTrustfor ResearchinEcologyandtheEnvironment(ATREE),Bangalore560064,India. 27 University ofTwente,Enschede,Netherlands. 28 InstitutoNacionaldePesquisasdaAmazo nia(INPA), Manaus,Amazonas 69011 970, Brazil. 29 Wildlife Conservation Society,Bronx, New York 10460, USA. 30 University of York, Heslington, York YO10 5DD, UK. 31 La Selva Biological Station, San Pedro, Costa Rica. 32 Nature Conservation Foundation, Mysore 570 002, India. 33 University of Copenhagen, Copenhagen, Denmark. 34 James Cook University, Townsville, Queensland 4811, Australia. 35 Leiden University, Leiden, Netherlands. 36 Wildlife Conservation Society, Kampala, Uganda. 37 Woods Hole Research Center, Falmouth, Massachusetts 02540, USA. 38 Oregon State University, Corvallis, Oregon 97331, USA. 39 Museo delle Scienze, 38122 Trento, Italy. 40 University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. 41 University of Vienna, 1030 Vienna, Austria. 42 Bwindi ImpenetrableNational Park,Kabale,Uganda. 43 Universityof Kansas,Lawrence, Kansas 66045, USA. 44 Pontificia Universidad Javeriana, Bogota , Colombia. 45 Wildlife Institute of India, Dehradun, India. 46 Unidad de Parques Nacionales Naturales deColombia, Bogota ,Colombia. 47 Museo de Historia Natural Noel Kempff, Santa Cruz, Bolivia. 48 Yale University, New Haven, Connecticut 06511, USA. 49 Institute of Tropical Forest Conservation, Kabale, Uganda. 50 Budongo Conservation Field Station, Masindi, Uganda. 51 Monash University, Melbourne, Victoria 3800, Australia. 52 Utrecht University, Utrecht, Netherlands. 53 Finding Species, Takoma Park, Maryland 20912, USA. 54 University of Kent, Kent CT2 7NZ, UK. 55 Mahidol University Salaya, Nakhon Pathom 73170, Thailand. 56 University of Puerto Rico, San Juan 00936, Puerto Rico. 57 University Koblenz Landau, D 76829 Landau, Germany. 58 Department of National Parks, Chatuchak, Bangkok 10900, Thailand. 59 Taiwan Forestry Research Institute, Tapei 10066, Taiwan. 60 Universite Paul Sabatier, Toulouse, France. 61 Wildlife Conservation Society, Bangalore 560070, India. 62 Universidad Central de Venezuela, Aragua, Venezuela. 63 National University of Singapore, Singapore 117543. 64 Indian Institute of Science, Bangalore 560012, India. 65 World Wide Fund for Nature (WWF), New Delhi 110003, India. 66 World Wide Fund for Nature (WWF), Surrey GU7 1XR, UK. 67 Instituto Chico Mendes de Conserva a o de Biodiversidade, Atibaia, Sa o Paulo 12952 011, Brazil. 68 O Conselho Regional de Engenhara, Arquitetura e Agronomia do Para ,Bele m, Para , Brazil. 69 University of Texas, Austin, Texas 78712, USA. 70 National Museum of the Philippines, Manila, Phillipines. 71 Stanford University, Stanford, California 94305, USA. 72 State University of New York at Stony Brook, Stony Brook, New York 11794, USA. 73 UniversityofColorado,Boulder,Colorado80309,USA. 74 WildlifeConservationSociety, Kinshasa, Democratic Republic of Congo. 75 University of Calgary, Alberta T2N 1N4, Canada. 76 Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany. 77 MontclairStateUniversity,Montclair,NewJersey07043,USA. 78 UniversityofCalifornia, San Diego, California 92093, USA. 79 Field Museum of Natural History, Chicago, Illinois 60605, USA. 80 Universitat de Barcelona, 08028 Barcelona, Spain. 81 Kenya Institute for Public Policy Research and Analysis, Nairobi, Kenya. 82 Missouri Botanical Garden, St. Louis, Missouri 63166, USA. 83 University of Leeds, Leeds LS2 9JT, UK. 84 Wild Chimpanzee Foundation, Abidjan 23, Cote d"Ivoire. 85 Dinghushan Biosphere Reserve, Zhaoqing, People"s Republic of China. 86 Tunghai University, Taichung 407, Taiwan. 87 Clemson University, Clemson, South Carolina 29634, USA. 88 Osaka City University, Osaka 558 8585, Japan. 89 Instituto Florestal, Sa o Paulo, Sa o Paulo 02377 000, Brazil. 90 Botanical Research Institute of Texas, Fort Worth, Texas 76107, USA. 91 South China Botanical Garden, Guangzhou 510650, People"s Republic of China. 92 Anglia Ruskin University, Cambridge CB1 1PT, UK. 93 Fundacio n para la Conservacio n del Bosque Chiquitano, Bolivia. 94 University of Ulm, 89069 Ulm, Germany. 95 Mbarara University of Science and Technology, Mbarara, Uganda. 96 Conservation International, Arlington, Virginia22202,USA. 97 SocietyofSubtropicalEcology,Taipei,Taiwan. 98 RoyalHaskoning, Water and Ecology Group, Groningen, Netherlands. 99 Boston University, Boston, Massachusetts 02215, USA. 100 Centre Suisse de Recherches Scientifiques en Co te d"Ivoire, Abidjan, Co te d"Ivoire. 101 Universidade Estadual de Londrina, Londrina, Parana , Brazil. 102 Universidad Central de Venezuela, Caracas, Venezuela. 103 The Bat Jungle, Monteverde, Costa Rica. 104 University of Alabama, Huntsville, Alabama 35899, USA. 105 Bo te Postale 7847, Libreville, Gabon. 106 95 Warren Road, Framingham, Massachusetts 01702, USA. 107 Coleccio nOrnitolo gica Phelps, Caracas, Venezuela. 108 Parque Estadual Horto Florestal, Sa oPaulo,Sa o Paulo 02377 000, Brazil. 109 Royal Society for the Protection of Birds, Sandy SG19 2DL, UK. 110 National Taiwan University, Taipei, Taiwan. 111 University of Wu rzburg, Biocenter, D97074 Wuerzburg, Germany. 112 Organization for Tropical Studies, Durham, North Carolina 27705, USA. 113 USDA International Institute of Tropical Forestry, R o Piedras, Puerto Rico 00926. 114 Makerere University,Kampala,Uganda. 115 GreenCapacityInc.,NewFlorence,Pennsylvania15944, USA. 116 Museu Paraense Em lio Goeldi, Bele m, Para 66040 170, Brazil. 117 Ohio State University, Columbus, Ohio 43210, USA. 118 Wildlife Conservation Society, Flores, Guatemala. 119 Universidad Federal do Para ,Bele m, Para 66040 170, Brazil. 120 Lukuru Wildlife Research Foundation, Kinshasa, Democratic Republic of Congo. 121 Aarhus University, 4000 Roskilde, Denmark. 122 Nagoya University, Nagoya, Japan. 123 University of Waterloo, Waterloo, Ontario N2L 3G1, Canada. 124 Kent State University, Kent, Ohio 44242,USA. 125 InstituteofEntomology,CeskeBudejovice,CzechRepublic. 126 University ofWashington,Seattle,Washington98195,USA. 127 PNGInstituteofBiologicalResearch, Goroka, Papua New Guinea. 128 University of Suriname, Paramaribo, Suriname. 129 113 3885 Richet Rd, Prince George, British Columbia V2K 2J2, Canada. 130 PondicherryUniversity,Puducherry605 014,India. 131 Funda a oFlorestal,Sa oPaulo, Sa o Paulo 02377 000, Brazil. 132 Research Centre for Biology, Cibinong 16911, Indonesia. 133 University of Georgia, Athens, Georgia 30602, USA. 134 Strix Wildlife Consultancy, Singapore. 135 Southern Illinois University, Carbondale, Illinois 62901, USA. 136 Zoological Society of Milwaukee, Milwaukee, Wisconsin 53226, USA. 137 Estacio nde Biologia Chamela, Universidad Nacional Auto noma de Me xico, Jalisco 48980, Mexico. 138 Danum Valley Field Centre, Sabah, Malaysia. 139 Oxford University, Oxford BN26 5UX, UK. 140 San Diego State University, San Diego, California 92182, USA. 141 Museum fu r Naturkunde, Berlin, Germany. 142 City University of New York, New York 10065, USA. 143 MahidolUniversity,Bangkok10400,Thailand. 144 ResearchInstituteforHumanityand Nature, Kyoto, Japan. 145 Karlsruhe University of Applied Sciences, Karlsruhe, Germany. 146 National Zoological Park, Washington DC 20013, USA. 147 Gola Forest Programme, Kenema,SierraLeone. 148 WakeForestUniversity,Winston Salem,NorthCarolina27106, USA. 149 University of California, Los Angeles, California 90095, USA. 150 Av. Maalha es Barata 376, Bele m, Para 66040 170, Brazil. 151 University of Southern California, Los Angeles, California 90089, USA. 152 Institute of Applied Ethnobotany, Pompano Beach, Florida 33069, USA. 153 Texas A & M University, Kingsville, Texas 78363, USA. 154 Indian Institute of Science, Bangalore, India. 155 Georg August Universita t, Go ttingen, Germany. 156 Wildlife Conservation Society, Bangui, Central African Republic. 157 Centre for Cellular and Molecular Biology, Hyderabad, India. 158 701, Vesta B, Lodha Paradise, Thane, India. 159 Paluma Environmental Education Centre, Paluma, Queensland 4816, Australia. 160 Universidad Central de Venezuela, Maracay, Venezuela. 161 Embrapa Roraima, Boa Vista, Roraima, Brazil. 162 Treasure Valley Math and Science Center, Boise, Idaho 83714, USA. 163 Rice University, Houston, Texas 77005, USA. 164 Stony Brook University, Stony Brook, New York 11794, USA. 165 Resources Himalaya Foundation, Kathmandu, Nepal. 166 Gunung Palung National Park, West Kalimantan, Indonesia. {Deceased. RESEARCHLETTER 294 | NATURE | VOL 489 | 13 SEPTEMBER 2012 Macmillan Publishers Limited. All rights reserved 2012

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2 Representativeness of study sites Our 60 tropical protected areas spanned 36 different nations. To provide an indication of the degree to which our sites were 'typical', we compared the relative frequency of reserves within 'high protection' (IUCN Categories I IV), 'multiple use' (IUCN Categories V VI), and unclassified categories between our sample and all 16,038 protected areas within the same nations from the World Database on Protected Areas ( We excluded China from this comparison because its reserve classification scheme differs from that of other nations in having virtually no high protection reserves; the ratio of multiple use to high protection reserves in China was 628.3, whereas ratios for all the other 35 nations were < 3.4. We found no significant difference in the frequencies of reserves in the three different categories between our sample and expected values derived from all 16,038 reserves in the same nations (G adj = 4.056, d . f. = 2, P = 0.13; G test for goodness of fit, with Williams' correction for sample size) (Supplementary Fig. 2). Other kinds of data, such as the budgets and staffing for protected areas, were unavailable for most sites, precluding more in depth comparisons of this nature. Supplementary Figure 2 Number of high protection (IUCN Categories I IV), multiple use (Categories V VI) and unclassified protected areas in our study compared to expected values derived from all 16,038 protected areas in the same tropical nations. 01020304050 I IVV VIUnclassifiedG adj = 4.056, df = 2, P = 0.13 Observed Expected Number of PAs IUCN Classification Reserve isolation We also assessed the relative geographical isolation of the protected areas in our study, as measured by their distance to the nearest city. We did so because reserve isolation might influence the human pressures that a reserve experiences, and we wished to know whether our reserves were more or less isolated from nearby human populations than is typical of other reserves in the same nations. For each of our 60 protected areas, we overlaid its boundary map onto a mapped surface of travel time accessibility 1 . This surface estimates, for any point on Earth, the mean travel time in minutes required to reach the nearest city of > 50,000 residents, using conventional local

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3 means such as automobiles, boats and hiking. The surface has a spatial resolution of 0.0083 decimal degrees (925 m at the equator), and we averaged the measurements for every pixel within each reserve to estimate its average isolation. We then randomly selected 60 reserves for comparison. We stratified the randomly selected reserves across the same 36 nations in which our protected areas occur (choosing for each nation an equal number of random reserves as that found in our original sample). The randomly selected reserves were chosen from the World Database on Protected Areas (, using a Mersenne Twist random number generator with a random seed value. Marine protected areas were excluded from the random sample by considering only reserves whose centre most point fell on land. We found considerable overlap between the isolation of our reserves (mean SD = 741 761 minutes to the nearest city) and the randomly selected reserves (505 479 minutes) (Supplementary Fig. 3). The isolation values did not differ significantly on average, either when using a Mann Whitney U test (P = 0.071) or a two way ANOVA that contrasted log transformed isolation values between our sample and the random sites and also among the three major tropical regions (Africa, Americas, Asia Pacific). This latter analysis revealed no significant difference between our reserves and the random sites (F 1,114 = 3.19, P = 0.077), but some difference among the three major regions (F 2,114 = 3.33 , P = 0.039). In pairwise comparisons, reserves in Africa were more isolated (P < 0.05; Tukey's test) than those in the Asia Pacific, with reserves in the Americas being intermediate and not significantly different from those in the other two regions. Supplementary Figure 3 Comparison of the relative isolation (travelling time to the nearest city of > 50,000 residents) between the 60 tropical forest protected areas in our study and a random sample of 60 protected areas stratified across the same 36 nations. 05101520 200 400600800100012001400160018002000>2000 Reserves in this study Randomly selected reserves Number of protected areas Reserve isolation (minutes)

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4 Design of interviews We initially tested whether we could use research publications to assess the knowledge base at our research sites, using two of the best studied sites in the tropics, Barro Colorado Island in Panama and La Selva Biological Station in Costa Rica. Despite perusing the entire publication lists for both sites (up to early 2008), we found that recognized experts provided more comprehensive, up to date and time efficient assessments. Moreover, the number of available refereed publications varied enormously among our 60 selected sites, from ju st 10 to > 3,300 papers. A reliance solely on publications would have imparted an obvious sampling bias when attempting to compare different sites, whereas experts are able to integrate a much wider range of knowledge based on personal observations, commun ications with other researchers and critically evaluating the relevant technical literature for their site. Our 10 page interview form, coupled with a telephone or face to face interview, allowed us to plumb in detail the accumulated knowledge of our long term experts. The form (attached below as Appendix 1 ) includes 120 individual questions, 60 of which have five part answers. We carefully designed our interview form after consulting the relevant survey method literature 2 5 and with social science experts who routinely conduct such surveys. Two of the most important potential biases to avoid are (a) diluting high confidence responses with low confidence responses, and (b) interviewing 'clusters' of closely affiliated, like minded experts 2,3 . To minimize the first concern, we asked our experts to rank their level of confidence for each question they were asked ('speculative', 'good', 'high'). We discarded all speculative responses prior to analysis. To minimize the second concern, we used both technical publications and communications with an array of different individ uals to identify our experts. These experts were predominantly ecologists, zoologists, and botanists with long term field and empirical data collection experience in their respective protected area. Another concern in surveys such as ours is that respondents might provide biased responses either because they fear political or professional retribution 2,3 or are personally invested in seeing the protected area succeed 4 . To minimize this concern, we offered all respondents complete anonymity, sho uld they wish. We established the following conditions: if an outside party wishes to communicate with an expert for a particular reserve, they should contact the lead author of this study (William Laurance, email: [email protected]) who will then forward the request to the relevant expert. That expert can then either respond or ignore the request at their discretion. In practice, anonymity was not a concern for most of our experts, all of whom were offered, and most of whom accepted, co authorship of this study (however, to err on the side of ca ution , none is explicitly associated with any particularly protected area in this study). We also considered and rejected the notion that these experts might have provided overly positive responses because they wanted to see the reserve succeed. In practice, many respondents (virtually all of whom were independent researchers, n ot park employees) expressed at least some concerns about the condition of their reserve. Further, our interview protocol was so exhaustive, specific and objective (with both written and verbal components and interviews of 4 5 different researchers per reserve) that it would have been difficult for any individual to obfuscate important changes in the reserve. A final concern we had was whether 4 5 interviews were sufficient to identify the key trends at our different sites. To test this we conducted a 'saturation analysis' 5 , which is designed to determine how much new information is being provided by each additional interview (Supplementary Fig. 4). First, we arbitrarily selected four of our response variables that varied widely. Second, for each of our 21 reserves for which we had 5 interviews, we pooled the

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5 interview data to generate mean scores for each variable . Third, we compared the mean score across these reserves from 1, 2, 3, and then 4 interviews to those generated by 5 interviews, using linear regression. As shown by the rapid and nonlinear rise in R 2 for each variable , the mean scores for each reserve rapidly converge on the final values after just 2 4 interviews. We conclude from this assessment that our regime of 4 5 interviews per site was sufficient to capture the most important aspects of available expert knowledge. Supplementary Figure 4 Saturation curves for four representative response variables, compared to values achieved with randomly generated data . 020406080100 12345 Reserve protection Top predators Large frugivorous birds Exotic plant species Randomly generated dataSimilarity to final values (R 2 ) Number of interviews Statistical analyses For ease of interpretation, we devised a robust and relatively simple statistical approach to assess temporal changes in each guild and p otential environmental driver. We illustrate our strategy using the abundance of a single guild, apex predators, as an example. For each reserve, each expert was asked to indicate whether the overall abundance of apex predators had declined by at least 10 25%, remained roughly stable, or increased by at least 10 25%, over the past 20 30 years. These responses were scored as 1, 0, and 1, respectively A A We originally collected quantitative data on each guild or environmental driver, using an ordinal scale ( 3 = decline of > 50%; 2 = decline of 25 50%; 1 = decline of 10 25%; 0 = no change; 1 = increase of 10 25%; 2 = increase of 25 50%; 3 = increase of > 50%). However, we elected to simplify these data into a three point scale (+1, 0, 1) because the validity of means and standard deviations derived from ordinal data has been questioned 6 and because the three point and ordinal scales yielded virtually identical results. For example, calculated effect sizes for our guilds (using the 27 guilds with adequate sample sizes; Supplementary Table 2) based on the three point and ordinal scales were strongly, positively and linearly related (F 1,25 = 744.5, R 2 = 96.8%, P < 0.00001; least squares regression analysis). . If an expert had no knowledge

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