REV I EW CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change KR I ST INA J . ANDERSON -TE IXE IRA 1 , 2 , S TUART J . DAV IE S 1 , 3 , AMY C . BENNETT 2 , ER IKA B . GONZALEZ -AKRE 2 , HELENE C . MULLER -LANDAU1 , S . JO SEPH WR IGHT 1 , KAMAR IAH ABU SAL IM 4 , ANG EL ICA M . ALMEYDA ZAMBRANO2 , 5 , 6 , ALFONSO ALONSO 7 , J ENN I FER L . BALTZER 8 , YVES BASSET 1 , NORMAN A . BOURG 2 , EBEN N . BROADBENT 2 , 5 , 6 , WARREN Y . BROCKELMAN9 , SARAYUDH BUNYAVE JCHEWIN 1 0 , DAV ID F . R . P . BURSLEM1 1 , NATHAL IE BUTT 1 2 , 1 3 , M IN CAO1 4 , DA IRON CARDENAS 1 5 , GEORGE B . CHUYONG1 6 , KE I TH CLAY 1 7 , SUSAN CORDELL 1 8 , HANDANAKERE S . DATTARA JA 1 9 , X IAOBAO DENG 1 4 , MATTEO DETTO 1 , X IAO JUN DU2 0 , ALVARO DUQUE 2 1 , DAV ID L . ER IKSON 3 , CORNE ILLE E .N . EWANGO2 2 , GUNTER A . F I SCHER 2 3 , CHR I ST INE FLETCHER 2 4 , ROB IN B . FOSTER 2 5 , CHR I ST IAN P . G IARD INA 1 8 , GREGORY S . G I LBERT 2 6 , 1 , N IMAL GUNAT ILLEKE 2 7 , SAV ITR I GUNAT ILLEKE 2 7 , ZHANQING HAO2 8 , W ILL IAM W. HARGROVE 2 9 , T ERE SE B . HART 3 0 , B I L LY C .H . HAU3 1 , FANGL IANG HE 3 2 , FORREST M . HOFFMAN3 3 , ROBERT W . HOWE 3 4 , S TEPHEN P . HUBBELL 1 , 3 5 , FA I TH M . INMAN- NARAHAR I 3 6 , PATR ICK A . JANSEN1 , 3 7 , M INGX I J IANG 3 8 , DAN IEL J . JOHNSON1 7 , MAMORU KANZAK I 3 9 , ABDUL RAHMAN KASS IM 2 4 , DAV ID KENFACK 1 , 3 , S TAL INE K IBET 4 0 , 4 1 , MARGARET F . K INNAIRD 4 2 , 4 3 , L I SA KORTE 7 , KAMIL KRAL 4 4 , J I T ENDRA KUMAR3 3 , ANDREW J . LARSON4 5 , Y IDE L I 4 6 , X IANKUN L I 4 7 , SH I RONG L IU 4 8 , SHAWN K .Y . LUM4 9 , JAMES A . LUTZ 5 0 , KEP ING MA2 0 , DAMIAN M . MADDALENA3 3 , J EAN -REMY MAKANA5 1 , YADV INDER MALH I 1 3 , TOBY MARTHEWS 1 3 , RAF IZAH MAT SERUD IN 5 2 , S EAN M . MCMAHON1 , 5 3 , W I LL IAM J . MC SHEA 2 , HERV E R . MEM IAGHE 5 4 , X IANGCHENG MI 2 0 , TAKASH I M IZUNO3 9 , M ICHAEL MORECROFT 5 5 , JONATHAN A . MYERS 5 6 , VO J TECH NOVOTNY 5 7 , 5 8 , ALEXANDRE A . DE OL IVE IRA 5 9 , P ERRY S . ONG 6 0 , DAV ID A . ORWIG 6 1 , R EBECCA OSTERTAG 6 2 , JAN DEN OUDEN6 3 , GEOFFREY G . PARKER 5 3 , R ICHARD P . PH ILL I P S 1 7 , LAWREN SACK 3 5 , MOSES N . SA INGE 6 4 , WE IGUO SANG2 0 , KR IANGSAK SR I -NGERNYUANG6 5 , RAMAN SUKUMAR 1 9 , I - FANG SUN6 6 , W ITCHAPHART SUNGPALEE 6 5 , HEBBALALU SATHYANARAYANA SURESH 1 9 , SYLVESTER TAN 6 7 , S EAN C . THOMAS 6 8 , DUNCAN W. THOMAS 6 9 , J I L L THOMPSON7 0 , 7 1 , B EN JAMIN L . TURNER 1 , MAR IA UR IARTE 7 2 , R ENATO VALENC IA 7 3 , MARTA I . VALLE JO 7 4 , ALBERTO V ICENT IN I 7 5 , TOM AS VR SKA 4 4 , X IHUA WANG7 6 , XUGAO WANG3 0 , GEORGE WE IBLEN 7 7 , AMY WOLF 7 8 , HAN XU4 6 , SANDRA YAP 6 0 and JESS ZIMMERMAN71 1Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama, Republic of Panama, 2Conservation Ecology Center, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA, USA, 3Department of Botany, National Museum of Natural History, Washington, DC, USA, 4Environmental and Life Sciences, Faculty of Science, Universiti of Brunei Darussalam, Tungku Link Road, Bandar Seri Begawan, BE 1410, Brunei Darussalam, 5Stanford Woods Institute for the Environment, Stanford University, Stanford, CA, USA, 6Department of Geography, University of Alabama, Tuscaloosa, AL, USA, 7Center for Conservation Education and Sustainability, Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC, USA, 8Department of Biology, Wilfrid Laurier University, Waterloo, ON, N2L 3C5, Canada, 9Department of Biology, Mahidol University, Bangkok, Thailand, 10Research Office, Department of National Parks, Wildlife and Plant Conservation, Bangkok, Thailand, 11School of Biological Sciences, University of Aberdeen, Aberdeen, UK, 12School of Biological Sciences, University of Queensland, St. Lucia 4072, Australia, 13Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK, 14Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, 88 Xuefu Road, Kunming 650223, China, 15Instituto Amazonico de Investigaciones Cientıficas Sinchi, Bogota, Colombia, 16Department of Botany and Plant Physiology, University of Buea, Buea, Cameroon, 17Department of Biology, Indiana University, Bloomington, IN, USA, 18Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, USA, 19Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India, 20Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China, 21Departamento de Ciencias Forestales, Universidad Nacional de Colombia, Medellin, Colombia, 22Centre de Formation et de Recherche en Conservation Forestiere (CEFRECOF) Epulu, Ituri Forest, Reserve de Faune a Okapis, Epulu, Democratic Republic of Congo, 23Kadoorie Farm and Botanic Garden, Tai Po, Hong Kong, 24Forest 1© 2014 John Wiley & Sons Ltd Global Change Biology (2014), doi: 10.1111/gcb.12712 Global Change Biology Research Institute Malaysia, Selangor, Malaysia, 25Botany Department, The Field Museum, Chicago, IL, USA, 26Environmental Studies Department, University of California, Santa Cruz, Santa Cruz, CA, USA, 27Faculty of Science, Department of Botany, University of Peradeniya, Peradeniya, Sri Lanka, 28State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China, 29Eastern Forest Environmental Threat Assessment Center, USDA-Forest Service Station Headquarters, Asheville, NC, USA, 30Tshuapa-Lomami-Lualaba Project, Lukuru Wildlife Research Foundation, Kinshasa BP 2012, Democratic Republic of the Congo, 31Kadoorie Institute and School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, 32Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada, 33Computational Earth Sciences Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA, 34Department of Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, WI 54311, USA, 35Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA, 36College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa, Honolulu, HI, USA, 37Resource Ecology Group, Wageningen University, Wageningen, The Netherlands, 38Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China, 39Graduate School of Agriculture, Kyoto University, Kyoto, Japan, 40National Museums of Kenya, P.O. Box 40658 -00100, Nairobi, Kenya, 41Land Resource Management & Agricultural Technology Department, University of Nairobi, P.O. Box 29053-00625 Nairobi, Kenya, 42Mpala Research Centre, PO Box 555, Nanyuki 10400, Kenya, 43Global Conservation Programs, Wildlife Conservation Society, 2300 Southern Blvd., Bronx, NY 10460, USA, 44Department of Forest Ecology, Silva Tarouca Research Institute, Brno, Czech Republic, 45Department of Forest Management, College of Forestry and Conservation, The University of Montana, Missoula, MT, USA, 46Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, China, 47Guangxi Institute of Botany, Chinese Academy of Sciences, Guilin, Guangxi, China, 48Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China, 49Natural Sciences & Science Education Academic Group, National Institute of Education, Nanyang Technological University, Singapore, 50Wildland Resources Department, Utah State University, Logan, UT, USA, 51Wildlife Conservation Society, Brazzaville, Democratic Republic of the Congo, 52Environmental and Life Sciences, Faculty of Science, Universiti of Brunei Darussalam, Tungku Link Road, BE 1410, Bandar Seri Begawan, Brunei Darussalam, 53Forest Ecology Group, Smithsonian Environmental Research Center, Edgewater, MD, USA, 54Institut de Recherche en Ecologie Tropicale/Centre National de la Recherche Scientifique et Technologique, Libreville, GABON, 55Natural England, Sheffield, England, UK, 56Department of Biology, Washington University in St. Louis, St. Louis, MO, USA, 57New Guinea Binatang Research Centre, PO Box 604, Madang, Papua New Guinea, 58Biology Centre, Academy of Sciences of the Czech Republic and Faculty of Science, University of South Bohemia, Branisovska 31, Ceske Budejovice 370 05, Czech Republic, 59Departamento Ecologia, Universidade de Sa˜o Paulo,Instituto de Biocieˆncias, Cidade Universita´ria, Sa˜o Paulo, SP, Brazil, 60Institute of Biology, University of the Philippines Diliman, Quezon City, Philippines, 61Harvard Forest, Harvard University, Petersham, MA, USA, 62Department of Biology, University of Hawaii, Hilo, HI, USA, 63Forest Ecology and Forest Management Group, Wageningen University, Wageningen, The Netherlands, 64Tropical Plant Exploration Group (TroPEG), P.O. Box 18 Mundemba, Southwest Region, Cameroon, 65Faculty of Architecture and Environmental Design, Maejo University, Chiang Mai Province, Thailand, 66Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualian, Taiwan, 67Sarawak Forest Department, Kuching, Sarawak, Malaysia, 68Faculty of Forestry, University of Toronto, 33 Willcocks St., Toronto, ON M5S 3B3, Canada, 69School of Biological Sciences, Washington State University, Vancouver, WA, USA, 70Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK, 71Institute for Tropical Ecosystem Studies, Department of Environmental Science, University of Puerto Rico, Rio Piedras Campus, PO Box 70357, San Juan 00936-8377, Puerto Rico, 72Department of Ecology, Evolution & Environmental Biology, Columbia University, New York, NY, USA, 73Department of Biological Sciences, Pontifical Catholic University of Ecuador, Apartado Postal 17-01-2184, Quito, Ecuador, 74Calle 37, Instituto Alexander von Humboldt, Number 8-40 Mezzanine, Bogota´, Colombia, 75Instituto Nacional de Pesquisas da Amazoˆnia, Manaus, Brazil, 76School of ecological and environmental sciences, East China Normal University, Shanghai, China, 77Department of Plant Biology, University of Minnesota, St. Paul, MN, USA, 78Departments of Biology & Natural & Applied Sciences, Lab Sciences 435, UW-Green Bay, Green Bay, WI 54311, USA Abstract Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for char- acterizing forest responses to global change. Within very large plots (median size 25 ha), all stems ≥1 cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25°S–61°N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 2 K. J . ANDERSON-TEIXEIRA et al. each of the world’s major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 °C), changes in precipitation (up to 30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8 g N m2 yr1 and 3.1 g S m2 yr1), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5 km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS- ForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change. Keywords: biodiversity, Center for Tropical Forest Science (CTFS), climate change, demography, forest dynamics plot, Forest Global Earth Observatory (ForestGEO), long-term monitoring, spatial analysis Received 31 May 2014 and accepted 6 July 2014 Introduction Forests play key roles in biodiversity maintenance and climate regulation. Globally, they support over half of all described species and provide a range of valuable ecosystem services (Groombridge, 2002; Pan et al., 2013). Forests play a particularly significant role in cli- mate regulation; they contain ~45% of carbon (C) in the terrestrial biosphere and influence climate on local to global scales through their low albedo and high rates of evapotranspiration (Snyder et al., 2004; Bonan, 2008; Anderson-Teixeira et al., 2012; Pan et al., 2013). Global change pressures – including climate change, pollution, agricultural expansion, logging, nontimber forest prod- uct extraction, hunting, and the spread of invasive spe- cies – are affecting forests worldwide, threatening biodiversity, altering community composition, and driving feedbacks to climate change (Foley et al., 2005; Chapin et al., 2008; Wright, 2010). Understanding and predicting such changes will be critical to biodiversity conservation, management of ecosystem services, and climate protection. The Center for Tropical Forest Science (CTFS) – For- est Global Earth Observatory (ForestGEO) is a global network of forest research sites that is strategically poised for monitoring, understanding, and predicting forest responses to global change. This international partnership currently includes 59 long-term forest dynamics research sites in 24 countries (Fig. 1), which have been monitored continuously since as early as 1981 (Barro Colorado Island; Condit, 1995). The net- work applies a unique standardized tree census proto- col across all of the world’s major forest biomes, allowing comparison across sites (e.g., Condit, 2000; Muller-Landau et al., 2006a,b; Chave et al., 2008; Chis- holm et al., 2013, 2014). Supplementary measurements, also following standardized procedures, provide addi- tional information on plants, animals, and ecosystem processes, making it possible to identify ecological interactions that might otherwise be missed (e.g., Harri- son et al., 2013). This review describes the defining fea- tures of a CTFS-ForestGEO plot, the distribution and representativeness of CTFS-ForestGEO sites, supple- mentary measurements and their applications, global change pressures across the CTFS-ForestGEO network, and the impacts of these drivers documented to date. Attributes of a CTFS-ForestGEO plot The unifying measurement at all CTFS-ForestGEO sites is an intensive census of all freestanding woody stems ≥1 cm diameter at breast height (DBH), typically repeated every 5 years, that characterizes forest struc- ture, diversity and dynamics over a large spatial area (Table 1). Plot sizes are large, ranging from 2 to 120 ha, with a median size of 25 ha and 90% ≥10 ha (Table 2). Following standardized methodology, each individual (genet) is mapped, tagged, and identified to species when it first enters the census. In the case of multi- stemmed individuals, each stem ≥1 cm DBH (ramet) is censused. On each stem, diameter is measured at breast height (1.3 m) or above stem irregularities (Manokaran et al., 1990; Condit, 1998). The census includes both trees and shrubs; henceforth, the term “trees” will refer to all individuals in the census. An accompanying fine- scale topographic survey allows identification of topo- graphically defined habitat types (e.g., ridges, valleys, slopes; Condit, 1998). This core CTFS-ForestGEO proto- col has proved useful for a wide range of analyses (Table 1). Site distribution and representativeness This core tree census protocol has been applied to 59 sites distributed among all of the world’s major forest biomes, making CTFS-ForestGEO the only international forest monitoring network with global distribution (Fig. 1; Table 2). In total, 1653 ha of forest (>5.68 Correspondence: Kristina J. Anderson-Teixeira, tel. 1 540 635 6546, fax 1 540 635 6506, e-mail: teixeirak@si.edu © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 CTFS-FORESTGEO NETWORK 3 Fig. 1 Map of the CTFS-ForestGEO network illustrating its representation of bioclimatic, edaphic, and topographic conditions globally. Site numbers correspond to ID# in Table 2. Shading indicates how well the network of sites represents the suite of environmental fac- tors included in the analysis; light-colored areas are well-represented by the network, while dark colored areas are poorly represented. Stippling covers nonforest areas. The analysis is described in Appendix S1. Table 1 Attributes of a CTFS-ForestGEO census Attribute Utility Very large plot size Resolve community and population dynamics of highly diverse forests with many rare species with sufficient sample sizes (Losos & Leigh, 2004; Condit et al., 2006); quantify spatial patterns at multiple scales (Condit et al., 2000; Wiegand et al., 2007a,b; Detto & Muller-Landau, 2013; Lutz et al., 2013); characterize gap dynamics (Feeley et al., 2007b); calibrate and validate remote sensing and models, particularly those with large spatial grain (Mascaro et al., 2011; Rejou-Mechain et al., 2014) Includes every freestanding woody stem ≥1 cm DBH Characterize the abundance and diversity of understory as well as canopy trees; quantify the demography of juveniles (Condit, 2000; Muller-Landau et al., 2006a,b). All individuals identified to species Characterize patterns of diversity, species-area, and abundance distributions (Hubbell, 1979, 2001; He & Legendre, 2002; Condit et al., 2005; John et al., 2007; Shen et al., 2009; He & Hubbell, 2011; Wang et al., 2011; Cheng et al., 2012); test theories of competition and coexistence (Brown et al., 2013); describe poorly known plant species (Gereau & Kenfack, 2000; Davies, 2001; Davies et al., 2001; Sonke et al., 2002; Kenfack et al., 2004, 2006) Diameter measured on all stems Characterize size-abundance distributions (Muller-Landau et al., 2006b; Lai et al., 2013; Lutz et al., 2013); combine with allometries to estimate whole-ecosystem properties such as biomass (Chave et al., 2008; Valencia et al., 2009; Lin et al., 2012; Ngo et al., 2013; Muller-Landau et al., 2014) Mapping of all stems and fine-scale topography Characterize the spatial pattern of populations (Condit, 2000); conduct spatially explicit analyses of neighborhood influences (Condit et al., 1992; Hubbell et al., 2001; Uriarte et al., 2004, 2005; R€uger et al., 2011, 2012; Lutz et al., 2014); characterize microhabitat specificity and controls on demography, biomass, etc. (Harms et al., 2001; Valencia et al., 2004; Chuyong et al., 2011); align on the ground and remote sensing measurements (Asner et al., 2011; Mascaro et al., 2011). Census typically repeated every 5 years Characterize demographic rates and changes therein (Russo et al., 2005; Muller- Landau et al., 2006a,b; Feeley et al., 2007a; Lai et al., 2013; Stephenson et al., 2014); characterize changes in community composition (Losos & Leigh, 2004; Chave et al., 2008; Feeley et al., 2011; Swenson et al., 2012; Chisholm et al., 2014); characterize changes in biomass or productivity (Chave et al., 2008; Banin et al., 2014; Muller-Landau et al., 2014) © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 4 K. J . ANDERSON-TEIXEIRA et al. T ab le 2 C h ar ac te ri st ic s of th e C T FS -F or es tG E O si te s. Si te s ar e or d er ed al p h ab et ic al ly by bi og eo gr ap h ic zo n e (s en su O ls on et al ., 20 01 ), th en by co u n tr y, an d fi n al ly by si te n am e. M or e si te d at a ar e gi ve n in th e ap p en d ix (T ab le s S1 – S7 ) an d on li n e (h tt p :/ / w w w .c tf s. si .e d u / D at a) N o. Si te C ou n tr y K € op p en C li m at e zo n e† M A T (° C )‡ M A P (m m yr  1 ) ‡ D om in an t So il or d er (s )§ D om in an t ve ge ta ti on ty p e( s) ** N at u ra lD is t. R eg im e† † N sp ec ie s P lo t Si ze (h a) Y ea r es ta bl is h ed ‡ ‡ A fr ot ro pi cs 1 K or u p C am er oo n A m 26 .6 52 72 U lt ,O x B E W 49 4 55 19 96 2 It u ri (E d or o an d L en d a) D em oc ra ti c R ep u bl ic of C on go A f 24 .3 16 82 O x B E W ;A 44 5 40 19 94 3 R ab i G ab on A w 26 .0 22 82 O x B E W 34 2 25 20 10 4 M p al a K en ya C fb 17 .9 65 7 A lf ;V e B d D Fi ,A 22 12 0 20 11 A us tr al as ia 5 W an an g P ap u a N ew G u in ea A f 26 .0 35 00 A lf ;I n B E L ;E 50 0* 50 20 09 In do -M al ay a 6 K u al a B el al on g B ru n ei D ar u ss al am A f 26 .5 52 03 U lt B E L 85 0– 10 50 * 25 20 09 7 D in gh u sh an C h in a C fa 20 .9 19 85 B E 21 0 20 20 05 8 H ei sh id in g C h in a C fa 22 .0 17 44 B E 24 5 50 20 13 9 H on g K on g C h in a C w a 23 .3 23 99 O x B E H 67 – 14 7* 21 20 12 10 Ji an fe n gl in g C h in a A w 19 .8 16 57 U lt B E H 29 1 60 20 12 11 N on gg an g C h in a C w a 22 .0 13 76 O x B E ;B d D D 22 3 15 20 11 12 X is h u an gb an n a C h in a C w a 21 .8 14 93 O x B E W ;D 46 8 20 20 07 13 M u d u m al ai In d ia A w 22 .7 12 55 A lf B d D Fi ;A ;D 72 50 19 87 14 D an u m V al le y M al ay si a A f 26 .7 28 22 U It B E D ;A * 50 20 10 15 L am bi r M al ay si a A f 26 .6 26 64 U lt B E L ;D 11 82 52 19 91 16 P as oh M al ay si a A f 27 .9 17 88 U lt B E W 81 4 50 19 86 17 P al an an P h il ip p in es A f 26 .1 33 80 U lt ;I n B E H 33 5 16 19 94 18 B u ki t T im ah Si n ga p or e A f 26 .9 24 73 U lt B E A 34 7 4 19 93 19 Si n h ar aj a Sr iL an ka A f 22 .5 50 16 U lt B E W 20 4 25 19 93 20 Fu sh an T ai w an C fa 18 .2 42 71 U lt ;I n B E H 11 0 25 20 04 21 K en ti n g T ai w an A m 25 .4 19 64 B E H 95 10 19 96 22 L ie n h u ac h ih T ai w an C w b 20 .8 22 11 U lt B E H ;L 14 4 25 20 08 23 N an je n sh an T ai w an A w 23 .5 35 82 U lt B E ;B d D W ;H 12 5 6 19 89 24 Z en lu n T ai w an A m 22 .7 26 20 N E H 12 20 05 25 D oi In th an on T h ai la n d A w 20 .9 19 08 U It B E – 16 2 15 19 97 26 H u ai K h a K h ae n g (H K K ) T h ai la n d A w 23 .5 14 76 A lf B E ;B d D Fi ;D 25 1 50 19 92 27 K h ao C h on g T h ai la n d A m 27 .1 26 11 U lt ;I n B E W ;L 59 3 24 20 00 28 M o Si n gt o T h ai la n d A w 23 .5 21 00 B E ;B d D W 26 2 30 .5 20 00 © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 CTFS-FORESTGEO NETWORK 5 T ab le 2 (c on ti n u ed ) N o. Si te C ou n tr y K € op p en C li m at e zo n e† M A T (° C )‡ M A P (m m yr  1 ) ‡ D om in an t So il or d er (s )§ D om in an t ve ge ta ti on ty p e( s) ** N at u ra lD is t. R eg im e† † N sp ec ie s P lo t Si ze (h a) Y ea r es ta bl is h ed ‡ ‡ N ea rc ti c 29 H al ib u rt on C an ad a D fb 4. 2 96 2 B cD – 30 13 .5 20 07 30 Sc ot ty C re ek C an ad a D fc  3. 2 36 9 G e N E P T ;F i 12 – 15 * 21 20 13 31 H ar va rd Fo re st U SA D fb 9. 0 10 50 In B d D H 60 35 20 10 32 L il ly D ic ke y W oo d s U SA C fa 11 .6 12 03 In ;U lt ;A lf B cD W ;D ;I c 35 25 20 09 33 Sa n ta C ru z U SA C sb 14 .8 77 8 M o B E ;N E Fi ,W 33 16 20 07 34 Sm it h so n ia n C on se rv at io n B io lo gy In st it u te (S C B I) U SA C fa 12 .9 10 01 A lf B cD W ,I c 64 25 .6 20 08 35 Sm it h so n ia n E n vi ro n m en ta l R es ea rc h C en te r (S E R C ) U SA C fa 13 .2 10 68 U lt ;I n ;E n B cD H ;W 79 16 20 07 36 T ys on R es ea rc h C en te r U SA C fa 13 .5 95 7 A lf B cD D ;F i; Ic ;W 42 20 20 13 37 W ab ik on U SA D fb 4. 2 80 5 A lf B cD W 42 25 .6 20 08 38 W in d R iv er U SA C sb 9. 2 24 95 A n N E Fi ;W ;I n 26 25 .6 20 10 39 Y os em it e N at io n al P ar k U SA C sb 10 .2 10 65 A lf N E Fi ;W ;D ;I n 23 25 .6 20 09 N eo tr op ic s 40 Il h a d o C ar d os o B ra zi l C fa 22 .4 21 00 S B E 10 6 10 .2 20 04 41 M an au s B ra zi l A f 26 .7 26 00 O x B E W 14 40 * 25 20 04 42 A m ac ay ac u C ol om bi a A f 25 .8 32 15 U lt B E Fl 11 33 25 20 06 43 L a P la n ad a C ol om bi a C fb 19 .0 40 87 A n B E W 24 0 25 19 97 44 Y as u n i E cu ad or A f 28 .3 30 81 U lt B E – 11 14 50 19 95 45 B ar ro C ol or ad o Is la n d (B C I) P an am a A m 27 .1 25 51 O x B d D ;B E D ;W 29 9 50 19 81 46 C oc ol i P an am a A m 26 .6 19 50 O x; In B d D ;B E D ;W 17 6 4 19 94 47 Sa n L or en zo / Sh er m an P an am a A m 26 .2 30 30 B E D ;W 23 8 6 19 96 48 L u qu il lo P u er to R ic o, U SA A m 22 .8 35 48 O x; U lt B E H ;L 13 8 16 19 90 O ce an ia 49 L au p ah oe h oe U SA C fb 16 .0 34 40 A n B E W 21 4 20 08 50 P al am an u i U SA C fb 20 .0 83 5 H i B E W 15 4 20 08 P al ea rc ti c © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 6 K. J . ANDERSON-TEIXEIRA et al. T ab le 2 (c on ti n u ed ) N o. Si te C ou n tr y K € op p en C li m at e zo n e† M A T (° C )‡ M A P (m m yr  1 ) ‡ D om in an t So il or d er (s )§ D om in an t ve ge ta ti on ty p e( s) ** N at u ra lD is t. R eg im e† † N sp ec ie s P lo t Si ze (h a) Y ea r es ta bl is h ed ‡ ‡ 51 B ad ag on gs h an C h in a C fa 15 .9 14 10 In B E ;B d D Fl 23 8 25 20 11 52 B ao ti an m an C h in a C w a 15 .1 88 6 B d D A ;D 12 6 25 20 09 53 C h an gb ai sh an C h in a D w b 2. 9 70 0 A lf N E ;B cD 52 25 20 04 54 D on gl in gs h an C h in a D w b 4. 7 57 0 A lf B cD Fi 58 20 20 10 55 G u ti an sh an C h in a C fa 15 .3 19 64 U lt B E ;B d D Ic 15 9 24 20 05 56 T ia n to n gs h an C h in a C fa 16 .2 13 75 O x B E H ;D 15 3 20 20 08 57 Z ofi n C ze ch R ep u bl ic C fb 6. 2 86 6 S; In ;H i B cD ;N E W ;I n 12 25 20 12 58 Sp eu ld er bo s N et h er la n d s C fb 10 .1 83 3 In B cD W ;A 13 27 20 13 59 W yt h am W oo d s U K C fb 10 .0 71 7 E B cD 23 18 20 08 *M ea su re m en t in p ro gr es s. † A f: T ro p ic al w it h si gn ifi ca n t p re ci p it at io n ye ar -r ou n d ; A m : T ro p ic al m on so on ; A w : T ro p ic al w et an d d ry ; C sb -D ry -s u m m er su bt ro p ic al / m id -l at it u d e cl im at e w it h d ry su m - m er s (a .k .a .: W ar m -s u m m er M ed it er ra n ea n ); C fa :H u m id su bt ro p ic al / m id -l at it u d e cl im at e w it h si gn ifi ca n t p re ci p it at io n ye ar -r ou n d ;C w a: H u m id su bt ro p ic al / m id la ti tu d e cl i- m at e w it h d ry w in te rs ; C fb : O ce an ic w it h si gn ifi ca n t p re ci p it at io n ye ar -r ou n d ; C w b: O ce an ic w it h d ry w in te rs ; D fb : H u m id C on ti n en ta l w it h si gn ifi ca n t p re ci p it at io n ye ar - ro u n d ;D w b: H u m id co n ti n en ta lw it h d ry w in te rs ;D fc :S u ba rc ti c. ‡ C li m at e d at a ar e th e be st av ai la bl e fo r ea ch si te (b as ed on ju d gm en t of si te P Is ;y ea rs va ry ). Fo r si te s w h er e lo ca l d at a ar e n ot av ai la bl e or n ot re p or te d ,v al u es (i ta lic iz ed ) ar e m ea n 19 50 – 20 00 cl im at e fr om W or ld C li m at 30 ar cs ec on d re so lu ti on (T ab le S4 ;H ijm an s et al ., 20 05 ). § C at eg or ic al fo ll ow in g th e U SD A So il T ax on om y Sy st em (S oi lS u rv ey St af f, 19 99 ): A lf ,A lfi so ls ;A n ,A n d is ol s; E ,E n ti so il s; G e, G el is ol s; H i, H is to so ls ;I n ,I n ce p ti so ls ;O x, O xi so ls ; U lt ,U lt is ol s; S, Sp od os ol s; V e, V er ti so ls . ** B E ,b ro ad le af ev er gr ee n ;B d D ,b ro ad le af d ro u gh t d ec id u ou s; B cD ,b ro ad le af co ld d ec id u ou s; N E ,n ee d le le af ev er gr ee n . † † A , an im al ac ti vi ty (d es tr u ct iv e) ; D , D ro u gh t; E , E ro si on ; Fi , Fi re ; Fl , fl oo d ; H , h u rr ic an e/ ty p h oo n ; Ic , Ic e st or m s; In in se ct ou tb re ak s; L , la n d sl id es ; P T , p er m af ro st th aw ; W , w in d st or m s (l oc al ); ‘– ’, n o m aj or n at u ra ld is tu rb an ce s. ‡ ‡ W h en ce n su s sp an n ed m u lt ip le ye ar s, th e fi rs t ye ar is li st ed . © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 CTFS-FORESTGEO NETWORK 7 million individuals) are currently monitored, with a cumulative sum of >17 000 ha-years of forest monitor- ing. CTFS-ForestGEO sites cover a wide diversity of phys- ical and biotic environments (Figs 1 and 2; Table 1, Table S1). The network spans latitudes 25°S–61°N, with sites in every biogeographic realm (sensu Olson et al., 2001; Table 1, Table S1). Climate varies widely (Fig. 2; Table 1, Table S2): mean annual temperature (MAT) ranges from 3.2 °C (Scotty Creek, Canada) to 28.3 °C (Yasuni, Ecuador), and mean annual precipitation (MAP) from 369 mm yr1 (Scotty Creek, Canada) to 5272 mm yr1 (Korup, Cameroon). Elevation ranges from 3 m.a.s.l. (Ilha do Cardoso, Brazil) to 1911 m.a.s.l. (Yosemite, USA), and relief from 4 m (SERC, USA) to 298 m (Tiantongshan, China; Table S1). According to the Soil Survey Staff (1999) soil classification, 11 of the world’s 12 soil orders are represented (the exception is Aridisols; Table 1), with corresponding marked varia- tion in fertility. The CTFS-ForestGEO network is generally represen- tative of the range of bioclimatic, edaphic, and topo- graphic conditions experienced by forests globally (Fig. 1), as evidenced by a multivariate spatial cluster- ing analysis with 4 km resolution (Hargrove et al., 2003; Hoffman et al., 2013; Maddalena et al., 2014; Appendix S1). Particularly well-represented regions include tropical rain forests on upland or ‘tierra firme’ habitats – especially in the Indo-Malay biogeographic zone – and temperate forests of Eastern China and East- ern North America. Underrepresented regions include temperate forests in the Southern Hemisphere; seasonal forests and woodland savannas south and east of the Amazon and in Africa; the Rocky Mountains of North America; and boreal forests – particularly in the Pale- arctic biogeographic zone. On a finer scale, many of the CTFS-ForestGEO sites in Asia, Europe, and North America are on more topographically complex terrain compared to the original forest distribution, as are most remaining intact forests in these regions. Forests with extreme edaphic conditions – for example, mangrove, swamp, and peat forests – remain almost completely unrepresented. Dominant vegetation types of the CTFS-ForestGEO sites include broadleaf evergreen, broadleaf drought deciduous, broadleaf cold deciduous, and needle-leaf evergreen forests (Table 1). Floristically, the network has extensive coverage, with >10 000 tree and shrub species (and >14 000 unique site-species combinations). Unique tree floras that are not yet represented include the high-endemism forests of Madagascar; southern temperate forests in New Zealand, Australia, and southern South America; and dry forests in Africa and the southern and eastern Amazon. The sites are generally in old-growth or mature sec- ondary forests and are commonly among the most intact, biodiverse, and well-protected forests within their region. They are subjected to a range of natural disturbances (Table 1), and a number of sites have experienced significant natural disturbances in recent years (e.g., fire at Yosemite, typhoons at Palanan). In addition, most sites have experienced some level of anthropogenic disturbance (discussed below; Table S5). Supplementary measurements and applications At all sites, the core census is complemented by one or more supplementary measurements that provide fur- ther basis for standardized comparisons across the world’s major forest biomes. Supplementary measure- ments provide additional information on plants, ani- mals, and ecosystem and environmental variables (Table 3). In this section, we review CTFS-ForestGEO -specific protocols and other relatively standard Fig. 2 Current and projected future (2050) mean annual tem- perature and precipitation of CTFS-ForestGEO sites superim- posed upon Whittaker’s classic climate-biomes diagram (Whittaker, 1975; Ricklefs, 2007). Dots represent average climate from 1950 to 2000. Wedges represent the range of projected cli- mates through 2050 as projected by the HADGEM2-ES model; specifically, smaller and larger temperature increases represent IPCC’s RCP 2.6 and RCP 8.5 scenarios, respectively. Biome codes are as follows: TrRF, tropical rain forest; TrSF/S, tropical seasonal forest/savanna; SD, subtropical desert; TeRF, temper- ate rain forest; TeSF, temperate seasonal forest; W/S, wood- land/shrubland; TeG/D, temperate grassland/desert; BF, boreal forest; T, tundra. Data from WorldClim (worldclim.org); recent climate data differ from those in Table 1. Details on cli- mate data and analysis are given in Appendix S1; data are listed in Table S4. © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 8 K. J . ANDERSON-TEIXEIRA et al. Table 3 Summary of supplementary CTFS-ForestGEO measurement protocols applied at five or more sites Measurement N sites* Description Utility Plants Lianas 7 (15) Lianas are included as part of the core census; they are mapped, identified to species, and measured at breast height (1.3 m) Characterize liana abundance and diversity and changes therein (Schnitzer, 2005; DeWalt et al., 2015; Thomas et al., 2015); understand liana impacts on tree community (Ingwell et al., 2010). Functional traits 33 (39)† Traits characterized include three dimensions (maximum height and crown diameter); leaf traits [size, specific leaf area, thickness, (N), (P), dry matter content]; wood traits (stem wood density, C content); and reproductive traits (dispersal mode; fruit, diaspore, and seed fresh and dry masses). Characterize species’ differences in physiology and ecological roles (Condit et al., 1996; Santiago & Wright, 2007; Muller-Landau et al., 2008; Kraft et al., 2010; Wright et al., 2010; Westbrook et al., 2011; Katabuchi et al., 2012; Liu et al., 2012); detect directional changes in functional composition (Feeley et al., 2011; Hietz et al., 2011; Swenson et al., 2012; Harrison et al., 2013); improve inventory-based C stock estimates (Martin & Thomas, 2011; Cushman et al., 2014); parameterize models High-precision diameter growth 28 (32) Diameter growth is measured weekly to annually using dendrometer bands on a subset of trees. Understand effects of tree size, species, and environmental conditions on growth; characterize seasonal growth patterns (McMahon & Parker, 2014); estimate the woody stem growth component of aboveground net primary productivity (ANPPwood) Flower & seed production 24 (33) Species-level flower & seed production are quantified using weekly to bimonthly censuses of 60–336 0.5-m2 traps. Quantify reproductive phenology (Zimmerman et al., 2007); infer seed dispersal distances (Muller-Landau et al., 2008); quantify interannual variation and its ecological implications (Wright et al., 1999, 2005; Harms et al., 2000; Usinowicz et al., 2012); detect directional changes (Wright & Calderon, 2006) Seedling performance 21 (30) Seedling establishment, growth and survival are quantified annually in three 1-m2 plots associated with each seed trap. Characterize density- and distance- dependent effects on con- and hetero- specific seedling recruitment (Harms et al., 2000; Comita et al., 2010; Lebrija- Trejos et al., 2013); Understand postdisturbance successional dynamics (Dalling et al., 1998; Dalling & Hubbell, 2002) DNA barcoding 27 (28) Short DNA sequences from a standard position within the genome are used to construct phylogenies and distinguish individual species from one another. Can be applied to all tissues of the plants (e.g., roots, pollen, leaves, and bark) or animals. Over 3000 plant species have been barcoded to date. Build phylogenetic trees of local community relationships and investigate constraints on the assembly of communities (Pei et al., 2011; Swenson et al., 2011; Lebrija-Trejos et al., 2013); identify tree roots to species (Jones et al., 2011); reconstruct networks of feeding, pollination, and parasitism (Hrcek et al., 2011) Animals Arthropods 5 (13) © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 CTFS-FORESTGEO NETWORK 9 Table 3 (continued) Measurement N sites* Description Utility A variety of key taxa are monitored 1–4 times annually‡using a variety of techniques (light traps, Winkler extractors, McPhail traps, butterfly transects, termite transects, and bee baits). Elucidate the role of arthropods in forest ecosystems (Novotny et al., 2002; Novotny & Basset, 2005); evaluate the impact of global change on the full range of forest trophic levels Vertebrates 14 (34) Camera trapping is used to monitor terrestrial mammals. Elucidate the role of vertebrates in forest ecosystems; detect directional changes Ecosystem & Environmental Aboveground biomass 59 Ground based: Biomass is estimated from core census data using best available allometries, often in combination with site-specific height and wood density data. Characterize spatial variation in biomass within sites in relation to environmental gradients and species diversity (Valencia et al., 2009; Chisholm et al., 2013); detect directional changes in C stocks (Chave et al., 2008; Muller-Landau et al., 2014); calibrate and evaluate models of biomass based on airborne LiDAR (Asner et al., 2011; Mascaro et al., 2011; Rejou-Mechain et al., 2014) (15) Airborne: LiDAR flights (one-time or repeated) provide data on biomass and tree architecture. Dead wood/CWD 21 (25) Standing dead wood and fallen coarse woody debris are surveyed by transect or comprehensive survey. Quantify C stocks in dead wood and changes therein Fine root biomass & soil carbon 16 (32) Measured to 3 m depth on every hectare, with additional replicates to shallower depths. Understand the role of associations between plants and mycorrhizal fungi in driving soil carbon storage (Peay et al., 2010; Averill et al., 2014) Soil nutrients 23 (26) Extractable soil cations, available N, nitrogen mineralization rates, and extractable phosphorus at 0 to 10- cm depths are measured at high spatial resolution. Characterize species’ microhabitat associations (Lee et al., 2002; Davies et al., 2003; John et al., 2007; Tan et al., 2009; Baldeck et al., 2013a,b,c; De Oliveira et al., 2014); characterize plant performance in relation to soil nutrients (Russo et al., 2005, 2013) Litterfall 21 (29) Litter is collected biweekly to monthly from traps, oven-dried, sorted (to leaves, woody, reproductive, and other), and weighed. In combination with woody growth data, quantify aboveground net primary productivity (ANPP) and its phenology and environmental drivers Bio- micrometeorology (13) Eddy-covariance technique is used to continuously measure CO2, H2O, and energy exchange between ecosystem and the atmosphere. Understand forest ecophysiology and C cycling on half-hourly to multiannual time scales Meteorology 5(33) Some sites have local meteorological stations within 10 km of the plot. Characterize climatic controls on forest processes such as flower and fruit production, tree growth and mortality, and ecosystem-atmosphere gas exchange (Condit et al., 2004; Wright & Calderon, 2006; Feeley et al., 2007a; Dong et al., 2012; Li et al., 2012) *Numbers indicate sites where measurements have been made or are in progress following a specific CTFS Forest GEO protocol. Numbers in parentheses indicate total number of sites with measurements using any protocol. †Varies by trait. Number indicates sites with measurements of one or more functional traits. ‡Varies by protocol. See Appendix S1 for details. © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 10 K. J . ANDERSON-TEIXEIRA et al. measurements that are comparable across sites. The Supplementary Information section provides further information on methodologies (Appendix S2) and details which measurements have been made at each site (Tables S6 and S7). Plants Supplementary measurements on plants include liana abundance and diversity, functional traits, high-preci- sion diameter growth, flower and seed production, seedling performance, and DNA barcoding (Table 3). Liana censuses help to characterize the important role of lianas in forest dynamics. Measurements of func- tional traits – well-defined, measurable properties of organisms that are strongly associated with ecological performance – provide information on key attributes and ecological roles of the species included in the cen- sus. High-precision growth measurements provide fine-scale understanding of temporal and spatial varia- tion in tree growth and forest productivity. Flower, seed and seedling censuses enable study of complete tree life cycles, which are critically important for forest regeneration and long-term species persistence. DNA barcoding provides a powerful means of species identi- fication that allows elucidation of phylogenetic relation- ships and ecological roles (Dick & Kress, 2009; Kress et al., 2009, 2010). Animals- Arthropod and vertebrate initiatives (Table 3) yield understanding of the roles of these taxa in forest dynamics through their roles as herbivores, pollinators, seed dispersers, predators, ecosystem engineers, and vectors of microbial diversity. In a unique effort to monitor multitaxon assemblages in tropical rainforests (Basset et al., 2013; but see Leidner et al., 2010 for long- term monitoring of a single taxon), key arthropod groups are being monitored to better understand how interactions between arthropods and plants affect forest dynamics and to evaluate the impact of global change on the full range of forest trophic levels. Vertebrate monitoring is helping to elucidate how mammals dif- ferentially affect tree species and how modification of the fauna may impact the future forest (e.g., Wright et al., 2007; Harrison et al., 2013; see below). Ecosystem and environmental Supplementary measurements of ecosystem and envi- ronmental variables include major aboveground C stocks and fluxes (aboveground biomass, standing dead wood and coarse woody debris, ANPPwood, litter- fall, net ecosystem exchange); soil nutrients, C, and fine root biomass; bio-micrometeorology, and meteorology (Table 3). These measurements provide a basis for understanding environmental and biotic controls on C stocks and fluxes within forest ecosystems and how these may respond to global change. Soils measure- ments provide a basis for understanding the critical role of soils in determining species composition, forest structure, and primary productivity, as well as their globally significant role as an important C reservoir. Bio-micrometeorological measurements further eluci- date the important role of forests in climate regulation through ongoing exchange of CO2, H2O, and energy between the ecosystem and the atmosphere. Meteoro- logical data are critical for understanding how the bio- tic community and whole ecosystem processes respond to climate variables over half-hourly to multiannual time scales. Combined applications In combination, the core tree census and supplemen- tary measurements enable unique analyses of the many interacting components of forest ecosystems, yielding a holistic picture of forest dynamics. For instance, core census data have been combined with data on lianas, vertebrates, seeds, seedlings, and reproductive func- tional traits to link decreasing populations of seed dis- persers to changing patterns of plant reproduction, liana abundance, and tree growth and survival (Wright & Calderon, 2006; Wright et al., 2007; Ingwell et al., 2010; Harrison et al., 2013). Core census, functional trait, and DNA barcoding data have been combined to understand the roles of phylogeny and functional traits in shaping habitat associations and diversity in space and time (Pei et al., 2011; Swenson et al., 2011). The combination of core census data, plant functional traits, ecosystem measurements, soils data, and weather data lend themselves to parameterizing and evaluating eco- system and earth system models. Thus, the broad suite of standardized measurements at CTFS-ForestGEO sites (Tables 1 and 3) provides opportunities to address a multitude of questions on forest dynamics and their responses to global change pressures. Global change pressures at CTFS-ForestGEO sites All ecosystems on Earth – including CTFS-ForestGEO’s relatively intact forests – are affected by anthropogenic influences (Fig. 3). Human appropriation of land and water for agriculture and other purposes; emission of extraneous compounds to the atmosphere (e.g., CO2, CH4, N2O, NOy, NHx, SO2) and water (e.g., NO3 , PO4 3); extraction of food, fuel, and fiber from natural © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 CTFS-FORESTGEO NETWORK 11 ecosystems; and transport of species around the globe has so pervasively influenced Earth’s climate, hydrol- ogy, biogeochemistry, land cover, and species diversity as to warrant classification of a new geologic period in Earth’s history – the Anthropocene (Schlesinger, 2012; Vitousek et al., 1997a; Zalasiewicz et al., 2010, 2011). Over the lifetime of the CTFS-ForestGEO network, atmospheric CO2 has increased 16%, from 340 ppm in 1981 to 396 ppm in 2013 (Tans & Keeling, 2014), with variable effects on climate globally. Over a similar time frame, temperatures have increased across the network by an average of 0.61 °C, with greater increases at colder sites (Figs 3 and 4; Table S3; details on data and analysis in Appendix S1). On both annual and daily time scales, minimum temperatures have increased more than maximum temperatures, leading to decreases in the diurnal temperature range. Frost-day frequency has decreased at sites that experience frost. Potential evapotranspiration (PET) has increased slightly on average (+2.5%) – particularly at low-PET sites. A tendency for increased cloud cover has offset the increases in PET that would be expected based on temperature increases alone, and high-PET sites have therefore experienced little change in PET on average (Fig. 4). Changes in mean annual precipitation (MAP) and wet-day frequency have been variable, with an overall tendency toward increases (averaging 6.0% and 2.7%, respectively) – particularly at high-precipitation sites (Fig. 4). Changes to the difference between annual MAP and PET have also been variable, with a tendency for wet sites (high MAP-PET) to become wetter – partic- ularly in the Neotropical and Indo-Malay biogeograph- ic zones – and low MAP-PET sites to become drier (Fig. 4). Changes in seasonality and the number of months with precipitation40%. Generally speaking, percent tree cover on the landscape decreases with distance from the site, while recent (2000–2012) forest loss rates and forest fragmentation increase (Fig. 5; data from Hansen et al., 2013; see Appendix S1 for details). In addition to forest loss in the surrounding landscapes, the majority of sites have been exposed to past and/or ongoing extraction of timber or nontimber forest products, hunt- ing, or invasive species (Table S5). A few sites are have high human population density in the surrounding areas and are affected by urbanization. Forest responses to global change As described above, all CTFS-ForestGEO sites are expe- riencing multifaceted global change pressures (Fig. 3). With spatially explicit dynamic tree data for large forest dynamics plots and the additional measurements sum- marized above (Table 2), the network is poised to advance mechanistic understanding of the impact of global and environmental change on the world’s for- ests. Are forests changing? Change is the natural condition of forests (e.g., Baker et al., 2005; Laurance et al., 2009), which makes it chal- lenging to detect and attribute directional responses to global change pressures. A key finding from the net- work is that forests generally, and in particular tropical forests, are highly dynamic; for instance, in the first 18 years of monitoring at BCI, >40% of trees ≥1 cm DBH (or 34% ≥10 cm DBH) turned over, and 75% of all species changed in abundance by >10% (Leigh et al., 2004). Superimposed upon this dynamism, forests are responding to global change pressures. Data from the network reveal some generalities and long-term trends of change in forests worldwide. Forest composition in terms of species and functional groups has changed at multiple sites across the net- work, in different directions at different sites (Condit et al., 1996; Chave et al., 2008; Feeley et al., 2011; Makana et al., 2011; Swenson et al., 2011). An analysis of data from twelve CTFS-ForestGEO sites reveals that environmental variability – as opposed to demographic stochasticity – is the most important factor driving tree population dynamics on decadal time scales (Chisholm et al., 2014). Across relatively undisturbed tropical for- ests, the dominance of slow-growing species increased at nine of ten sites analyzed (significantly so at five sites), indicating that these forests may be recovering from past disturbances, even as they are impacted by a variety of global change pressures (Chave et al., 2008). In addition, at six tropical sites monitored over more than 10 years, there have been long term increases in the proportions of flowers and seeds produced by lianas (Fig. 6; Wright & Calderon, 2006; Wright, unpub- lished analysis) – a trend that corresponds with long (a) (b) (c) Fig. 5 Characterization of forest cover, fragmentation, and loss in the landscapes surrounding CTFS-ForestGEO sites, with dis- tance zones describing concentric circles centered at each site. (a) Average % tree cover in year 2012; (b) % loss of existing tree cover from 2000 to 2012 (note the vertical scale is the square of forest loss); (c) Forest fragmentation, as characterized by forest edge: area ratio in year 2012. Note that ‘forest’ can include agro- forestry areas. Data from (Hansen et al., 2013). Analysis meth- ods given in Appendix S1. Data for each site given in Table S5. © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 14 K. J . ANDERSON-TEIXEIRA et al. term increases in the abundance of lianas observed on BCI (Panama) and elsewhere in the tropics (Ingwell et al., 2010; Schnitzer & Bongers, 2011). While commu- nity change appears to be the rule rather than the exception across the network, and while there have been some instances of rapid change in forest composi- tion (e.g., Condit et al., 1995; Chave et al., 2008), there have not been any hugely dramatic changes such as a forest die-off affecting the majority of large trees at the network sites. Trends in various components of aboveground net primary productivity (ANPP) have also been moni- tored at some sites. Across the network, the woody component of NPP (ANPPwood) has increased or decreased, as a function of both climate change and succession. Forests globally are mixed in terms of their productivity trends (Laurance et al., 2004; Clark et al., 2010; Gedalof & Berg, 2010; Wright, 2010). For instance, decreases in ANPPwood were observed in tropical forests in Panama (BCI) during 1981–2005 and Malaysia (Pasoh) during 1990–2000 (Feeley et al., 2007b) and increases in ANPPwood were observed in secondary forests in Maryland, USA (SERC; McMa- hon et al., 2010). Notably lacking is evidence of con- sistent increases in ANPP, as might be expected based solely on increasing atmospheric CO2 concen- tration (e.g., Norby et al., 2005). In the tropics, allo- cation of NPP to reproduction appears to have shifted; at five of six tropical sites where flower and seed production has been monitored for more than 10 years, there has been a long-term increase in flower production but not seed production (Wright & Calderon, 2006; Wright, unpublished analysis). Ongoing monitoring of NPP and flower and seed production will be vital to characterizing trends in productivity and C allocation. Finally, changes in standing biomass over time have been detected. Across ten relatively undisturbed tropical forests, highly resolved estimates of net biomass change show that aboveground biomass increased on average 0.24  0.16 Mg C ha1 yr1 (Fig. 7; Chave et al., 2008). This value is comparable to (though slightly lower than) values recorded for net- works of small forest plots in Amazonia (0.62  0.23 Mg C ha1 yr1; Baker et al., 2004), and Africa (0.63  0.36 Mg C ha1 yr1; Lewis et al., 2009a). Com- bining published data for the CTFS-ForestGEO, RAIN- FOR, and AfriTRON tropical forest sites leads to an overall average of 0.34  0.11 Mg C ha1 yr1 based on a total of 8243 ha-years of monitoring (Muller- Landau et al., 2014). Ongoing monitoring will be important for quantifying trends in biomass in the global forests represented by CTFS-ForestGEO. What are the mechanisms by which global change impacts forests? While data from the CTFS-ForestGEO network add to abundant evidence that forests globally are changing (e.g., Soja et al., 2007; Lewis et al., 2009b; Allen et al., 2010; Wright, 2010), it is difficult to identify the mecha- nisms underlying such changes given ubiquitous and simultaneous changes in multiple global change drivers (Fig. 3). The information-rich nature of CTFS-Forest- GEO sites has yielded insights into the mechanisms of response to global change pressures. Warming is expected to alter forest dynamics, but predicting effects at the ecosystem scale remains a major scientific challenge (e.g., U.S. DOE, 2012). Moni- toring, physiological measurements, and nearby warm- ing experiments combine to yield insights into how Fig. 6 Ratio of flower production by lianas (33 species) to that of trees (48 species) over 17 years on Barro Colorado Island, Panama. Redrawn from Wright & Calderon (2006). Fig. 7 Aboveground biomass change in twelve tropical forests. Solid line represents mean for ten undisturbed sites; *indicates disturbed plots. Replotted from Chave et al. (2008) with an updated value for BCI (Muller-Landau et al., 2014; K.C. Cush- man, personal communications). © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 CTFS-FORESTGEO NETWORK 15 warming may impact forest dynamics. The effects of warming are perhaps most dramatic at Scotty Creek, Canada – the highest latitude site, which is experienc- ing rapid warming (Figs 2 and 3; Tables S3 and S4) – where accelerating permafrost thaw is resulting in tree functional decline near forested plateau edges (i.e., reduced sap flow, radial growth, and leaf area) and driving loss of forest area at a rate of 0.5% yr1 (Baltzer et al., 2014). At another Canadian site (Haliburton For- est), a heat wave event during spring leaf-out in 2010 resulted in a >50% decline in leaf area of the dominant tree species (Filewod & Thomas, 2014), and a large net ecosystem carbon loss in the same year (Geddes et al., 2014). However, at most temperate and tropical sites, the impacts of warming are less obvious and tend to be confounded by other aspects of global change (Fig. 3). Data from four tropical forest sites (BCI, Huai Kha Kha- eng, Lambir, and Pasoh) indicate that tree growth rate correlates negatively with nighttime temperature, as expected from increased respiration rates causing a reduced carbon balance (Feeley et al., 2007a; Dong et al., 2012) – a trend that has also been observed at an exter- nal site in Costa Rica (Clark et al., 2010). In contrast, warming experiments associated with two of the sites reveal that warming may also directly or indirectly increase woody productivity; specifically, soil warming at Harvard Forest has increased tree growth through increased N mineralization (Melillo et al., 2011), and chamber warming experiments in Panama revealed that increased nighttime temperatures increased seed- ling growth rates (Cheesman & Winter, 2013). Ongoing monitoring, experimentation, and modeling will be necessary to disentangle the diverse productivity responses of forests to warming. Warming may also shift C allocation to reproduction; flower production at BCI, Panama has increased with increasing temperature (Pau et al., 2013). Future warming (Fig. 2) will inevita- bly impact forests, and ongoing monitoring at CTFS- ForestGEO sites should help to document and explain these changes. Changes in aridity and drought severity have the potential to impact forests worldwide, including those in wet climates (Allen et al., 2010; Choat et al., 2012). Across the tropics, increases in aridity or the occurrence of severe droughts have led to forest “browning”, mor- tality episodes, or fires (Van Nieuwstadt & Sheil, 2005; Lewis et al., 2011; Zhou et al., 2014), and there is con- cern that potential future increases in aridity in some parts of the tropics could result in severe tropical forest dieback (e.g., U.S. DOE, 2012). Research across the CTFS-ForestGEO network has yielded insights into the role of aridity in shaping tropical forest dynamics. Droughts in Panama (BCI, San Lorenzo, and Cocoli) and Malaysia (Lambir) have revealed differential drought sensitivity by size class, microhabitat associa- tion, and functional type (Condit et al., 1995, 2004; Potts, 2003). In Panama, mild or even fairly strong drought increased both woody productivity and pro- duction of flowers and seeds – presumably because of increased solar radiation (Condit et al., 2004; Wright & Calderon, 2006). At a tropical dry forest in India (Mu- dumalai), drought increased mortality rate, but with a 2–3 year lag for larger trees (Suresh et al., 2010). These findings yield insight into how moist tropical forests may respond to predicted changes in aridity (Fig. 2; Table S4; IPCC, 2013). Beyond climate, impacts of other global change driv- ers have been observed across the CTFS-ForestGEO network. Nitrogen deposition (Fig. 1; Table S5) has altered forest biogeochemistry across the globe. Tem- perate forests are typically N limited; however, high N deposition at Haliburton Forest, Canada, has caused a shift from N to P limitation (Gradowski & Thomas, 2006, 2008), providing evidence of constraints on increases in temperate forest productivity driven by elevated CO2 and/or nitrogen deposition. Because tropical forests are typically limited by elements other than N, N deposition is not expected to increase the productivity of these forests (Matson et al., 1999). At the two tropical CTFS-ForestGEO sites where relevant measurements have been made, increased 15N concen- trations in plant tissues suggests substantial N deposi- tion and altered N cycles (Hietz et al., 2011). Specifically, on BCI, leaf N and d15N in recent (2007) samples were elevated relative to herbarium samples (~1968) (Hietz et al., 2011). These changes have been mechanistically linked to increased N availability through a nearby fertilization experiment, which increased foliar N concentrations and d15N by similar amounts but did not affect productivity (Wright et al., 2011; Mayor et al., 2014a,b). A similar increase in d15N was observed in wood from Huai Kha Khaeng, Thai- land (Hietz et al., 2011). These results imply that, in tropical forests, N deposition is accelerating N cycling without increasing productivity, and reduced cation availability resulting from N deposition may be one potential explanation for observed declines in tree growth rates at some tropical sites (see above; Matson et al., 1999). Habitat fragmentation (Fig. 5) and faunal degrada- tion have also been linked to altered dynamics at CTFS-ForestGEO sites. The CTFS-ForestGEO site near Manaus, Brazil, is part of the Biological Dynamics of Forest Fragments Project (BDFFP), which has revealed that forest fragmentation rapidly and profoundly alters tree, arthropod, bird, and primate communities, reduc- ing species diversity and shifting composition toward dominance of more disturbance-adapted species (Lau- © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 16 K. J . ANDERSON-TEIXEIRA et al. rance et al., 2006). Across the network, more highly fragmented sites (e.g., Witham Woods, UK; Bukit Ti- mah, Singapore; Lambir, Malaysia; Heishiding, China; Fig. 3; Table S5) tend to have degraded faunas, as indi- cated by the absence of apex predators and larger verte- brates that were present historically, whereas faunal communities tend to remain more intact in unfragment- ed forests such as Yasuni (Ecuador), Rabi (Gabon), and Scotty Creek (Canada) (Turner & T Corlett, 1996; La- Frankie et al., 2005; Laurance et al., 2012; Harrison et al., 2013;W.F. Laurance, personal communication). As detailed below, faunal degradation – whether caused by habitat fragmentation, hunting, or other pressures – has strong impacts on forest structure and dynamics. The strong influence of fauna on forest composition and dynamics (e.g., Wright, 2010; Estes et al., 2011; Sch- mitz et al., 2013) has been documented at several CTFS- ForestGEO sites. At Mpala, Kenya, an experiment excluding herbivores of different sizes and replicated across a rainfall gradient revealed that herbivores of different sizes influence the biomass and growth rates of trees and understory plants, plant community com- position, and small mammal communities (Goheen et al., 2013). At Mudumalai, elephants (Elephas maxi- mus) cause high mortality among the small- to med- ium-sized stems, particularly in a few favored forage species (Sukumar et al., 2005). At SCBI (Virginia, USA), where white-tailed deer (Odocoileus virginianus) popula- tions greatly exceed their historical levels, 20 years of deer exclusion from a 4-ha subsection of the CTFS-For- estGEO plot has resulted in a >4-fold increase in sap- ling abundance relative to heavily browsed forest outside the exclosure (McGarvey et al., 2013). Large impacts of mammalian herbivores have also been found in an exclosure study adjacent to the Pasoh plot site in Malaysia (Ickes et al., 2001), where native pigs (Sus scrofa) have a dramatic effect on tree recruitment. In Panama, comparison of forest plots protected from bushmeat hunting with those exposed to poachers revealed that by reducing the abundance of frugivores and seed dispersers, hunting decreases the abundance of plant species with seeds dispersed by these animals while increasing the abundance of species with seeds dispersed by bats, small birds, or mechanical means (Wright et al., 2007). The latter includes lianas whose seeds are much more likely to be dispersed by wind (60% of liana species vs. 25% of canopy trees and <10% of midstory and understory trees and shrubs). Lianas have thus increased disproportionately in abundance where hunters remove the frugivores that disperse the seeds of most tree species, hence hunting may have unforeseen consequences for carbon sequestration (Jan- sen et al., 2010). Directional change in tree communities driven by faunal degradation has also been demonstrated. At Lambir, where populations of large mammals and birds have been severely impacted by hunting, tree community dynamics changed profoundly from 1992 to 2008 (Harrison et al., 2013). Specifically, sapling densities increased and regenera- tion of tree species with animal-dispersed seeds decreased and became more spatially clustered (Harri- son et al., 2013). Thus, ongoing faunal degradation due to hunting and habitat fragmentation in many forests globally is expected to alter forest community composi- tion, tree dispersal and regeneration, species diversity, forest structure, and carbon cycling. CTFS-ForestGEO research has also shed light on com- munity interactions that will act to either magnify or buffer forest responses to global change. Species are linked to one another through complex webs of interac- tion. For example, mapping of quantitative trophic food- webs at Wanang (Papua New Guinea) and current efforts to document tritrophic foodwebs of seeds, seed predators and parasitoids at this same location, at Khao Chong (Thailand) and Barro Colorado Island (Panama) demonstrates the complexity of ecological interactions in forest ecosystems (Novotny et al., 2010). Studies of seed dispersal and seedling recruitment demonstrate the criti- cal role of vertebrates and insects in tree reproduction and the composition of future forests (e.g., Wright et al., 2007; Harrison et al., 2013). It is therefore unsurprising that global change impacts on one group cascade through the ecosystem. For example, as described above, dramatic reduction in large mammal and bird popula- tions at Lambir, Malaysia has altered the dynamics of tree dispersal and regeneration (Harrison et al., 2013). Similarly, in the light-limited moist tropical forests of Panama, El Ni~no events bring relatively cloud free, sunny conditions that enhance fruit production while subsequent La Ni~na events bring rainy, cloudy condi- tions, and lower levels of fruit production that can lead to famines, particularly among terrestrial frugivores and granivores (Wright et al., 1999; Wright & Calderon, 2006). Climate change is bringing changes in cloud cover and atmospheric transmissivity to PAR (Table S3) with cascading effects on frugivores, granivores, and the plant species with which they interact. At the same time, the diversity and complexity of for- est communities may serve to provide some resilience to global change. A diversity of tree physiological strat- egies implies a wide range of responses to global change that helps to provide ecosystem resilience (e.g., Isbell et al., 2011; Mori et al., 2013). For example, Pana- manian tree species have displayed a wide range of physiological responses to temperature variation (Cheesman & Winter, 2013; Slot et al., 2014), and trees of different species have generally responded differ- ently to experimental manipulation of CO2, tempera- © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 CTFS-FORESTGEO NETWORK 17 ture, or precipitation globally (Anderson-Teixeira et al., 2013). The resilience enabled by species diversity may be exemplified by the stability of biomass, size struc- ture, and functional composition of the BCI forest (Chave et al., 2008; Swenson et al., 2012) despite severe droughts that impacted drought-sensitive species (Con- dit et al., 1995, 1996). In addition, in the tropics, perva- sive negative density dependence – i.e., elevated mortality of a plant species in areas where it is abun- dant – may buffer change because as a species becomes rare, it will suffer less from negative density depen- dence (Comita et al., 2010). Thus, accounting for biodi- versity in ecosystem models will be important for predicting forest responses to climate change. While such complexity makes it challenging to predict forest responses to global change, it may serve to partially buffer forest response to global change, which might otherwise be more dramatic. Conclusions The CTFS-ForestGEO forest dynamics sites are repre- sentative of the world’s more intact forests, covering a diversity of geographical, climatic, edaphic, topo- graphic, and biotic environments (Figs 1 and 2; Table 2). Yet, even this selection of the world’s more intact forests is being impacted by multifaceted global change drivers (Figs 2–5). Because many interacting species and processes are simultaneously being affected by a variety of global change pressures, extracting a mechanistic understanding of observed forest changes is challenging, requiring a holistic understanding of the abiotic setting, site history, demography for all tree life stages, trophic interactions, and ecosystem-level pro- cesses. The broad suite of measurements made at CTFS-ForestGEO sites (Tables 1 and 3) makes it possi- ble to understand the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS-ForestGEO net- work is yielding insights into how and why the forests are changing. As global change pressures inevitably intensify (Fig. 2; IPCC, 2013), ongoing monitoring across the network should prove valuable for docu- menting and understanding multifaceted forest responses and feedbacks to the climate system. To pro- ject into the future, broad suite of variables measured at CTFS-ForestGEO sites (Tables 1 and 3) will be invalu- able for parameterizing and evaluating ecosystem and earth system models, particularly those that character- ize forest demography and differences among species or functional groups (e.g., Moorcroft et al., 2001; Medvi- gy et al., 2009). Together, CTFS-ForestGEO’s unique standardized core census (Table 1) and supplementary measurements (Table 3), applied across all of the world’s major forest biomes (Fig. 1; Table 1), will pro- vide mechanistic insight as forests change in the 21st century. Acknowledgements We thank everyone involved in the collection of the vast quan- tity of data and information in the CTFS-ForestGEO network; to F. Dentener and W. Laurance for providing data; E. Leigh, Y. Lin, J. McGarvey and A. Miller for helpful comments; E. Aikens, L. Gonzalez and M. Azimi for help with analysis and figures. 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Climate data for all CTFS-ForestGEO sites: aver- age for 1980–2012 from CGIAR-CSI climate data. Table S3. Recent climate change at CTFS-ForestGEO sites (difference between 2008–2012 and 1951–1980 average) cal- culated from CGIAR-CSI climate data. Table S4. Climate Change Projections for CTFS-ForestGEO sites. Table S5. Atmospheric deposition; forest degradation, loss, and fragmentation; and local anthropogenic disturbances at CTFS-ForestGEO sites. Table S6. Record of supplementary measurements at CTFS- ForestGEO sites. Table S7. Record of arthropod sampling at CTFS-ForestGEO sites. Table S8. Acknowledgement of funding to individual CTFS-ForestGEO sites. © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12712 22 K. J . ANDERSON-TEIXEIRA et al. Anderson-Teixeira et al. (2014), Global Change Biology   1 SUPPLEMENTARY INFORMATION CTFS-ForestGEO: A worldwide network monitoring forests in an era of global change Anderson-Teixeira et al. (2014), Global Change Biology CONTENTS Appendix S1. Data sources and analysis methods 5 1.  Climate  and  atmospheric  deposition  data   5  CGIAR-CSI Climate Data 5 WorldClim current and projected climate data 6 Atmospheric deposition 6 2.  Multivariate  spatial  clustering  analysis   7  3.  Analysis  of  forest  degradation,  loss,  and  fragmentation   8  Appendix S2. CTFS-ForestGEO measurement protocols 11 1.  Plants   11  1.1. Core census 11 1.2. Lianas 11 1.3. Functional Traits 11 1.3.1. Wood density (WD) 12 1.3.2. Height (H) 12 1.3.3. Crown traits (C) 12 1.3.4. Leaf traits (L) 13 Anderson-Teixeira et al. (2014), Global Change Biology   2 1.3.5. Reproductive traits (R) 13 1.4. High-precision diameter growth 13 1.4.1. Infrequent (<1 measurement/month) dendrometer band measurements (P1) 13 1.4.2. Frequent dendrometer (≥1 measurement /month) band measurements (P2) 14 1.5. Flower and seed production 14 1.6. Seedling performance 14 1.7. DNA barcoding of plants 15 2.  Animals   15  2.1. Arthropods 15 2.1.1. Light traps 15 2.1.2. Winkler 16 2.1.3. McPhail traps 16 2.1.4. Butterfly transects 16 2.1.5. Termite transects 17 2.1.6. Bee baits 17 2.1.7. Interaction studies: seed predation 17 2.1.8. DNA barcoding of arthropods 18 2.2. Vertebrates 18 2.2.1. Camera trapping: TEAM Protocol (P1) 18 2.2.2. Camera trapping: CTFS-ForestGEO Protocol (P2) 19 3.  Ecosystem  and  Environmental  Variables   19  3.1. Aboveground biomass 19 3.1.1. Ground based estimates 19 Anderson-Teixeira et al. (2014), Global Change Biology   3 3.1.2. Airborne LiDAR estimates 20 3.2. Dead Wood/ Coarse Woody Debris (CWD) 20 3.2.1. CWD long transect (P1) 20 3.2.2. Fallen CWD dynamics (P2) 21 3.2.3. Standing CWD dynamics (P3) 21 3.2.4. CWD comprehensive (P4) 21 3.3. Soil Carbon and Fine Root Biomass 22 3.4. Soil Nutrients 22 3.4.1. Soil nutrient mapping (P1) 22 3.4.2. Soil nutrient mapping-Turner protocol (P2) 22 3.4.3. Soil nutrient mapping-Turner protocol (P3) 23 3.5. Litterfall 23 3.6. Bio-micrometeorology 23 3.7. Micrometeorology 24 Table S1. Geographic coordinates, elevation data, and references to site descriptions for all CTFS-ForestGEO sites. 25 Table S2. Climate data for all CTFS-ForestGEO sites: average for 1980-2012 from CGIAR-CSI climate data. 28 Table S3. Recent climate change at CTFS-ForestGEO sites (difference between 2008-2012 and 1951-1980 average) calculated from CGIAR-CSI climate data. 31 Table S4. Climate Change Projections for CTFS-ForestGEO sites. 34 Table S5. Atmospheric deposition; forest degradation, loss, and fragmentation; and local anthropogenic disturbances at CTFS-ForestGEO sites. 37 Anderson-Teixeira et al. (2014), Global Change Biology   4 Table S6. Record of supplementary measurements made at CTFS-ForestGEO sites. 40 Table S7. Record of arthropod sampling at CTFS-ForestGEO sites. 44 Table S8. Site-specific acknowledgments for selected CTFS-ForestGEO sites. 46 References 50   Anderson-Teixeira et al. (2014), Global Change Biology   5 Appendix S1. Data sources and analysis methods 1. Climate and atmospheric deposition data CGIAR-CSI Climate Data In order to obtain standardized climate data for all sites, global climate data with 0.5 degree spatial resolution were downloaded from the CGIAR-CSI database (http://www.cgiar-csi.org/data) in January 2014. Specifically, we retrieved monthly data for 1951 – 2012 for ten variables: daily mean temperature (°C), monthly average daily minimum temperature (°C), monthly average daily maximum temperature (°C), diurnal temperature range (°C), frost day frequency (days), precipitation (mm), wet day frequency (days), cloud cover (%), and vapour pressure (hecta-Pascals) from the CRU-TS v3.10.01 Historic Climate Database for GIS (http://www.cgiar-csi.org/data/uea-cru-ts-v3-10-01-historic-climate-database). In addition, potential evapotranspiration (PET; mm day-1) estimates were obtained from the Global Potential Evapo-Transpiration (Global-PET) dataset (http://www.cgiar-csi.org/data/global-aridity-and-pet-database; Zomer, 2007; Zomer et al., 2008). Data for each CTFS-ForestGEO site was extracted and is available online (www.ctfs.si.edu/Data). Monthly data were used to calculate the annual values. Annual values were averaged over 1980-2012 to obtain climatic averages (Table S2). Recent change (Fig. 4, Table S3) was calculated as the difference between 2008-2012 and 1951-1980 average. Note: Comparison of available local weather station data (Table 2) to CRU data revealed close correlation for MAT (R2 >94%). However, CRU data tended to systematically underestimate MAP at sites with high MAP, particularly those receiving >3000 Anderson-Teixeira et al. (2014), Global Change Biology   6 mm yr-1 (e.g., Korup, Kuala Belalong, Sinharaja, Fushan, La Planada). Thus, CRU precipitation values for high precipitation sites should be considered probable underestimates. WorldClim current and projected climate data Current and projected future climate data (Fig. 2; Table S4) were downloaded from WorldClim (http://www.worldclim.org; Hijmans et al., 2005) in November 2013 at the highest available spatial resolution (30 arc-seconds for current climate; 30 seconds for future climate). Current climate is based on an interpolation of observed data, representative of 1950-2000 (v. 1.4). Future projections are based on predictions of the HadGEM2-ES model as part of the CMIP5 (IPPC Fifth Assessment) for the year 2050 (2041-2060 climatic average) under the lowest and highest emissions scenarios (RCP 2.6 and RCP 8.5, respectively). These data have been downscaled and calibrated using WorldClim’s current climate (v. 1.4) as a baseline, which makes it appropriate to compare current and future climate data from these sources (e.g., Fig. 2). Note: Comparison of available local weather station data (Table 2) to WorldClim data revealed close correlation for MAT (R2 >97%). However, WorldClim data tended to systematically underestimate MAP at sites with high MAP, particularly those receiving >3000 mm yr-1 (e.g., Korup, Kuala Belalong, Sinharaja, Fushan, La Planada). Thus, WorldClim precipitation values for high precipitation sites should be considered probable underestimates. Atmospheric deposition Anderson-Teixeira et al. (2014), Global Change Biology   7 Data on deposition of nitrogen (NOy and NHx) and sulfur (SOx) were obtained from the data set of N Dentener et al. (2006) (F. Dentener, personal communication). These data are estimates for the year 2000 and have one-degree resolution. 2. Multivariate spatial clustering analysis Multivariate Spatio-Temporal Clustering (MSTC) (Hoffman & Hargrove, 1999; Hargrove & Hoffman, 2004; Hoffman et al., 2008; Kumar et al., 2011) and network representativeness analysis (Hargrove et al., 2003; Hoffman et al., 2013) were used to calculate representativeness for the CTFS-ForestGEO network in forested and non-forested areas. These analyses require continuous grids of each variable for the extent of the study area. The data used for both the MSTC and for the subsequent representativeness analysis of the CTFS-ForestGEO network were 17 variables on a 4 km grid comprised of 13,719,022 map cells of global land area (Baker et al., 2010). The 17 variables in the dataset were: (1) precipitation during the hottest quarter (mm); (2) precipitation during the coldest quarter (mm); (3) precipitation during the driest quarter (mm); (4) precipitation during the wettest quarter (mm); (5) ratio of precipitation to potential evapotranspiration (unitless); (6) temperature during the coldest quarter (°C); (7) temperature during the hottest quarter (°C); (8) day/night diurnal temperature difference (°C); (9) sum of monthly Tavg where Tavg ≥ 5°C (°C); (10) integer number of consecutive months where Tavg ≥ 5°C (unitless); (11) available water holding capacity of soil (unitless); (12) bulk density of soil (g/cm3); (13) carbon content of soil (g/cm2); (14) nitrogen content of soil (g/cm2); (15) compound topographic index (relative wetness; unitless); (16) solar interception (kW/m2); (17) elevation (m). Fifty ecoregions were delineated using MSTC (Kumar et al., 2011). The regions produced by this unsupervised classification method were then labeled with ecoregion or land cover type names derived from a suite of expert maps compared with the spatial clusters using the Mapcurves algorithm developed by Hargrove et al. (2006). Forested areas were then extracted and combined to derive the global forested area delineated in Figure 1. Representativeness analysis provided a quantitative “dissimilarity score” for each of the CTFS-ForestGEO 59 sites using the Euclidean distance in 17-dimensional data space between each site and every other Anderson-Teixeira et al. (2014), Global Change Biology   8 cell in the map. The 59 individual site maps were then combined to create a single map by selecting the minimum value for each grid cell from the collection of 59 individual dissimilarity scores. The final map is the minimum representativeness surface for the entire network. For a high resolution version of Figure 1 and additional figures and information from the MSTC analysis, Mapcurves analysis, and representativeness analysis see Maddalena et al. (2014).   3. Analysis of forest degradation, loss, and fragmentation To evaluate forest degradation, loss, and fragmentation surrounding CTFS-ForestGEO plots, we performed a spatial and temporal analysis using global data on deforestation and forest cover and change with 30m resolution (Hansen et al. 2013, data downloaded February 2014 from http://earthenginepartners.appspot.com/science-2013-global-forest). Raw raster data was downloaded for: (a) Tree canopy cover, defined as ‘canopy closure for all vegetation taller than 5 m in height’, in the year 2000 (%); (b) pixels converted from forest to other land uses between 2000 and 2012; and (c) areas of no data, mapped land surface, and permanent water bodies. A separate raster of forest area was calculated from the tree canopy cover raster using a threshold function that defined terrestrial land surface pixels having greater than 10% canopy cover as forest, following the definition used by FAO (2000). To define areas of original forest cover surrounding each site, a global raster map of original pre-human modification forest cover produced by UNEP-WCMC was downloaded April 2014 from http://www.unep-wcmc.org/generalised-original-and-current-forests-1998_718.html. Only four sites had less than 100% original forest coverage within 50km. All spatial statistics were limited to terrestrial land areas of original forest cover. Anderson-Teixeira et al. (2014), Global Change Biology   9 Spatial analyses were performed in R (R Core Team, 2013) using the raster, geosphere, and rgdal packages using parallel processing via the foreach and dosnow packages. The land surrounding each CTFS-ForestGEO plot was buffered into five distinct spatial zones: (i) within the plot (but not including the entire plot; calculated as a circle originating at the plot center with a radius of half the smaller plot dimension); (ii) from the plot to 1 km distance; (iii) from 1-5 km; (iv) from 5-25 km; and (v) from 25-50 km. Three core metrics were calculated: (a) percentage tree cover in 2012; (b) percentage of tree cover present in 2000 that was lost by 2012, and (c) forest fragmentation, defined as the length of forest edge adjacent to a deforested area (i.e., an area of original forest no longer forest) per unit forest area (units: km km-2). An index of forest degradation was calculated for the purpose of comparing the severity of forest degradation and loss across sites (e.g., Fig. 3). Specifically, the index is the average of eight numbers: % reduction in tree cover relative to plot (calculated from ‘a’ above) and % forest loss from 2000-2012 (‘b’ above), each at the four distance zones outside of the plot (ii-v above). Thus, the index integrates forest loss across a range of distances from the plot, giving more weight (on a per-area basis) to the area immediately surrounding the plot. It combines historical (pre-2000) and recent (2000-2012) forest loss, giving more weight to recent forest loss. It is important to note that the Hansen et al. (2013) dataset does not distinguish between natural forest and agroforestry areas; agroforestry areas with greater than 10% canopy cover and vegetation taller than 5 m in height are included in this definition of “forest”. Thus, “forest cover” in the surrounding landscapes is not necessarily primary or natural forest, and “forest loss” may include cutting of agroforestry plantations (i.e., as part of a rotation cycle). For example, at Pasoh (Malaysia), oil palm and rubber plantations are a feature of the landscape around the reserve, and “forest loss” from 2000-2012 adjacent to the reserve is attributable to the oil palm rotation, not to original forest loss. Moreover, the dataset does not distinguish between natural disturbance and deforestation; rather, “forest loss” implies either a stand-clearing disturbance or deforestation. Anderson-Teixeira et al. (2014), Global Change Biology   10 Selected results are provided in Table S5; full data are available for download at www.ctfs.si.edu/Data. Copies of R scripts used in the above analyses are available for download from the Harvard Dataverse Network at http://thedata.harvard.edu/dvn/dv/eben.   Anderson-Teixeira et al. (2014), Global Change Biology   11 Appendix S2. CTFS-ForestGEO measurement protocols This section describes the CTFS-ForestGEO core census and other protocols applied at five or more sites across the network (Table 3). 1. Plants 1.1. Core census Protocols for the tree core census are described in detail by Condit (1998). In brief, every free-standing woody stem>1cm DBH consist is identified to species, mapped, and tagged when it first enters the census within a plot. On each stem, diameter is measured at breast height (1.3 m) or above stem irregularities (Manokaran et al., 1990; Condit, 1998). The census is typically repeated every five years. Database standards and management practices are described in Condit et al. (2014). Analysis of CTFS-ForestGEO census data is commonly conducted using the CTFS R package, which includes functions to analyze tree abundance, growth, mortality and recruitment rates, biomass, and demographic changes (downloadable at http://ctfs.arnarb.harvard.edu/Public/CTFSRPackage/). 1.2. Lianas Lianas (woody vines) are inventoried as part of the core census at some sites. Lianas are mapped, identified to species, and measured at breast height (1.3m) according to the protocols detailed in Gerwing et al. (2006) and Schnitzer et al (2008). 1.3. Functional Traits Detailed methods for functional trait measurements are publicly available at www.ctfs.si.edu/group/Plant+Functional+Traits/Protocols. Below is a summary: Anderson-Teixeira et al. (2014), Global Change Biology   12 1.3.1. Wood density (WD) Wood density is measured for trees and lianas. Methods for collection may differ across sites, but processing methods are identical, following Cornelissen et al (2003). At sites where wood collection is prohibited due to the destructive nature of the method (e.g., BCI), samples are collected opportunistically from outside the permanent plot. Wood samples are collected with an increment borer for trees larger than 10 cm DBH, and a 10-cm long, 1-cm diameter stem segment is taken from lianas and shrubs. In some cases, 1-cm diameter branch samples are used in place of cores. Wood specific gravity is measured using the water displacement method to determine fresh volume. Samples are then dried in a convection oven (at 60°C) to finally calculate oven dried wood specific gravity (i.e., density). 1.3.2. Height (H) Tree height is measured either (1) on a size-stratified sample of trees (e.g., Bohlman & O’Brien, 2006) or (2) on the largest-diameter individuals in the plot for the purpose of estimating maximum tree height (Wright et al., 2010). Methods for measuring tree height are described online (http://www.ctfs.si.edu/data///documents/Crown_traits_draft.pdf) and in Larjavaara & Muller-Landau (2013); the CTFS-ForestGEO standard is to use what Larjavaara & Muller-Landau refer to as the sine method. 1.3.3. Crown traits (C) Crown traits measured across the network include crown diameter and crown exposure index. To estimate crown diameter (m), the crown radius is measured from the center to the edge of the crown in eight cardinal directions, then averaged. A qualitative crown exposure index serves as a proxy for light availability is recorded following a procedure adapted from Clark & Clark (1992). Full details are available online at http://www.ctfs.si.edu/data///documents/Crown_traits_draft.pdf. Anderson-Teixeira et al. (2014), Global Change Biology   13 1.3.4. Leaf traits (L) Six leaf traits are measured following the procedures of Cornelissen et al (2003): lamina size (mm2); specific leaf area (m2 kg-1); leaf thickness (µm); N concentration (mg g-1); P concentration (mg g-1); and dry matter content (mg g-1). The most recent tree census is used to randomly select 5-6 of the largest and smallest individuals of each tree species for sampling. Two to five leaves are measured for each individual. Fresh mass is recorded upon leaf removal and dry mass after drying at 60° C for 72 hrs. 1.3.5. Reproductive traits (R) Four reproductive traits are measured: dispersal mode (categorical), diaspore shape (unitless), diaspore mass (mg), and seed mass (mg). Diaspores are the unit that is dispersed by explosive force, by wind or by animals. Diaspores are dissected to isolate the embryo plus endosperms (i.e., seed). Collection of plant reproductive parts happens opportunistically and varies across sites subject to plant phenology. We attempt to collect five mature fruits from five individuals of each species, although for rare species or for those from which fruits rarely fall we collect single fruits or diaspores. Dispersal mode and shape classification follows Cornelissen et al (2003). 1.4. High-precision diameter growth 1.4.1. Infrequent (<1 measurement/month) dendrometer band measurements (P1) Metal or plastic dendrometer bands are installed on trees to obtain precise estimates of diameter growth. Bands are fixed to a stratified random subset of trees (n= 225 - 3,000; varies by site) and are measured one to four times per year using precision digital calipers. In temperate regions, measurements are made at the beginning and end of the growing season. Crown exposure index, crown condition (completeness), and sometimes liana coverage of the crown are also judged on a 5-point scale at every recensus. Protocols for construction, materials and installation of metal and plastic bands are available at http://www.ctfs.si.edu/group/Carbon/Protocol+Documents. Anderson-Teixeira et al. (2014), Global Change Biology   14 1.4.2. Frequent dendrometer (≥1 measurement /month) band measurements (P2) To resolve seasonal growth patterns, dendrometer bands installed on a subset of trees are measured at least once a month (commonly every two weeks) during the growing season. A workflow for optimizing the fit and interpretation of intra-annual growth measurements in a seasonal forest (SERC) is detailed in McMahon & Parker (2014). This paper outlines methods for fitting growth models to intra-annual measurements using R (R Core Team, 2013). 1.5. Flower and seed production Flower and seed production of trees and lianas is monitored using flower/seed traps (n=60-336; varies by site). Each flower trap has a surface area of 0.5 m2 and is elevated off the ground to reduce risk of seed predation. Traps are located randomly within plots (to represent different habitat types), or in a stratified random design at 4-13 m intervals on alternating sides of pre-existing trails. Specimens are collected weekly to bimonthly. All plant reproductive parts are identified to species, seed and fruits are counted and flowers recorded on a qualitative logarithmic scale. Details for trap construction and methods are available online (http://www.ctfs.si.edu/floss/page/methods/). 1.6. Seedling performance To monitor the establishment, growth, and survival of seedlings, three 1-m2 seedling plots are installed in association with each flower/seed trap (n≤1,008 seedling plots associated with ≤336 seed traps; n varies by site). Woody seedlings are identified, measured (height and number of leaves), and permanently tagged. They are monitored annually (quarterly at some sites) from germination until plants reach 1 cm DBH and enter the core census. Canopy photographs are taken over each seedling plot annually to assess light availability. The proximity of seed traps and seedling plots enables an evaluation of the seed-to-seedling transition through comparisons of seed inputs and seedling recruitment. Anderson-Teixeira et al. (2014), Global Change Biology   15 1.7. DNA barcoding of plants DNA sequences are being captured at multiple genetic loci for all tree species in the CTFS-ForestGEO network, with nearly 3,000 plant species sequenced to date (http://www.ctfs.si.edu/group/Science+Initiatives/DNA+Barcoding). Collection of plant samples for DNA barcode data begins with proper taxonomic identification of individual species from which a reference voucher and tissue sample are collected (see Kress et al., 2012 for workflow). Ideally, 4-5 individuals are sampled per species. Field collected samples consist of 0.1-0.5 grams of green leaf tissue that are placed in silica gel desiccant. Only 0.01 gram of tissue is used in DNA extraction for plants where PCR and sequencing follows Fazekas et al. (2012; see also http://ccdb.ca/resources.php). Sequence data are cleaned and aligned into a multi-gene sequence matrix using Geneious (version 7.0, Biomatters), and then used in maximum-likelihood based phylogentic reconstruction following Kress et al. (2009) to generate phylogenetic trees. Quantitative assessment of phylogenetic diversity metrics are conducted in R using the Picante package (see Swenson, 2012; picante.r-forge.r-project.org/ ). DNA barcode data are included in the BOLD database (e.g., Wabikon, USA: dx.doi.org/10.5883/DS-WABLK). 2. Animals 2.1. Arthropods Multi-taxon censuses are being conducted at five tropical sites (Table S6-S7), focusing on a target set of assemblages chosen for their ecological relevance, taxonomic tractability and ease of sampling (Table S7; http://www.ctfs.si.edu/group/arthropod%20monitoring/). 2.1.1. Light traps We use 10 W black light traps (automatic bucket-type model) fitted with intercept panes and a roof protecting catches from rain (Kitching et al., 2001). Traps are filled with crumpled paper to provide surface to hold moths and other insects so that they do not Anderson-Teixeira et al. (2014), Global Change Biology   16 loose most of their scales. Plastic, open egg trays separate larger insects from more fragile specimens. Insects are collected dry and killed by five strips of DDVP insecticide dispensed in the trap. The attraction range of one trap is < 50m (Baker & Sadovy, 1978). 2.1.2. Winkler To concentrate and extract litter ants, mini-Winkler eclectors (Besuchet et al., 1987; Agosti, 2000) are used from a 0.25 m2 sample of leaf litter. The litter is picked up from within a 0.25m2 frame, concentrated with a litter sifter and stored into a cloth bag. Each replicate (sample) is calibrated with a 400ml cylinder randomly scooped up and hung in a mini-Winkler. The extraction of material lasts for 72 hours. Ants are collected in ethanol and then processed as required. 2.1.3. McPhail traps McPhail traps (International Atomic Energy Agency, 2003; model from Biobest, www.biobest.be), baited with methyleugenol and cuelure are used to attract tephritid flies. The traps are running for a week and are set up in the vegetation, not in direct sunlight, at 3-4 m height. Attraction range of baits is < 100-200m (Cunningham & Couey, 1986). 2.1.4. Butterfly transects Walking transects of 500 m, timed to about 30 minutes (similar to Caldas & Robbins, 2003) are established to observe and catch butterflies. The observer restricts his/her attention to a 2 m wide strip across the transect and up to 5m height. For each transect, air temperature, relative humidity (%), and wind speed are also recorded. Cloudiness (%) is estimated visually. A full description of the protocol and how to implement it practically (establishment of local reference collection, etc.) is detailed in (Basset et al., 2013). Anderson-Teixeira et al. (2014), Global Change Biology   17 2.1.5. Termite transects Termite sampling transects are destructive (wood fragmentation, soil disturbance, etc.) and therefore are performed outside the permanent plots. Each year, we sample one transect of 400m, including 1 quadrat of 5m2 searched for 30 minutes by one person, every 10m (total 40 samples; Roisin et al., 2006). This include 4 different operations: (a) inspection of all trunks and branches for termite galleries up to 2m in height; (b) breaking any dead logs and branches; (c) scooping 6 smaller soil samples of ca. 15x15x10 cm; and (d) stirring and inspecting most of litter within the quadrat. 2.1.6. Bee baits Cineole baits are used to attract euglossine bees traps (Ackerman et al., 1982; Roubik, 2001), dispensed in McPhail traps (see item 3). The traps are baited with 7ml cineole and 100ml of commercial ethyleneglycol (car coolant) and run for a week. 2.1.7. Interaction studies: seed predation Non-rotting fruit and seeds from focal plant families are collected from inside and outside the plots. Fruits/seeds are processed as soon as possible after collection and placed in suitable rearing containers covered with black mesh and lined with tissue paper. Fruits of different species, tree individuals, collection sites, stage of maturity, size, and collection date are stored in separate rearing containers. Containers are checked a minimum of two times per week for emerging seed predators and parasitoids. Fruit/seeds are kept in a rearing shed for a period of three months. After this period, fruits/seeds are dissected before being discarded. In cases where developing larvae are encountered during dissection, fruits/seeds are returned to the rearing shed to allow for continued development of immature individuals. The protocol was adapted from (Janzen, 1980). Anderson-Teixeira et al. (2014), Global Change Biology   18 2.1.8. DNA barcoding of arthropods Field arthropod samples are collected by placing a leg of each individual into vells of a microplate filled with 95% ethanol. The voucher specimen is dry mounted, pictured and preserved in a local reference collection. Vouchers are later transferred into collections of national importance in the host country. Sample preparation and DNA sequencing for arthropods are detailed in Wilson (2012; see also http://ccdb.ca/resources.php). Sequences and voucher pictures are gradually becoming all public at http://www.boldsystems.org/. 2.2. Vertebrates The vertebrate program (http://www.ctfs.si.edu/group/vertebrates) is collecting data on vertebrates in selected sites across the ForestGEO network. To date, the focus is on ground-dwelling mammals, which are monitored using standardized camera trapping procedures. 2.2.1. Camera trapping: TEAM Protocol (P1) Terrestrial mammals are monitored following the terrestrial vertebrate monitoring protocol implemented by the Tropical Ecology Assessment and Monitoring Network (TEAM Network, 2011; see also http://www.teamnetwork.org). This protocol uses digital camera traps (60-90 camera traps points) at a density of 1 camera every 2 km² to monitor the status of species and changes in the community. Photographs are processed with an application called DeskTEAM (Fegraus et al., 2011). The data product is used to build annual occupancy and spatial occurrence models through sites. Protocols are available at http://www.teamnetwork.org/protocols/bio/terrestrial-vertebrate. Anderson-Teixeira et al. (2014), Global Change Biology   19 2.2.2. Camera trapping: CTFS-ForestGEO Protocol (P2) Terrestrial mammals are monitored using camera traps deployed at points in a 1-km² grid centered on each plot at a density of 1 camera trap / 2 ha (one hundred times more dense than TEAM protocol). The rates at which species pass in front of the cameras and are photographed are used as proxy for their abundance and can be compared between survey years and across plots. Photographs are securely stored and processed with custom-made database and processing tools (Kays et al., 2009).  Protocols are at http://www.ctfs.si.edu/group/vertebrates. 3. Ecosystem and Environmental Variables 3.1. Aboveground biomass 3.1.1. Ground based estimates Biomass is estimated from tree diameter, height, and wood density data (when available) using the best available allometric equations. In the tropics, calculations rely on standard allometric equations (e.g., Chave et al., 2005). In the temperate and boreal regions, species- and even site-specific allometric equations are sometimes available (e.g., Yosemite; Lutz et al., 2012), and generic allometries (e.g., Jenkins et al., 2003) are used when these are not available. Aboveground biomass (AGB) based on general allometric equations (currently Chave et al., 2005) can be calculated using the CTFS R package available at http://ctfs.arnarb.harvard.edu/Public/CTFSRPackage/index.php/web/tutorials/biomass/index. This code will soon be updated to take advantage of the newest tropical forest allometries (Chave et al. 2014). Anderson-Teixeira et al. (2014), Global Change Biology   20 3.1.2. Airborne LiDAR estimates Airborne LiDAR measurements have been made following a variety of protocols (e.g., Lefsky et al., 1999; Parker et al., 2004; Weishampel et al., 2007; Mascaro et al., 2011). There is not a specific CTFS-ForestGEO protocol. 3.2. Dead Wood/ Coarse Woody Debris (CWD) Two alternative sets of protocols for measuring necromass have each been implemented at multiple CTFS-ForestGEO sites. The CTFS Forest Carbon Research Initiative methods include CWD long transect, and fallen and standing CWD dynamics (P1-P3 below). An alternative method that has been employed at several temperate sites involves comprehensive inventories of all woody debris within the plot perimeter (P4 below). These methods are described below. 3.2.1. CWD long transect (P1) Dry mass of fallen woody debris per area is quantified using line-intersect surveys following Warren & Olsen (1964). An inventory of fallen coarse pieces (or CWD, >200 mm in diameter) is performed on the entire transect, and fine woody debris (or FWD, 20-200mm in diameter) on 10% of the transect (2 m of every 20 m). The diameter of each piece intersecting a transect is measured to enable estimation of the average volume of woody debris on the plot as a whole and its confidence limits. Where permitted, a sample is also taken from each piece to enable estimation of the dry mass of woody debris per unit area on the plot as a whole, with its confidence limits (Larjavaara & Muller-Landau, 2011). Where sampling on the plot is not allowed, other data on the wood density of woody debris are used instead. Hardness of coarse pieces is in all cases recorded using a penetrometer, and these values can be used as a basis for assigning wood densities (Larjavaara & Muller-Landau, 2010). The protocol is described in detail at http://www.ctfs.si.edu/group/Carbon/Protocol+Document. Anderson-Teixeira et al. (2014), Global Change Biology   21 3.2.2. Fallen CWD dynamics (P2) Fallen coarse woody debris (CWD; >200 mm diameter) is quantified using a repeated inventory of line transects. Transects are 20-m long within typically one hundred 40 m x40 m subplots (same subplots used for the standing CWD and the stratified sample of dendrometers). More details can be found in the online protocol document (http://www.ctfs.si.edu/group/Carbon/Protocol+Documents). 3.2.3. Standing CWD dynamics (P3) Standing dead trees are inventoried within 40 m x 40 m sub-plots. Standing CWD (>200 mm) are censused throughout the whole subplot, while standing FWD (20-199 mm) are censused only in the central area with a radius of 5 m. For each standing dead tree greater than 200 mm in diameter, dbh (or diameter above buttress), height, and hardness (using a penetrometer) are measured. In addition, the proportion of branches remaining is categorized. More details can be found in the online protocol document (http://www.ctfs.si.edu/group/Carbon/Protocol+Documents). 3.2.4. CWD comprehensive (P4) This alternative method of inventorying woody debris includes all deadwood objects within a plot perimeter (at some sites, only trees >100 mm dbh are measured). All pieces are outlined as vectors on a site local map, which allows posterior calculation of length and orientation plus local coordinates. Objects are sorted by two binary classifications into a “standing/lying” and “whole/broken” class. According to their combination and height attributes six deadwood types are defined: whole dead standing tree, broken dead standing stem (snag), whole dead lying tree, base part of dead lying stem, further parts of dead lying stem, and stump. Volume is calculated using DBH allometric equations (truncated cones for stem parts). A decomposition class (hardwood, touchwood, and disintegrated) is assigned to each piece to track tree individuals until their final decomposition (Král et al., 2014). Anderson-Teixeira et al. (2014), Global Change Biology   22 3.3. Soil Carbon and Fine Root Biomass Soil samples are systematically taken from around the center 20 x 20 m quadrat in every hectare at each plot. Soil is sampled to 3 m in the center of the quadrat, with additional samples taken to 1 m (x4) and 10 cm (x9) around the quadrat. Roots are separated by hand into fine roots < 2 mm and coarse roots > 2 mm diameter, dried at 60°C, and weighed. The soils are air-dried, sieved (<2 mm) and a subsample ground for analysis. Soil carbon concentration is determined by combustion and gas chromatography using a Thermo Flash EA 1112 Elemental Analyzer (for details, http://www.ctfs.si.edu/group/Carbon/Protocol+Documents) 3.4. Soil Nutrients 3.4.1. Soil nutrient mapping (P1) Soils are sampled using a regular grid of points every 50 m within sites. Each alternate grid point is paired with an additional sample point to capture variation in soil properties. 50 g of topsoil (0- to 10-cm depth) is collected at each sample point, and available cations and P are extracted using the Mehlich-3 extractant solution. N mineralization rates are measure on site using 3-inch diameter pipes 15 cm into the ground and incubated for 28 days (in-field incubation). Maps of estimated soil resource availability at the 10 x 10 m scale for each plot are then generated following John et al. (2007). 3.4.2. Soil nutrient mapping-Turner protocol (P2) More recent nutrient mapping has used Bray-1 solution to determine available phosphorus and 0.1 M BaCl2 to determine exchangeable base cations and extractable Al and Mn. The latter is preferred to the Mehlich extraction because it yields measures of effective cation exchange capacity, base saturation, and the potential toxins Al and Mn. It does not, however, provide extractable micronutrient data. Soil pH is determined in deionized water, 0.01 M CaCl2 and 0.1 M BaCl2. Anderson-Teixeira et al. (2014), Global Change Biology   23 3.4.3. Soil nutrient mapping-Turner protocol (P3) This method follows same steps as P2 above for cations but includes measurements of N mineralization (NH4 and NO3) using in-field resin bags. Briefly, mixed ion exchange resins are sealed in mesh bags and placed in the upper 10 cm of soil at the same sample locations as in P1 above. After three weeks, resin bags are removed, cleaned, and extracted in 0.5 M HCl. In addition to nitrogen, the extracts are also analyzed for P and base cations. 3.5. Litterfall Litter production of the stand, including trees and lianas of all species combined, is monitored using a set of aboveground and ground litter traps (n=100 pairs). Traps are located systematically or randomly within plots. Each aboveground litter trap has a surface area of 0.5 m2 and is elevated off the ground to reduce risk of seed predation. Ground traps are next to the aboveground trap and are used to monitor palm fronds and branchfalls of material that is too large to be captured in the aboveground traps. The traps are censused on a weekly to monthly basis. Trap contents are oven-dried at 65 C, then sorted into leaves, reproductive parts (flowers, seeds, fruits), fine woody material, and other. These fractions are weighed for each trap. Details of trap construction and methods are available online at http://www.ctfs.si.edu/group/Carbon/Protocol+Documents. 3.6. Bio-micrometeorology At or adjacent to 15 sites, ecosystem-atmosphere gas exchange has been measured using the eddy-covariance technique (e.g., Barford et al., 2001; Kume et al., 2011; Thomas et al., 2011; Kosugi et al., 2012; Soderberg et al., 2012; Wharton et al., 2012; Zhang et al., 2012). There is not a specific CTFS-ForestGEO protocol. While integration between flux measurements and core tree census data remains limited, these co-located measurements represent an important opportunity to link the growth and water use of individual trees to whole-ecosystem carbon cycling and evapotranspiration. Anderson-Teixeira et al. (2014), Global Change Biology   24 3.7. Micrometeorology Meteorological stations vary by site. At sites with meteorological stations installed as part of the CTFS Carbon Program (BCI, SCBI, Huai Kha Khaeng, Khao Chong, and Pasoh), a standardized meteorological station installed within or adjacent to the plot. The stations include several sensors recorded automatically by a CR1000 datalogger (Campbell Scientific) at a 5-minute interval. These sensors include: 1) an aspirated and shield temperature and a relative humidity sensor plus an additional secondary temperature sensor (MetOne Instruments); 2) a 2-D sonic anemometer WS425 (Vaisala); 3) a tipping rain bucket TB4-L (Campbell Scientific); and 4) a solar radiometer CMSP2 (Kipp & Zonen), plus a secondary radiometer LI-290 (LiCOR biogeoscience). In addition to meteorological data, some sites monitor soil temperature, moisture, and/or snow presence (e.g., Raleigh et al., 2013). Anderson-Teixeira et al. (2014), Global Change Biology   25 Table S1. Geographic coordinates, elevation data, and references to site descriptions for all CTFS-ForestGEO sites.   # Site Latitu de Longi tude Eleva tion-m in (m) Eleva tion- max ( m) Topog raphic relief (m) Site Description 1 Korup 5.07389 8.85472 150 240 90 Thomas et al., 2003, 2015; Chuyong et al., 2004 2 Ituri (Edoro and Lenda)* 1.4368 28.5826 700 850 150 Makana et al., 2004 3 Rabi -1.9246 9.88004 28 54 26 4 Mpala 0.2918 36.8809 1660 1800 140 Georgiadis, 2011 5 Wanang -5.25 145.267 90 190 100 6 Kuala Belalong 4.5384 115.154 160 320 160 7 Dinghushan 23.1695 112.511 230 470 240 Pei et al., 2011 8 Heishiding 23.27 111.53 435 698 263 Yin & He, 2014 9 Hong Kong 22.4263 114.181 145 257 112 10 Jianfengling 18.7308 108.905 866 1017 151 11 Nonggang 22.4333 106.95 370 180 190 Wang et al., 2014 12 Xishuangbanna 21.6117 101.574 709 869 160 Cao et al., 2008 13 Mudumalai 11.5989 76.5338 980 1120 140 Sukumar et al., 2004 14 Danum Valley 5.10189 117.688 15 Lambir 4.1865 114.017 104 244 140 Lee et al., 2003, 2004 16 Pasoh 2.982 102.313 70 90 20 Manokaran et al., 2004 17 Palanan 17.0402 122.388 72 122 50 Co et al., 2004 18 Bukit Timah 1.35 103.78 74 124 50 Lum et al., 2004; LaFrankie et al., 2005 19 Sinharaja 6.4023 80.4023 424 575 151 Gunatilleke et al., 2004 20 Fushan 24.7614 121.555 600 733 133 Su et al., 2007 21 Kenting 21.98 120.7969 250 300 50 Lin et al., 2011; Wu et al., 2011 Anderson-Teixeira et al. (2014), Global Change Biology   26 # Site Latitu de Longi tude Eleva tion-m in (m) Eleva tion- max ( m) Topog raphic relief (m) Site Description 22 Lienhuachih 23.9136 120.879 667 841 174 Lin et al., 2011; Chang et al., 2012 23 Nanjenshan 22.059 120.854 300 340 40 Sun & Hsieh, 2004 24 Zenlun 23.4247 120.5509 25 Doi Inthanon 18.5833 98.4333 1630 1710 80 Kanzaki et al., 2004 26 Huai Kha Khaeng 15.6324 99.217 549 638 89 Bunyavejchewin et al., 2004, 2009 27 Khao Chong 7.54347 99.798 110 360 250 28 Mo Singto 14.4333 101.35 725 815 90 Brockelman et al., 2011; Chanthorn et al., 2013 29 Haliburton 45.2901 -78.6377 412.5 454.4 41.9 30 Scotty Creek 61.3 -121.3 258 274 16 Chasmer et al., 2014 31 Harvard Forest 42.5388 -72.1755 340 368 28 Motzkin et al., 1999 32 Lilly Dickey Woods 39.2359 -86.2181 230 303 73 33 Santa Cruz 37.0124 -122.075 314 332 18 Gilbert et al., 2010 34 SCBI 38.8935 -78.1454 273 338 65 Bourg et al., 2013 35 SERC 38.8891 -76.5594 6 10 4 McMahon & Parker, 2014 36 Tyson Research Center 38.5178 -90.5575 172 233 61 37 Wabikon 45.5546 -88.7945 38 Wind River 45.8197 -121.9558 352.4 384.7 32.3 Lutz et al., 2013 39 Yosemite National Park 37.7662 -119.819 1774.1 1911.3 137.2 Lutz et al., 2012 40 Ilha do Cardoso -25.0955 -47.9573 3 8 5 de Oliveira et al., 2014 41 Manaus -2.4417 -59.7858 40 80 40 Gomes et al., 2013 42 Amacayacu -3.8091 -70.2678 Arias Garcia et al., 2009 43 La Planada 1.1558 -77.9935 1796 1840 44 Vallejo et al., 2004 Anderson-Teixeira et al. (2014), Global Change Biology   27 # Site Latitu de Longi tude Eleva tion-m in (m) Eleva tion- max ( m) Topog raphic relief (m) Site Description 44 Yasuni -0.6859 -76.397 215 245 30 Valencia et al., 2004 45 Barro Colorado Island 9.1543 -79.8461 120 160 40 Hubbell, 1979; Condit, 1998; Leigh et al., 2004 46 Cocoli 8.9877 -79.6166 Condit et al., 2004 47 San Lorenzo/ Sherman 9.2815 -79.974 Condit et al., 2004 48 Luquillo 18.3262 -65.816 333 428 95 Thompson et al., 2002; Thompson et al., 2004 49 Laupahoehoe 19.9301 -155.287 1150 1170 20 Ostertag et al., 2014 50 Palamanui 19.7394 -155.994 255 275 20 Ostertag et al., 2014 51 Badagongshan 29.46 110.52 1470 1369 101 Wang et al., 2014 52 Baotianman 33.4956 111.9397 241 53 Changbaishan 42.3833 128.083 792 810 18 Wang et al., 2009 54 Donglingshan 39.9566 115.425 1290 1509 219 Liu et al., 2011 55 Gutianshan 29.25 118.117 446 715 269 Lai et al., 2009; Ma et al., 2009; Lin et al., 2012 56 Tiantongshan 29.8116 121.783 304 602 298 Yang et al., 2011 57 Zofin 48.6638 14.7073 735 825 90 Král et al., 2010; Šamonil et al., 2011 58 Speulderbos 52.253 5.702 49 63 14 Wijdeven, 2003 59 Wytham Woods 51.7743 -1.3379 104 163 59 Butt et al., 2009; Thomas et al., 2011b                                                                                                                    * Ituri has four plots at two locations (Edoro and Lenda). Geographic coordinates are the midpoint value. Anderson-Teixeira et al. (2014), Global Change Biology   28 Table S2. Climate data for all CTFS-ForestGEO sites: average for 1980-2012 from CGIAR-CSI climate data. Additional climate data are available online (www.ctfs.si.edu/Data). Note: These values do not correspond exactly to values in Table 2 (most of which come from local weather stations measured over a range of time frames) or Figure 2 (which come from the WorldClim database). For high precipitation-sites within the CTFS-ForestGEO network, values from the CRU-TS v3.10.01 Historic Climate Database tend to underestimate MAP, dramatically so at some sites (e.g., Korup, Kuala Belalong, Sinharaja, Fushan, La Planada; see Appendix S1).   # Site Annu al tempe rature (°C) Januar y tempe rature (°C) July tempe rature (°C) Frost d ays (days/ yr) Annua l PET (mm/y r) MAP (mm/y r) Month s with PPT

100 >100 >100 >100 0.0 0.0 0.0 0.4 - - - - 0 P, A, W,E,H P, A, I 5 Wanang 0.06 0.11 0.79 129 136 142 141 6.5 3.2 2.9 2.8 5.8 3.5 2.5 2.6 2 H H 6 Kuala Belalong 0.18 0.19 0.18 100 99 91 83 0.0 0.9 3.6 6.0 0.0 0.6 3.2 4.2 5 h - 7 Dinghushan 0.67 2.16 1.85 92 76 46 39 0.0 8.3 8.8 13.0 3.0 6.6 13.5 20.3 22 8 Heishiding 0.63 2.25 1.60 35 17 63 73 14.2 30.0 16.9 14.8 33.4 47.1 19.9 16.2 36 9 Hong Kong 0.65 1.18 1.79 93 51 40 22 0.3 0.4 1.1 8.1 1.0 8.1 11.9 17.6 25 F, W, B, H, E e 10 Jianfengling 0.35 0.79 0.71 102 93 51 39 0.1 0.5 6.0 8.8 0.1 2.2 7.7 12.8 16 11 Nonggang 0.45 2.00 0.81 99 110 62 59 0.3 1.2 1.9 5.2 4.6 3.8 12.5 13.1 11 - - 12 Xishuangbanna 0.39 1.26 0.43 79 74 76 75 1.1 5.4 4.2 6.1 5.3 6.5 5.5 5.9 14 H 13 Mudumalai 0.38 0.95 0.79 106 95 66 54 0.0 0.0 0.7 0.8 0.2 2.0 7.1 9.0 11 W, H h 14 Danum Valley 0.14 0.13 0.17 102 103 90 79 0.2 0.5 6.8 13.9 0.3 0.6 4.4 6.8 7 15 Lambir 0.18 0.16 0.18 97 82 45 57 1.2 7.1 44.0 31.7 0.7 5.2 14.1 9.6 25 H, e H 16 Pasoh 0.32 0.41 0.41 99 53 52 61 0.1 44.0 32.9 24.1 0.1 5.2 12.8 10.0 30 h, e 17 Palanan 0.15 0.30 0.35 99 67 94 71 0.8 2.6 1.2 3.8 0.8 6.9 2.0 6.2 10 H, w, e - Anderson-Teixeira et al. (2014), Global Change Biology   38 18 Bukit Timah 0.32 0.25 0.49 70 37 22 50 0.3 2.3 17.2 28.0 7.3 17.4 24.3 13.9 34 F, CC 19 Sinharaja 0.20 0.47 0.36 98 93 82 68 0.1 0.5 1.2 1.9 0.1 1.1 4.1 8.4 8 e 20 Fushan 0.52 0.54 1.47 102 101 81 69 0.0 0.0 0.3 0.7 0.0 0.1 3.0 5.0 6 h 21 Kenting 0.35 0.34 0.81 84 68 79 90 1.7 1.7 2.0 3.1 4.2 8.0 5.4 4.2 11 F, e e 22 Lienhuachih 0.49 0.92 1.21 94 84 71 61 0.8 1.4 1.1 0.6 2.5 4.7 5.9 4.9 12 H, F h 23 Nanjenshan 0.44 0.63 1.07 97 80 70 75 1.3 1.6 2.3 3.4 1.1 5.0 4.9 5.0 11 - - 24 Zenlun 0.49 0.92 1.21 114 95 82 88 8.0 4.5 1.3 1.6 11.4 10.0 5.5 5.8 6 W W 25 Doi Inthanon 0.46 0.92 0.36 97 85 61 51 0.0 0.6 3.9 3.3 0.4 6.0 5.8 7.8 14 - - 26 Huai Kha Khaeng 0.45 0.65 0.43 96 98 83 64 0.1 0.0 0.3 0.5 0.1 0.0 1.0 2.5 7 - h 27 Khao Chong 0.23 0.28 0.27 98 92 57 47 0.0 2.1 12.0 14.2 0.3 2.4 12.8 15.5 17 28 Mo Singto 0.49 0.63 0.54 102 97 68 24 0.1 0.1 0.8 1.5 0.1 1.0 5.5 16.7 14 29 Haliburton Forest 0.51 0.31 0.87 100 98 97 95 0.1 0.6 0.4 0.6 0.4 1.1 1.4 1.9 1 30 Scotty Creek Forest Dynamics Plot** 0.03 0.02 0.74 138 139 184 177 0.0 0.0 0.1 0.3 - - - - 0 31 Harvard Forest 0.94 0.27 1.16 98 92 88 78 3.7 1.7 2.0 2.2 1.1 3.3 4.2 6.3 7 P, W,I I 32 Lilly Dickey Woods 0.99 0.59 1.73 99 82 67 29 0.1 0.2 0.4 0.5 0.4 4.4 6.1 13.3 15 W, b, h b, h, i 33 Santa Cruz 0.27 0.13 0.15 84 71 77 31 0.9 1.1 3.0 1.6 5.0 6.5 4.0 13.6 18 w I 34 SCBI 0.99 0.38 1.60 87 69 57 51 0.0 1.9 1.3 2.0 2.1 7.3 8.3 9.9 18 P, I I 35 SERC 1.07 0.32 1.51 78 53 49 37 4.3 1.3 2.3 3.7 7.0 13.9 13.5 16.4 24 36 Tyson Research Center 0.84 0.65 1.36 87 69 43 33 0.0 2.1 1.8 1.6 2.4 6.8 13.5 14.8 22 H, W, p, i i, h 37 Wabikon Lake Forest 0.40 0.34 0.59 95 93 83 81 1.5 0.8 2.8 3.4 0.6 1.8 4.3 3.9 7 W, h h 38 Wind River 0.18 0.19 0.21 81 93 89 71 0.0 1.3 2.1 8.8 2.9 1.6 1.9 5.7 10 I I 39 Yosemite National Park 0.26 0.10 0.14 92 85 56 32 4.0 7.6 6.0 5.8 2.7 5.0 6.9 12.0 20 I I 40 Ilha do Cardoso 0.29 0.44 0.26 100 95 92 88 0.4 0.0 0.3 1.4 0.4 1.6 2.2 3.2 3 41 Manaus 0.24 0.22 0.11 100 100 98 96 0.0 0.1 0.9 2.1 0.0 0.0 0.6 1.4 1 42 Amacayacu 0.20 0.10 0.09 100 95 95 96 0.0 1.4 1.7 2.1 0.0 1.8 1.3 1.0 2 43 La Planada 0.20 0.35 0.49 99 93 88 74 0.0 1.1 1.3 2.3 0.0 1.6 2.2 5.3 7 P E 44 Yasuni 0.19 0.28 0.30 99 98 99 97 0.0 0.2 0.1 1.8 0.1 0.7 0.2 0.8 1 cc e, h 45 Barro Colorado Island 0.20 0.20 0.23 101 100 69 63 0.1 0.1 4.3 6.7 0.1 0.4 8.4 11.4 10 w, f - 46 Cocoli 0.21 0.22 0.25 87 72 55 60 4.9 4.4 3.9 4.4 3.8 7.1 10.2 9.8 18 Anderson-Teixeira et al. (2014), Global Change Biology   39                                                                                                                † Codes are as follows: F-farming; P-pasture; W-wood harvesting; CC-clear cut/ complete clearing; B-burn; H-hunting; E-extraction of NTFP (non-timber forest products); I-invasive species; ‘-‘ no significant disturbances. Capital letters denote strong pressure; lowercase denote mild pressure. ‡ Forest cover/ loss/ fragmentation/ degradation are average values for four plots. § Tree cover at this savanna site falls below the 10% tree cover threshold used to classify forest. Therefore, calculations were not limited to areas originally classified as forest. Forest fragmentation index was unreliable due to low-density tree cover and therefore is not reported.  **  Forest fragmentation index was unreliable due to low-density tree cover and therefore is not reported.   47 San Lorenzo/ Sherman 0.20 0.20 0.23 101 99 79 65 0.3 0.7 4.8 7.5 0.4 1.2 7.2 11.3 9 48 Luquillo 0.09 0.07 0.13 99 83 49 54 0.3 1.4 2.8 2.3 0.5 5.7 15.3 12.6 15 CC, F 49 Laupahoehoe 0.04 0.04 0.12 100 95 42 33 0.0 0.1 0.5 0.9 0.0 0.7 6.7 8.4 16 I, A I, H 50 Palamanui 0.04 0.04 0.12 105 68 68 64 6.9 2.2 0.9 0.5 20.0 16.2 15.6 13.3 14 I, A I 51 Badagongshan 0.73 3.05 2.34 104 103 103 95 0.3 0.1 0.4 0.8 9.4 9.8 9.2 11.3 1 e 52 Baotianman 0.83 1.84 2.83 94 91 74 46 0.6 0.3 0.6 0.9 1.1 1.4 5.5 8.9 12 53 Changbaishan 0.38 0.74 0.84 101 95 95 87 0.0 0.6 0.9 1.0 0.2 2.2 3.0 4.7 3 w - 54 Donglingshan 0.64 0.81 2.18 78 35 28 18 0.0 0.2 0.2 0.5 6.3 19.9 22.5 26.5 30 CC 55 Gutianshan 0.94 2.02 2.93 95 87 74 71 0.1 0.2 4.1 3.2 0.3 2.4 6.3 5.8 10 w, b, H - 56 Tiantongshan 0.81 1.13 3.14 104 86 30 37 0.0 0.4 0.7 0.9 1.7 4.9 12.0 13.0 19 57 Zofin 0.76 1.09 1.08 90 80 50 45 8.8 11.3 4.6 5.0 7.1 9.5 13.2 13.7 21 w, h - 58 Speulderbos 0.82 1.48 1.00 80 61 38 20 1.2 3.0 5.1 2.8 4.9 9.3 17.1 29.0 27 59 Wytham Woods 0.73 0.97 0.96 60 7 5 8 0.0 0.7 2.0 1.7 7.2 48.3 47.1 40.2 40 P, W, h, I I Anderson-Teixeira et al. (2014), Global Change Biology   40 Table S6. Record of supplementary measurements made at CTFS-ForestGEO sites. Coded as follows: P(#): measured using standardized CTFS-ForestGEO protocol outlined in Appendix S2 (numbers differentiate multiple protocols in the same category); ‘N’- Will be measured by NEON (see NEON, 2011); ‘+’ measured (any protocol); '-' not measured or no information; * in progress; (f)-planned for near future, with funding. Other codes explained in footnotes. # Site N tree censu ses‡‡ Liana s Funct ional T raits§§ Dendr omete r Band s Flowe r & Se ed Produ ction Seedli ng Perfor mance DNA barcod ing*** Arthro pods†† † Verteb rates Airbo rne Li DAR Dead Wood / CWD Fine R oots Soil C Soil N utrien ts Litterf all Eddy Covar iance‡ ‡‡ Weath er stat ion§§§ 1 Korup 3 P L; SM; H; WD - - - P(p)* - P1 - - - P P1 - - A 2 Ituri (Edoro and Lenda) 3 P + - - - - - + - - - - + - - 3 Rabi 1 - - - - - P(p)* - - - - - - - - - A 4 Mpala 1* - + - - - P(p)* - + - - - - +; P2(f) - + A 5 Wanang 1 - + - - - P(a) P - - - - - P3 - - 6 Kuala Belalong 1* - - - - - - - - - - - - * - - 7 Dinghushan 2 - L; SM; H; WD P1 P P P(p) - P2 - - - - + P - + 8 Heishiding 1 P L; SM; H; WD - + + - - - - - - - - - - 9 Hong Kong 1* - - - - - P(p)* P* - - - - - - - - A, C 10 Jianfengling 1 - L; SM; H; WD * P P P(p) - - - - - + + P + A 11 Nonggang 1* - L; SM; H; WD - - - - - P2 - - - - - - - B 12 Xishuangbanna 1 + L; SM; H; WD P1 P P P(p) - P2 - - - - + - - - 13 Mudumalai 4 - L; SM; H; WD P1 - - - - + - P1 - - - P - A 14 Danum Valley 1* - SM - - - P(p)* - + - - - + P1 - - A 15 Lambir 4 - L; SM; H; WD P1 - - - - + - P1 P* P* + P + A 16 Pasoh 6 + L; SM; H; WD P1 P P - - P1 - P1; P2; P3 P P +; P1 P + A Anderson-Teixeira et al. (2014), Global Change Biology   41 # Site N tree censu ses‡‡ Liana s Funct ional T raits§§ Dendr omete r Band s Flowe r & Se ed Produ ction Seedli ng Perfor mance DNA barcod ing*** Arthro pods†† † Verteb rates Airbo rne Li DAR Dead Wood / CWD Fine R oots Soil C Soil N utrien ts Litterf all Eddy Covar iance‡ ‡‡ Weath er stat ion§§§ 17 Palanan 3.5 - L; H(f) - * * P(p) + + - - - - - * - (f) 18 Bukit Timah 6 - - P1 - - P(p) - - - P1; P3; P4 P P P2 P - A 19 Sinharaja 3 - - - + - - - + - - - - +; P1 - - 20 Fushan 3 - H; L; WD P1 P P P(p) + + + P1; P2; P3 - - + P - A 21 Kenting 3 - H; C; L; SM P1 P P P(p) - + - - - - - - - C 22 Lienhuachih 1 - L; SM; H; WD P1 P P P(p) - - - P1; P2; P3 - - + P - A 23 Nanjenshan 3 - L - - + P(p) - - - P1; P2; P3 - - - - - A 24 Zenlun 2 - - - P P - + + - - - - - - - A 25 Doi Inthanon 4 - WD; H - - - + - - - - - - - - - C 26 HKK 4 - - P1 + - - - + - P1; P2 P(f) P(f) +; P P - A 27 Khao Chong 3 - - P1 + + P(a) P - - P1 P(f) P(f) +*; P1; P3 P - A 28 Mo Singto 2.5 P - P1 - - - - + - P1; P2 - - - P - 29 Haliburton Forest 1*(3) - L; SM; H; C; WD; O - P - P(p) - + + + - P P2 P + A, B 30 Scotty Creek 1 n/a C; WD P1 - - P(a) - - + - P(f) P(f) - P(f) + A 31 Harvard Forest 1 - N P1; P2 - - - N N +; N N N N N +; N +; N A; N 32 Lilly Dickey 1 - - P1 - - - - - - - - P* +; P2* - - A 33 Santa Cruz 2 + H; C; L; WD(f) - P - P(p) - + + - - - + - - A 34 SCBI 2 + L; H; C; O P1; P2 P P P(p) +; N +; P2; N +; N P1; P3; P4; N P; N P; N +; P2; N P; N N A, N 35 SERC 1 P N P1; P2 + + P(p) N +; P2*; N +; N P4; N P; N P; N +; P3; N N +; N A, N 36 Tyson 1(4) - L; WD; O * + * - - * - P4 - - +; P2 + - A Anderson-Teixeira et al. (2014), Global Change Biology   42 # Site N tree censu ses‡‡ Liana s Funct ional T raits§§ Dendr omete r Band s Flowe r & Se ed Produ ction Seedli ng Perfor mance DNA barcod ing*** Arthro pods†† † Verteb rates Airbo rne Li DAR Dead Wood / CWD Fine R oots Soil C Soil N utrien ts Litterf all Eddy Covar iance‡ ‡‡ Weath er stat ion§§§ 37 Wabikon Lake Forest 2 - - P1 + + P(p) - + - - - - - - - 38 Wind River 1(2) n/a N P1 + - - N N +; N +; N N N P2(f); N N +; N A; N 39 Yosemite 2* n/a - P1 - - - - - + + - - - - - A 40 Ilha do Cardoso 1 - + - P - - - - - - - - P1/ P2/ + - - 41 Manaus 1 P - - - - P(p) - P1 - - - - - - - + 42 Amacayacu 1 - L; SM; H; WD P1 - - - - - - P1; P2; P3 P P P2 P +(f) 43 La Planada 2 - - - - - - - - - - - - P1 - - 44 Yasuni 3 + L; SM; H; WD P1 P P - P P1 + P1; P2; P3 P P P1; P2 P - A 45 BCI 7 P; + L; SM; H; C; WD; O P1 P P P(p); P(a) P +; P1; P2 + P1; P2; P3 P P P1 P + A 46 Cocoli 3 + - - - - - - - - - - - - - - C 47 San Lorenzo 1 + L; SM; H; WD P1 P P - - - + - - P P2 - - C 48 Luquillo 5 + L; SM; H; WD, C P1 P P P(p) + + - +; P P P P2 P - A 49 Laupahoehoe 1 - - - P P P(p)* - - + - - - - + - A 50 Palamanui 2* - - - P P P(p)* - - - - - - - + - A 51 Badagongshan 1* - - P1 P P - - P2 - - - - - P - 52 Baotianman 1 - - - P P - - P2 - - - - - - - 53 Changbaishan 2 - L; SM; H; WD P1 P P P(p) - P1; P2 - - - - + P - + 54 Donglingshan 1* - L; SM; H; WD - P P - - - - P1; P2; P3; P4 - - - - - 55 Gutianshan 2 - L; SM; H; WD P1 P P P(p) - + - P1; P2; P3 - - + P - B 56 Tiantongshan 1 - L; SM; H; WD - P P - - - - - - - - - - Anderson-Teixeira et al. (2014), Global Change Biology   43 # Site N tree censu ses‡‡ Liana s Funct ional T raits§§ Dendr omete r Band s Flowe r & Se ed Produ ction Seedli ng Perfor mance DNA barcod ing*** Arthro pods†† † Verteb rates Airbo rne Li DAR Dead Wood / CWD Fine R oots Soil C Soil N utrien ts Litterf all Eddy Covar iance‡ ‡‡ Weath er stat ion§§§ 57 Zofin 1(4) - + - - + P(p)* - - + P4 - + + - - A 58 Speulderbos 1 n/a - + - - - - P2* - - - - - - - B 59 Wytham Woods 2 - + + - + - - + + P1; P2; P3 + + + + + A                                                                                                                ‡‡ Number of censuses as of May 2014. Numbers in parentheses indicate total number of censuses including those prior to the sites adoption of the CTFS-ForestGEO core tree census protocol (i.e., censuses with any DBH cutoff and/or smaller plots). These include any in-progress survey. §§ H: tree height; C: crown dimensions; L: leaf traits; SM: seed mass; WD: wood density; O: other *** p- plants; a- arthropods ††† Arthropod measurements made using standardized CTFS-ForestGEO protocol are detailed in Table S7. ‡‡‡ Measured onsite or at a similar site within 10 km. §§§ A- onsite or a similar site within 10 km that is believed to have similar climate; B- nearby (within 50km), believed to have similar climate (e.g., similar elevation, distance from coast); C- nearby (within 50km), believed to have dissimilar climate (e.g., dissimilar elevation, distance from coast); '-' no known weather station within 50km; N-NEON (future). P denotes CTFS-ForestGEO protocols described in Appendix S2.     Anderson-Teixeira et al. (2014), Global Change Biology   44 Table S7. Record of arthropod sampling at CTFS-ForestGEO sites. Entries below are no. of individuals/no. of species / no. of DNA sequences / taxonomic knowledge (coded as follows: 1 = work needed; 2 = reasonable; 3 = checklist complete or nearly so) as of November 2013. Protocol Target taxa (order) Guild BCI Khao Chong Wanang Yasuni Hong Kong Light traps Passalidae (Coleoptera) Wood eaters 510 / 13 / 51 / 3 - - - - Platypodinae (Coleoptera) Wood eaters 662 / 19 / 56 / 2 959 / 24 / 0 / 1 - - - Dynastinae (Coleoptera) Scavengers 1,556 / 24 / 52 / 2 - - - - Isoptera Scavengers 14,289 / 30 / 62** / 2 4,896 / 4 / 0 / 1 - - - Flatidae (Hemiptera) Sap-suckers 1,855 / 28 / 97 / 3 311 / 20 / 0 / 1 - - - Reduviidae (Hemiptera) Predators 971 / 51 / 65 / 1 100 / 6 / 0 / 1 - - - Saturniidae (Lepidoptera) Chewers (leaves) 34 / 714 / 168 / 3 - - - - Geometridae (Lepidoptera) Chewers (leaves) 6,673 / 229 / 961 / 2 6,220 / 396 / 409 / 2 - - Planned starting 2014 Arctiinae (Lepidoptera) Chewers (leaves) 8,875 / 160 / 812 / 2 4,394 / 174 / 34 / 1 - - Planned starting 2014 Pyraloidea (Lepidoptera) Chewers (leaves) 11,253 / 339 / 832 / 1 7,412 / 445 / 103 / 1 - - Planned starting 2014 Ecitoninae - alates (Hymenoptera) Predators 4,416 / 16 / 67 / 1 - - - - Apidae + Halictidae - nocturnal (Hymenoptera) Pollinators 2,904 / 23 / x / 2 140 / 5 / 0 / 2 - - - Winkler Formicidae - litter (Hymenoptera) Varia 11,945 / 133 / 957 / 3 10,929 / 134 / 0 / 1 Planned starting 2014 2,500/100/0/1 - McPhail traps Tephritidae (Diptera) Chewers (fruits) - 17,945 / 83 / 93 / 2 Planned starting 2014 - - Butterfly transects Papilonoidea+Hesper Chewers 8,772 / 350 / 3,567 / 280 / 3,371 / 134 / - 73 / 28 / 0 / 1 Anderson-Teixeira et al. (2014), Global Change Biology   45 iidae (Lepidoptera) (leaves) 1,282 / 3 404 / 2 651 / 2 Termite transects Isoptera Scavengers 2,598 / 13 / 62** / 2 2,268 / 35 / 0 / 2 Planned starting 2014 Planned starting 2015? Planned starting 2015? Bee baits Apidae Euglossini (Hymenoptera) Pollinators 19,020 / 26 / 96 / 3 - - - - Seed predation Various in Lepidoptera, Coleoptera and Hymenoptera Seed predators 24,000 / ? / 1,148 / 1 1,373 / 90 / 0 / 1 4,626 / 23 / 0 / 1 - - ** Total number of sequences for all Isoptera Anderson-Teixeira et al. (2014), Global Change Biology   46 Table S8. Site-specific acknowledgments for selected CTFS-ForestGEO sites.  Site Acknowledgements Amacayacu We thank the Staff of the National Natural Park of Amacayacu and the National System of Protected Areas of Colombia. Badagongshan Work at Badagongshan was supported by the National Natural Science Foundation of China (31270562) and the Chinese Forest Biodiversity Monitoring Network (29200931131101919). Baotianman The 25 ha Baotianman forest dynamics plot was funded by National Science and Technology Support Plan (2008BAC39B02), State Key Laboratory of Vegetation and Environmental Change (LVEC2011zyts01), the Natural Science Foundation of China (31070554, 31270642, 31370586), and Biodiversity Committee, Chinese Academy of Sciences. Thanks to hundreds of college students, graduate students, local workers, and researchers for their hard works. Thanks to State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Chinese Forest Biodiversity Monitoring Network, Henan Agricultural University, Nanyang Normal University, China University of Mining & Technology (Beijing), Pingdingshan University, and Baotianman National Nature Reserve for their cooperation and kind support. Barro Colorado Island The BCI forest dynamics research project was founded by S.P. Hubbell and R.B. Foster and is now managed by R. Condit, S. Lao, and R. Perez under the Center for Tropical Forest Science and the Smithsonian Tropical Research Institute in Panama. Numerous organizations have provided funding, principally the U.S. National Science Foundation, and hundreds of field workers have contributed. Danum The Danum plot is a core project of the Southeast Asia Rain Forest Research Programme (SEARRP). We thank SEARRP partners especially Yayasan Sabah for their support, and HSBC Malaysia and the University of Zurich for funding. We are grateful to the research assistants who are conducting the census, in particular the team leader Alex Karolus, and to Mike Bernados and Bill McDonald for species identifications. We thank Stuart Davies and Shameema Esufali for advice and training. Harvard Forest Funding for the Harvard ForestGEO Forest Dynamics plot was provided by the Center for Tropical Forest Science and Smithsonian Institute’s Forest Global Earth Observatory (CTFS-ForestGEO), the National Science Foundation’s LTER program (DEB 06-20443 and DEB 12-37491) and Harvard University. Thanks to many field technicians who helped census the plot. Jason Aylward was instrumental as a field supervisor and with data screening and database management. Thanks to John Wisnewski and the woods crew at HF for providing materials, supplies, and invaluable field assistance with plot logistics. Joel Botti and Frank Schiappa provided survey expertise to establish the 35-ha plot. Special thanks to Stuart Davies and Rick Condit for field training, database assistance, and plot advice. Sean McMahon and Suzanne Lao were extremely helpful with field planning, data questions, and many plot logistics. Thanks to Jeannette Bowlen for administrative assistance and Anderson-Teixeira et al. (2014), Global Change Biology   47 Site Acknowledgements to Emery Boose and Paul Siqueira for help with plot coordinates. Thanks also to David Foster for his support and assistance with plot design, location, and integration with other long-term studies at HF. Hong Kong We thank the Hongkong Bank Foundation. Huai Kha Khaeng and Khao Chong We thank many people helped to create the permanent research plots in Huai Kha Khaeng and Khao Chong. The administrative staff of Huai Kha Khaeng Wildlife Sanctuary and Khao Chong Botanical Garden helped with logistic problems of the plots in many occasions. Over the past two decades the Huai Kha Khaeng 50-hectare plot and the Khao Chong 24-hectare plot projects have been financially and administratively supported by many institutions and agencies. Direct financial support for the plot has been provided by the people of Thailand through the Royal Forest Department (1991-2003) and the National Parks Wildlife and Plant Conservation Department since 2003, the Arnold Arboretum of Harvard University, the Smithsonian Tropical Research Institute, and the National Institute for Environmental Studies, Japan, as well as grants from the US National Science Foundation (grant #DEB-0075334 to P.S. Ashton and S.J. Davies), US-AID (with the administrative assistance of WWF-USA), and the Rockefeller Foundation. Administrative support has been provided by the Arnold Arboretum, the Harvard Institute for International Development, the Royal Forest Department, and the National Parks Wildlife and Plant Conservation Department. In addition, general support for the CTFS program has come from the Arnold Arboretum of Harvard University, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, Conservation, Food and Health, Inc., and the Merck Foundation. All of these organizations are gratefully acknowledged for their support. Jianfengling Jianfengling Forest Plot was supported by National Nonprofit Institute Research Grant of CAF (CAFYBB2011004, RITFYWZX200902, RITFYWZX201204), National Natural Science Foundation of China (31290223, 41201192), State Forestry Administration of China (201104057). It was also supported by the Jianfengling National Key Field Research Station for Tropical Forest Ecosystem. Kuala Belalong Funding for the 25 ha HOB Forest Dynamics Research Plot was provided by HSBC-Brunei Darussalam, Smithsonian's Centre for Tropical Forest Science and Universiti Brunei Darussalam. We also acknowlege the support from Heart of Borneo (HOB)-Brunei Darussalam, Brunei Forestry Department and the Kuala Belalong Field Studies Centre. Khao Chong See above: Huai Kha Khaeng and Khao Chong. Laupahoehoe and Palamanui The Hawai‘i Permanent Plot Network thanks the USFS Institute of Pacific Islands Forestry (IPIF) and the Hawai‘i Division of Forestry and Wildlife/Department of Land and Natural Resources for permission to conduct research within the Hawai‘i Experimental Tropical Forest; the Palāmanui Group, especially Roger Harris, for access to the lowland dry forest site. We thank the Smithsonian Tropical Research Institute Center for Tropical Forest Science, the University of California, Los Angeles, the Pacific Southwest Research Station of the USFS, Anderson-Teixeira et al. (2014), Global Change Biology   48 Site Acknowledgements the University of Hawai‘i, and NSF EPSCoR Grants No. 0554657 and No. 0903833 for support. Lilly Dickey Funding for the Lilly Dickey Woods Forest Dynamics Plot was provided by the Indiana Academy of Sciences and the Smithsonian Institution's Center for Tropical Forest Science. Luquillo This research was supported by grants BSR-8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910 and DEB 0963447 from NSF to the Institute for Tropical Ecosystem Studies, University of Puerto Rico, and to the International Institute of Tropical Forestry USDA Forest Service, as part of the Luquillo Long-Term Ecological Research Program. Funds were contributed for the 2000 census by the Andrew Mellon foundation and by CTFS for the 2011 census. The U.S. Forest Service (Dept. of Agriculture) and the University of Puerto Rico gave additional support. We also thank the many volunteers and interns who have contributed to the Luquillo forest censuses. Nonggang We appreciate the researchers from the Guangxi Institute of Botany, Chinese Academy of Sciences, for their contributions to the establishment and census of the 15-ha Nonggang karst forest plot. They are Wusheng Xiang, Bin Wang, Tao Ding, Shuhua Lu, Fuzhao Huang, Wenheng Han, Lanjun He, Qingbai Lu, Dongxing Li, respectively.We also thank many volunteersin the field work from the College of Life Science, Guangxi Normal University. We acknowledge the support from the Administration Bureau of the Nonggang National Nature Reserve. Mudumalai We thank the Ministry of Environment and Forests (Government of India), for funding research Tamilnadu Forest Department, for permissions to conduct long-term research Palamanui See above: Laupahoehoe and Palamanui Rabi We thank the Center for Conservation Education and Sustainable (CCES), Center for Tropical Forest Science (CTFS) and Shell Gabon. Santa Cruz The UCSC Forest Ecology Research Plot was made possible by National Science Foundation grants to Gregory S. Gilbert (DEB-0515520 and DEB-084259), by the Pepper-Giberson Chair Fund, the University of California Santa Cruz, the UCSC Natural Reserve, and the hard work of dozens of UCSC students. SCBI Funding for the establishment of the SCBI ForestGEO Large Forest Dynamics Plot was provided by the Smithsonian Global Earth Observatory initiative, the Smithsonian Institution, National Zoological Park and the HSBC Climate Partnership. We especially thank the numerous technicians, interns and volunteers of the Conservation Ecology Center at the SCBI who were essential in assisting with plot establishment and data collection. Support for the original exclosure fence installation was provided by the Friends of the National Zoo and Earthwatch Foundation. Tyson We thank the International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis, the Center for Tropical Forest Science and Forest Global Earth Observatories (CTFS- Anderson-Teixeira et al. (2014), Global Change Biology   49 Site Acknowledgements ForestGEO) Grants Program, and the Tyson Research Center for financial support. Wanang We wish to acknowledge financial support from the National Science Foundation (DEB-0816749), Swire & Sons Ltd., Darwin Initiative for the Survival of Species (19-008), the Grant Agency of the Czech Republic (14-36098G), and the Christensen Foundation. Wind River We acknowledge Ken Bible, Todd Wilson, the Gifford Pinchot National Forest, the USDA Forest Service Pacific Northwest Research Station, Utah State University, University of Washington, University of Montana, Washington State University, and the volunteers listed at http://www.wfdp.org. Wytham Woods Plot establishment and subsequent data collection was funded by HSBC/Smithsonian Institution. Thanks to Research and Field Assistant: Gordon Campbell; for practical site support to: Michele Taylor, Nigel Fisher, Terhi Riutta; and to fieldworkers (in addition to N. Butt & G. Campbell): Sam Armenta Butt, Luke Sherlock, Youshey Zakiuddin, Dan Gurdak, Arthur Downing, Dominic Jones, Jay Varney, Leo Armenta Butt, Jeremy Palmer, Daniel Goldhill. Yasuni We gratefully acknowledge the professional help of numerous biologists and field collaborators of the Yasuni forest dynamics plot, particularly Álvaro Pérez, Pablo Alvia and Milton Zambrano, who provided invaluable expertise on plant taxonomy. Consuelo Hernández organized the data and improved its quality. P. Universidad Católica del Ecuador (PUCE) and STRI co-financed the first two censuses of the plot. The third census was financed with funds of the Government of Ecuador and PUCE. Seed traps and seedling plots are monitored for over 10 years thanks to STRI and two awards from the NSF program LTREB (DBI 0614525 and 1122634). STRI also sponsored the Carbon Dynamics Initiative. This study was endorsed by the Ministerio de Ambiente del Ecuador permits MAE: No 004-2012-IC-FLO-MAE-DPO, 09-FLO-MA-DPO-PNY and 06-2011-FAU-DPAP. Yosemite We acknowledge Joe Meyer, Yosemite National Park, Utah State University, University of Washington, University of Montana, Washington State University, and the students and volunteers listed at http://www.yfdp.org. Zofin The research was supported by Czech Ministry of Education, Youth and Sports, project No. LH12038   Anderson-Teixeira et al. 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