ArticlePDF Available

CTFS-ForestGEO: A worldwide network monitoring forests in an era of global change

Global Change Biology

February 2015
21(2):n/a-n/a

DOI:10.1111/gcb.12712

Authors:

Kristina J. Anderson-Teixeira

Smithsonian Institution

Amy C Bennett

University of Leeds

Erika Gonzalez-Akre

Smithsonian Institution

Show all 108 authorsHide

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 characterizing 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 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 m−2 yr−1 and 3.1 g S m−2 yr−1), 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.

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 factors 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.

…

Current and projected future (2050) mean annual temperature and precipitation of CTFS-ForestGEO sites superimposed 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 climates 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, temperate rain forest; TeSF, temperate seasonal forest; W/S, woodland/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 climate data and analysis are given in Appendix S1; data are listed in Table S4.

…

continued)

…

Map of CTFS-ForestGEO sites illustrating the severity of four global change pressures: changes in mean annual temperate and precipitation relative to the 1951–1980 climatic average (MAT and MAP, respectively; Table S3), total N deposition (Table S5), and an index of habitat fragmentation (see Appendix S1; Table S5). Numbers correspond to ID#'s in Table 2. Analyses are detailed in Appendix S1.

…

continued)

…

Figures - uploaded by Helene C Muller-Landau

Content may be subject to copyright.

Content uploaded by Helene C Muller-Landau

Content may be subject to copyright.

REVIEW

CTFS-ForestGEO: a worldwide network monitoring

forests in an era of global change

KRISTINA J. ANDERSON-TEIXEIRA

1,2

, STUART J. DAVIES

1,3

,AMYC.BENNETT

,ERIKA

B. GONZALEZ-AKRE

, HELENE C. MULLER-LANDAU

,S.JOSEPHWRIGHT

, KAMARIAH

ABU SALIM

,ANG



ELICA M. ALMEYDA ZAMBRANO

2,5,6

, ALFONSO ALONSO

, JENNIFER

L. BALTZER

,YVESBASSET

, NORMAN A. BOURG

, EBEN N. BROADBENT

2,5,6

, WARREN

Y. BROCKELMAN

, SARAYUDH BUNYAVEJCHEWIN

, DAVID F. R. P. BURSLEM

NATHALIE BUTT

12,13

,MINCAO

, DAIRON CARDENAS

,GEORGEB.CHUYONG

KEITH CLAY

,SUSANCORDELL

, HANDANAKERE S. DATTARAJA

, XIAOBAO

DENG

, MATTEO DETTO

, XIAOJUN DU

, ALVARO DUQUE

, DAVID L. ERIKSON

CORNEILLE E.N. EWANGO

, GUNTER A. FISCHER

, CHRISTINE FLETCHER

,ROBINB.

FOSTER

, CHRISTIAN P. GIARDINA

, GREGORY S. GILBERT

26,1

,NIMAL

GUNATILLEKE

, SAVITRI GUNATILLEKE

, ZHANQING HAO

,WILLIAMW.

HARGROVE

,TERESEB.HART

,BILLYC.H.HAU

, FANGLIANG HE

, FORREST M.

HOFFMAN

,ROBERTW.HOWE

, STEPHEN P. HUBBELL

1,35

, FAITH M. INMAN-

NARAHARI

, PATRICK A. JANSEN

1,37

, MINGXI JIANG

, DANIEL J. JOHNSON

MAMORU KANZAKI

, ABDUL RAHMAN KASSIM

, DAVID KENFACK

1,3

,STALINE

KIBET

40,41

,MARGARETF.KINNAIRD

42,43

,LISAKORTE

, KAMIL KRAL

, JITENDRA

KUMAR

,ANDREWJ.LARSON

,YIDELI

,XIANKUNLI

, SHIRONG LIU

,SHAWN

K.Y. LUM

, JAMES A. LUTZ

,KEPINGMA

, DAMIAN M. MADDALENA

,JEAN-REMY

MAKANA

,YADVINDERMALHI

, TOBY MARTHEWS

, RAFIZAH MAT SERUDIN

SEAN M. MCMAHON

1,53

,WILLIAMJ.MCSHEA

,HERV



ER. MEMIAGHE

,XIANGCHENG

, TAKASHI MIZUNO

,MICHAELMORECROFT

, JONATHAN A. MYERS

,VOJTECH

NOVOTNY

57,58

, ALEXANDRE A. DE OLIVEIRA

,PERRYS.ONG

, DAVID A. ORWIG

REBECCA OSTERTAG

, JAN DEN OUDEN

,GEOFFREYG.PARKER

,RICHARDP.

PHILLIPS

, LAWREN SACK

,MOSESN.SAINGE

,WEIGUOSANG

, KRIANGSAK

SRI-NGERNYUANG

,RAMANSUKUMAR

, I-FANG SUN

, WITCHAPHART

SUNGPALEE

, HEBBALALU SATHYANARAYANA SURESH

,SYLVESTERTAN

,SEAN

C. THOMAS

,DUNCANW.THOMAS

, JILL THOMPSON

70,71

,BENJAMINL.TURNER

MARIA URIARTE

,RENATOVALENCIA

, MARTA I. VALLEJO

, ALBERTO

VICENTINI

,TOM





SVR



SK A

,XIHUAWANG

, XUGAO WANG

, GEORGE WEIBLEN

AMY WOLF

,HANXU

, SANDRA YAP

andJESS ZIMMERMAN

Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama, Republic of

Panama,

Conservation Ecology Center, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA,

USA,

Department of Botany, National Museum of Natural History, Washington, DC, USA,

Environmental and Life Sciences,

Faculty of Science, Universiti of Brunei Darussalam, Tungku Link Road, Bandar Seri Begawan, BE 1410, Brunei Darussalam,

Stanford Woods Institute for the Environment, Stanford University, Stanford, CA, USA,

Department of Geography, University

of Alabama, Tuscaloosa, AL, USA,

Center for Conservation Education and Sustainability, Smithsonian Conservation Biology

Institute, National Zoological Park, Washington, DC, USA,

Department of Biology, Wilfrid Laurier University, Waterloo, ON,

N2L 3C5, Canada,

Department of Biology, Mahidol University, Bangkok, Thailand,

Research Ofﬁce, Department of National

Parks, Wildlife and Plant Conservation, Bangkok, Thailand,

School of Biological Sciences, University of Aberdeen, Aberdeen, UK,

School of Biological Sciences, University of Queensland, St. Lucia 4072, Australia,

Environmental Change Institute, School of

Geography and the Environment, University of Oxford, Oxford, UK,

Key Laboratory of Tropical Forest Ecology, Xishuangbanna

Tropical Botanical Garden, Chinese Academy of Sciences, 88 Xuefu Road, Kunming 650223, China,

Instituto Amaz

onico de

Investigaciones Cient

ıﬁcas Sinchi, Bogot

a, Colombia,

Department of Botany and Plant Physiology, University of Buea, Buea,

Cameroon,

Department of Biology, Indiana University, Bloomington, IN, USA,

Institute of Paciﬁc Islands Forestry, USDA

Forest Service, Hilo, HI, USA,

Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India,

Institute of Botany,

Chinese Academy of Sciences, Beijing 100093, China,

Departamento de Ciencias Forestales, Universidad Nacional de Colombia,

Medellin, Colombia,

Centre de Formation et de Recherche en Conservation Foresti

ere (CEFRECOF) Epulu, Ituri Forest, Reserve

de Faune a Okapis, Epulu, Democratic Republic of Congo,

Kadoorie Farm and Botanic Garden, Tai Po, Hong Kong,

Forest

Global Change Biology (2014), doi: 10.1111/gcb.12712

Global Change Biology

Research Institute Malaysia, Selangor, Malaysia,

Botany Department, The Field Museum, Chicago, IL, USA,

Environmental

Studies Department, University of California, Santa Cruz, Santa Cruz, CA, USA,

Faculty of Science, Department of Botany,

University of Peradeniya, Peradeniya, Sri Lanka,

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology,

Chinese Academy of Sciences, Shenyang 110164, China,

Eastern Forest Environmental Threat Assessment Center, USDA-Forest

Service Station Headquarters, Asheville, NC, USA,

Tshuapa-Lomami-Lualaba Project, Lukuru Wildlife Research Foundation,

Kinshasa BP 2012, Democratic Republic of the Congo,

Kadoorie Institute and School of Biological Sciences, The University of

Hong Kong, Pokfulam, Hong Kong,

Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada,

Computational Earth Sciences Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA,

Department of Natural and

Applied Sciences, University of Wisconsin-Green Bay, Green Bay, WI 54311, USA,

Department of Ecology and Evolutionary

Biology, University of California, Los Angeles, Los Angeles, CA, USA,

College of Tropical Agriculture and Human Resources,

University of Hawaii at Manoa, Honolulu, HI, USA,

Resource Ecology Group, Wageningen University, Wageningen, The

Netherlands,

Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China,

Graduate School of Agriculture,

Kyoto University, Kyoto, Japan,

National Museums of Kenya, P.O. Box 40658 -00100, Nairobi, Kenya,

Land Resource

Management & Agricultural Technology Department, University of Nairobi, P.O. Box 29053-00625 Nairobi, Kenya,

Mpala

Research Centre, PO Box 555, Nanyuki 10400, Kenya,

Global Conservation Programs, Wildlife Conservation Society, 2300

Southern Blvd., Bronx, NY 10460, USA,

Department of Forest Ecology, Silva Tarouca Research Institute, Brno, Czech Republic,

Department of Forest Management, College of Forestry and Conservation, The University of Montana, Missoula, MT, USA,

Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, China,

Guangxi Institute of Botany, Chinese

Academy of Sciences, Guilin, Guangxi, China,

Research Institute of Forest Ecology, Environment and Protection, Chinese

Academy of Forestry, Beijing, China,

Natural Sciences & Science Education Academic Group, National Institute of Education,

Nanyang Technological University, Singapore,

Wildland Resources Department, Utah State University, Logan, UT, USA,

Wildlife Conservation Society, Brazzaville, Democratic Republic of the Congo,

Environmental and Life Sciences, Faculty of

Science, Universiti of Brunei Darussalam, Tungku Link Road, BE 1410, Bandar Seri Begawan, Brunei Darussalam,

Forest

Ecology Group, Smithsonian Environmental Research Center, Edgewater, MD, USA,

Institut de Recherche en Ecologie

Tropicale/Centre National de la Recherche Scientiﬁque et Technologique, Libreville, GABON,

Natural England, Shefﬁeld,

England, UK,

Department of Biology, Washington University in St. Louis, St. Louis, MO, USA,

New Guinea Binatang

Research Centre, PO Box 604, Madang, Papua New Guinea,

Biology Centre, Academy of Sciences of the Czech Republic and

Faculty of Science, University of South Bohemia, Branisovska 31, Ceske Budejovice 370 05, Czech Republic,

Departamento

Ecologia, Universidade de Sa

˜o Paulo,Instituto de Biocie

ˆncias, Cidade Universita

´ria, Sa

˜o Paulo, SP, Brazil,

Institute of Biology,

University of the Philippines Diliman, Quezon City, Philippines,

Harvard Forest, Harvard University, Petersham, MA, USA,

Department of Biology, University of Hawaii, Hilo, HI, USA,

Forest Ecology and Forest Management Group, Wageningen

University, Wageningen, The Netherlands,

Tropical Plant Exploration Group (TroPEG), P.O. Box 18 Mundemba, Southwest

Region, Cameroon,

Faculty of Architecture and Environmental Design, Maejo University, Chiang Mai Province, Thailand,

Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualian, Taiwan,

Sarawak

Forest Department, Kuching, Sarawak, Malaysia,

Faculty of Forestry, University of Toronto, 33 Willcocks St., Toronto, ON M5S

3B3, Canada,

School of Biological Sciences, Washington State University, Vancouver, WA, USA,

Centre for Ecology &

Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK,

Institute for Tropical Ecosystem Studies, Department of

Environmental Science, University of Puerto Rico, Rio Piedras Campus, PO Box 70357, San Juan 00936-8377, Puerto Rico,

Department of Ecology, Evolution & Environmental Biology, Columbia University, New York, NY, USA,

Department of

Biological Sciences, Pontiﬁcal Catholic University of Ecuador, Apartado Postal 17-01-2184, Quito, Ecuador,

Calle 37, Instituto

Alexander von Humboldt, Number 8-40 Mezzanine, Bogota

´, Colombia,

Instituto Nacional de Pesquisas da Amazo

ˆnia, Manaus,

Brazil,

School of ecological and environmental sciences, East China Normal University, Shanghai, China,

Department of Plant

Biology, University of Minnesota, St. Paul, MN, USA,

Departments 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

2K. 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

2

1

and 3.1 g S m

2

1

), 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

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; R

ejou-M

echain 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; Sonk

eet 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)

4K. J. ANDERSON-TEIXEIRA et al.

Table 2 Characteristics of the CTFS-ForestGEO sites. Sites are ordered alphabetically by biogeographic zone (sensu Olson et al., 2001), then by country, and ﬁnally by site name.

More site data are given in the appendix (Tables S1–S7) and online (http://www.ctfs.si.edu/Data)

No. Site Country

K€

oppen Climate

zone†

MAT

(°C)‡

MAP

(mm yr

1

)‡

Dominant

Soil order(s)§

Dominant

vegetation

type(s)**

Natural Dist.

Regime†† N species

Plot Size

(ha)

Year

established‡‡

Afrotropics

1 Korup Cameroon Am 26.6 5272 Ult, Ox BE W 494 55 1996

2 Ituri (Edoro

and Lenda)

Democratic

Republic

of Congo

Af 24.3 1682 Ox BE W; A 445 40 1994

3 Rabi Gabon Aw 26.0 2282 Ox BE W 342 25 2010

4 Mpala Kenya Cfb 17.9 657 Alf; Ve BdD Fi, A 22 120 2011

Australasia

5 Wanang Papua New

Guinea

Af 26.0 3500 Alf; In BE L; E 500* 50 2009

Indo-Malaya

6 Kuala Belalong Brunei

Darussalam

Af 26.5 5203 Ult BE L 850–1050* 25 2009

7 Dinghushan China Cfa 20.9 1985 BE 210 20 2005

8 Heishiding China Cfa 22.0 1744 BE 245 50 2013

9 Hong Kong China Cwa 23.3 2399 Ox BE H 67–147* 21 2012

10 Jianfengling China Aw 19.8 1657 Ult BE H 291 60 2012

11 Nonggang China Cwa 22.0 1376 Ox BE; BdD D 223 15 2011

12 Xishuangbanna China Cwa 21.8 1493 Ox BE W; D 468 20 2007

13 Mudumalai India Aw 22.7 1255 Alf BdD Fi; A; D 72 50 1987

14 Danum Valley Malaysia Af 26.7 2822 UIt BE D; A * 50 2010

15 Lambir Malaysia Af 26.6 2664 Ult BE L; D 1182 52 1991

16 Pasoh Malaysia Af 27.9 1788 Ult BE W 814 50 1986

17 Palanan Philippines Af 26.1 3380 Ult; In BE H 335 16 1994

18 Bukit Timah Singapore Af 26.9 2473 Ult BE A 347 4 1993

19 Sinharaja Sri Lanka Af 22.5 5016 Ult BE W 204 25 1993

20 Fushan Taiwan Cfa 18.2 4271 Ult; In BE H 110 25 2004

21 Kenting Taiwan Am 25.4 1964 BE H 95 10 1996

22 Lienhuachih Taiwan Cwb 20.8 2211 Ult BE H; L 144 25 2008

23 Nanjenshan Taiwan Aw 23.5 3582 Ult BE; BdD W; H 125 6 1989

24 Zenlun Taiwan Am 22.7 2620 NE H 12 2005

25 Doi Inthanon Thailand Aw 20.9 1908 UIt BE –162 15 1997

26 Huai Kha

Khaeng (HKK)

Thailand Aw 23.5 1476 Alf BE; BdD Fi; D 251 50 1992

27 Khao Chong Thailand Am 27.1 2611 Ult; In BE W; L 593 24 2000

28 Mo Singto Thailand Aw 23.5 2100 BE; BdD W 262 30.5 2000

CTFS-FORESTGEO NETWORK 5

Table 2 (continued)

No. Site Country

K€

oppen Climate

zone†

MAT

(°C)‡

MAP

(mm yr

1

)‡

Dominant

Soil order(s)§

Dominant

vegetation

type(s)**

Natural Dist.

Regime†† N species

Plot Size

(ha)

Year

established‡‡

Nearctic

29 Haliburton Canada Dfb 4.2 962 BcD –30 13.5 2007

30 Scotty Creek Canada Dfc 3.2 369 Ge NE PT; Fi 12–15* 21 2013

31 Harvard Forest USA Dfb 9.0 1050 In BdD H 60 35 2010

32 Lilly Dickey

Woods

USA Cfa 11.6 1203 In; Ult; Alf BcD W; D; Ic 35 25 2009

33 Santa Cruz USA Csb 14.8 778 Mo BE; NE Fi,W 33 16 2007

34 Smithsonian

Conservation

Biology Institute

(SCBI)

USA Cfa 12.9 1001 Alf BcD W, Ic 64 25.6 2008

35 Smithsonian

Environmental

Research Center

(SERC)

USA Cfa 13.2 1068 Ult; In; En BcD H; W 79 16 2007

36 Tyson Research

Center

USA Cfa 13.5 957 Alf BcD D; Fi; Ic; W 42 20 2013

37 Wabikon USA Dfb 4.2 805 Alf BcD W 42 25.6 2008

38 Wind River USA Csb 9.2 2495 An NE Fi; W; In 26 25.6 2010

39 Yosemite

National Park

USA Csb 10.2 1065 Alf NE Fi; W; D; In 23 25.6 2009

Neotropics

40 Ilha do Cardoso Brazil Cfa 22.4 2100 S BE 106 10.2 2004

41 Manaus Brazil Af 26.7 2600 Ox BE W 1440* 25 2004

42 Amacayacu Colombia Af 25.8 3215 Ult BE Fl 1133 25 2006

43 La Planada Colombia Cfb 19.0 4087 An BE W 240 25 1997

44 Yasuni Ecuador Af 28.3 3081 Ult BE –1114 50 1995

45 Barro Colorado

Island (BCI)

Panama Am 27.1 2551 Ox BdD; BE D; W 299 50 1981

46 Cocoli Panama Am 26.6 1950 Ox; In BdD; BE D; W 176 4 1994

47 San Lorenzo/

Sherman

Panama Am 26.2 3030 BE D; W 238 6 1996

48 Luquillo Puerto Rico, USA Am 22.8 3548 Ox; Ult BE H; L 138 16 1990

Oceania

49 Laupahoehoe USA Cfb 16.0 3440 An BE W 21 4 2008

50 Palamanui USA Cfb 20.0 835 Hi BE W 15 4 2008

Palearctic

6K. J. ANDERSON-TEIXEIRA et al.

Table 2 (continued)

No. Site Country

K€

oppen Climate

zone†

MAT

(°C)‡

MAP

(mm yr

1

)‡

Dominant

Soil order(s)§

Dominant

vegetation

type(s)**

Natural Dist.

Regime†† N species

Plot Size

(ha)

Year

established‡‡

51 Badagongshan China Cfa 15.9 1410 In BE; BdD Fl 238 25 2011

52 Baotianman China Cwa 15.1 886 BdD A; D 126 25 2009

53 Changbaishan China Dwb 2.9 700 Alf NE; BcD 52 25 2004

54 Donglingshan China Dwb 4.7 570 Alf BcD Fi 58 20 2010

55 Gutianshan China Cfa 15.3 1964 Ult BE; BdD Ic 159 24 2005

56 Tiantongshan China Cfa 16.2 1375 Ox BE H; D 153 20 2008

57 Zoﬁn Czech Republic Cfb 6.2 866 S; In; Hi BcD; NE W; In 12 25 2012

58 Speulderbos Netherlands Cfb 10.1 833 In BcD W; A 13 27 2013

59 Wytham Woods UK Cfb 10.0 717 E BcD 23 18 2008

*Measurement in progress.

†Af: Tropical with signiﬁcant precipitation year-round; Am: Tropical monsoon; Aw: Tropical wet and dry; Csb-Dry-summer subtropical/mid-latitude climate with dry sum-

mers (a.k.a.: Warm-summer Mediterranean); Cfa: Humid subtropical/mid-latitude climate with signiﬁcant precipitation year-round; Cwa: Humid subtropical/midlatitude cli-

mate with dry winters; Cfb: Oceanic with signiﬁcant precipitation year-round; Cwb: Oceanic with dry winters; Dfb: Humid Continental with signiﬁcant precipitation year-

round; Dwb: Humid continental with dry winters; Dfc: Subarctic.

‡Climate data are the best available for each site (based on judgment of site PIs; years vary). For sites where local data are not available or not reported, values (italicized) are

mean 1950–2000 climate from WorldClim at 30 arcsecond resolution (Table S4; Hijmans et al., 2005).

§Categorical following the USDA Soil Taxonomy System (Soil Survey Staff, 1999): Alf, Alﬁsols; An, Andisols; E, Entisoils; Ge, Gelisols; Hi, Histosols; In, Inceptisols; Ox, Oxisols;

Ult, Ultisols; S, Spodosols; Ve, Vertisols.

**BE, broadleaf evergreen; BdD, broadleaf drought deciduous; BcD, broadleaf cold deciduous; NE, needleleaf evergreen.

††A, animal activity (destructive); D, Drought; E, Erosion; Fi, Fire; Fl, ﬂood; H, hurricane/typhoon; Ic, Ice storms; Ininsect outbreaks; L, landslides; PT, permafrost thaw; W,

wind storms (local); ‘–’, no major natural disturbances.

‡‡When census spanned multiple years, the ﬁrst year is listed.

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 yr

1

(Scotty Creek, Canada) to

5272 mm yr

1

(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.

8K. 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

(ANPP

wood

)

Flower & seed

production

24 (33) Species-level ﬂower & seed

production are quantiﬁed using

weekly to bimonthly censuses of

60–336 0.5-m

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-m

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)

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; R

ejou-M

echain

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 CO

O, 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.

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, ANPP

wood

, 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 CO

O, 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., CO

O, NO

,NH

,SO

) and water (e.g., NO



43

); extraction of food, fuel, and ﬁber from natural

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 CO

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 precipitation<PET have been variable

across the network. In summary, CTFS-ForestGEO sites

have experienced warming and variable changes in

precipitation and aridity.

Ongoing climate change is inevitable, with its course

dependent upon future greenhouse gas emissions and

land use patterns (IPCC, 2013). The IPCC AR5 exam-

ines four representative concentration pathways

(RCP’s), the most optimistic of which has greenhouse

gas emissions going to zero before 2100 (RCP 2.6) and

the most pessimistic of which denotes continuously

increasing emissions leading to a radiative forcing of

8.5 W m

2

by 2100 (RCP 8.5; IPCC, 2013). Across this

range of future scenarios, the HADGEM2-ES model

predicts MAT increases averaging 2.0 °C under RCP

2.6 (range: 1.2–3.6 °C) to 3.0 °C under RCP 8.5 (range:

1.9–5.7 °C) across the CTFS-ForestGEO sites (Fig. 2;

Table S4). This warming will push some tropical forests

Fig. 3 Map of CTFS-ForestGEO sites illustrating the severity of four global change pressures: changes in mean annual temperate and

precipitation relative to the 1951–1980 climatic average (MAT and MAP, respectively; Table S3), total N deposition (Table S5), and

an index of habitat fragmentation (see Appendix S1; Table S5). Numbers correspond to ID#’s in Table 2. Analyses are detailed in

Appendix S1.

12 K. J. ANDERSON-TEIXEIRA et al.

into climates with no current analog (Fig. 2). Predicted

changes in annual precipitation at these sites range

from 8.6% to +19.0% under RCP 2.6 and 13.6% to

+7.3% under RCP 8.5 (Fig. 2; Table S4). When coupled

with predicted warming and associated increases in

potential evapotranspiration, constant or decreasing

precipitation –which is predicted for approximately

half the sites (Fig. 2, Table S4) –implies that conditions

will become more arid. At most CTFS-ForestGEO sites,

soil moisture and relative humidity are predicted to

decline in the near-term (i.e., 2016–2035), even under a

modest emissions scenario (Kirtman et al., 2013; Sher-

wood & Fu, 2014).

The biogeochemistry of these sites has also been

modiﬁed by human activities. The global nitrogen (N)

cycle has been dramatically transformed by human

activities (Schlesinger, 2012; Vitousek et al., 1997a; Gal-

loway et al., 2008; Canﬁeld et al., 2010). Atmospheric

deposition of reactive N can fertilize forests that are N

limited (Magnani et al., 2007; Yu et al., 2014), and can

also impair ecosystem function through soil acidiﬁcat-

ion and N saturation (Aber et al., 1989; Schlesinger,

2012; Vitousek et al., 1997b). At CTFS-ForestGEO sites,

current N deposition has a median value of 0.9 g

2

1

and ranges from 0.05 (Scotty Creek) to

3.8 g N m

2

1

(Badagongshan, China; Fig. 3),

implying that N deposition at many sites may exceed

critical loads for soil acidiﬁcation (Bouwman et al.,

2002). In addition, sulfuric acid deposition reduces soil

fertility (e.g., Likens et al., 1996) and increases tree mor-

tality (Dietze & Moorcroft, 2011). Across the network,

annual SO

deposition has a median value of 0.5 g

2

1

(range 0.08 g S m

2

1

at Mpala, Kenya

to 3.1 g S m

2

1

at Tiantongshan, China; Table S5;

data from Dentener et al., 2006; see Appendix S1 for

details). Nitrogen and sulfur deposition is predicted to

continue to increase in the future (Dentener et al., 2006).

At the local level, CTFS-ForestGEO sites have also

been directly exposed to a range of past and ongoing

anthropogenic perturbations. Some sites and their sur-

rounding areas were partially to fully logged in the

past, and in some cases the land was used for farming

or pasture (Table S5). Historical and contemporary for-

est loss (through deforestation or natural stand-clearing

disturbance) in surrounding areas has exposed some

sites to severe habitat fragmentation, whereas others

are surrounded by vast expanses of near-pristine forest

(Figs 3 and 5; Table S5). By the year 2012, 27 sites (pri-

marily in Europe, North America, and Asia) had tree

cover within a 5 km radius reduced by more than 10%

Fig. 4 Recent climate change at CTFS-ForestGEO sites. Speciﬁcally, shown is the change from the 1951–1981 average to the 2008–2012

average plotted as a function of the historical (1950–1981) average for four variables: mean annual temperature (MAT), mean annual

precipitation (MAP), mean annual potential evapotranspiration (PET), and the difference between mean annual precipitation and

potential evapotranspiration (MAPPET). Solid and dotted lines represent the linear ﬁt and its 95% CI, respectively; dashed horizontal

lines represent zero change. Based on CGIAR-CSI climate data (www.cgiar-csi.org/data; CRU-TS v3.10.01 Historic Climate Database

and PET estimates from Zomer, 2007; Zomer et al., 2008). Analyses are detailed in Appendix S1.

CTFS-FORESTGEO NETWORK 13

relative to tree cover in the plot, and seven sites even

had reductions >40%. 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 ≥1cm

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.

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 (ANPP

wood

) 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 ANPP

wood

were observed in

tropical forests in Panama (BCI) during 1981–2005

and Malaysia (Pasoh) during 1990–2000 (Feeley et al.,

2007b) and increases in ANPP

wood

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 CO

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 ha

1

(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 ha

1

; Baker et al., 2004), and Africa

(0.63 0.36 Mg C ha

1

; 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 ha

1

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).

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% yr

1

(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 CO

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

N concen-

trations in plant tissues suggests substantial N deposi-

tion and altered N cycles (Hietz et al., 2011).

Speciﬁcally, on BCI, leaf N and d

N 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 d

N by similar

amounts but did not affect productivity (Wright et al.,

2011; Mayor et al., 2014a,b). A similar increase in d

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-

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 CO

, tempera-

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.

Study on this manuscript was funded in part by a Smithsonian

Competitive Grants Program for Science award to KJAT. The

CTFS-ForestGEO network has received major support from the

Smithsonian Institution –particularly the Smithsonian Tropical

Research Institute, the Arnold Arboretum of Harvard Univer-

sity, the National Science Foundation (multiple grants), the

Rockefeller Foundation, the John Merck Fund, the John D. and

Catherine T. MacArthur Foundation, the Andrew W. Mellon

Foundation, the Frank Levinson Family Foundation, the HSBC

Climate Partnership, the Bromley Charitable Trust, John Swire

& Sons Pty Ltd, Celerity, F.H. Levinson Fund, Small World

Institute Fund and Jennifer and Greg Johnson. Site-speciﬁc sup-

port is listed in Table S8.

References

Aber J, Nedelhoffer K, Steudler P, Melillo J (1989) Nitrogen saturation in northern for-

est ecosystems. BioScience,39, 378–386.

Allen CD, Macalady AK, Chenchouni H, et al. (2010) A global overview of drought

and heat-induced tree mortality reveals emerging climate change risks for forests.

Forest Ecology and Management,259, 660–684.

Anderson-Teixeira KJ, Snyder PK, Twine TE, Cuadra SV, Costa MH, DeLucia EH

(2012) Climate-regulation services of natural and agricultural ecoregions of the

Americas. Nature Climate Change,2, 177–181.

Anderson-Teixeira KJ, Miller AD, Mohan JE, Hudiburg TW, Duval BD, DeLucia EH

(2013) Altered dynamics of forest recovery under a changing climate. Global

Change Biology,19, 2001–2021.

Asner GP, Mascaro J, Muller-Landau HC, et al. (2011) A universal airborne

LiDAR approach for tropical forest carbon mapping. Oecologia,168,1147–

1160.

Averill C, Turner BL, Finzi AC (2014) Mycorrhiza-mediated competition between

plants and decomposers drives soil carbon storage. Nature,505, 543–545.

Baker TR, Phillips OL, Malhi Y, et al. (2004) Increasing biomass in Amazonian forest

plots. Philosophical Transactions of the Royal Society B: Biological Sciences,359, 353–

365.

Baker PJ, Bunyavejchewin S, Oliver CD, Ashton PS (2005) Disturbance history and

historical stand dynamics of a seasonal tropical forest in western Thailand. Ecologi-

cal Monographs,75, 317–343.

Baldeck CA, Harms KE, Yavitt JB, et al. (2013a) Soil resources and topography shape

local tree community structure in tropical forests. Proceedings of the Royal Society B:

Biological Sciences,280, 20122532.

Baldeck CA, Harms KE, Yavitt JB, et al. (2013b) Habitat ﬁltering across tree life stages

in tropical forest communities. Proceedings of the Royal Society B: Biological Sciences,

280, 20130548.

Baldeck CA, Kembel SW, Harms KE, et al. (2013c) A taxonomic comparison of local

habitat niches of tropical trees. Oecologia,173, 1491–1498.

Baltzer JL, Veness T, Chasmer LE, Sniderhan AE, Quinton WL (2014) Forests on

thawing permafrost: fragmentation, edge effects, and net forest loss. Global Change

Biology,20, 824–834.

Banin L, Lewis SL, Lopez-Gonzalez G, et al. (2014) Tropical forest wood production: a

cross-continental comparison. Journal of Ecology,102, 1025–1037.

Basset Y, Eastwood R, Sam L, et al. (2013) Cross-continental comparisons of butterﬂy

assemblages in tropical rainforests: implications for biological monitoring: moni-

toring rainforest butterﬂies. Insect Conservation and Diversity,6, 223–233.

Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate

beneﬁts of forests. Science,320, 1444–1449.

18 K. J. ANDERSON-TEIXEIRA et al.

Bouwman AF, Vuuren DPV, Derwent RG, Posch M (2002) A global analysis of acidiﬁ-

cation and eutrophication of terrestrial ecosystems. Water, Air, and Soil Pollution,

141, 349–382.

Brown C, Burslem DFRP, Illian JB, et al. (2013) Multispecies coexistence of trees in

tropical forests: spatial signals of topographic niche differentiation increase with

environmental heterogeneity. Proceedings of the Royal Society B: Biological Sciences,

280,1–8.

Canﬁeld DE, Glazer AN, Falkowski PG (2010) The evolution and future of earth’s

nitrogen cycle. Science,330, 192–196.

Chapin FI, Randerson J, McGuire A, Foley J, Field C (2008) Changing feedbacks in the

climate-biosphere system. Frontiers in Ecology and the Environment,6, 313–320.

Chave J, Condit R, Muller-Landau HC, et al. (2008) Assessing evidence for a perva-

sive alteration in tropical tree communities. PLoS Biology,6, e45.

Cheesman AW, Winter K (2013) Elevated night-time temperatures increase growth in

seedlings of two tropical pioneer tree species. New Phytologist,197, 1185–1192.

Cheng J, Mi X, Nadrowski K, Ren H, Zhang J, Ma K (2012) Separating the effect of

mechanisms shaping species-abundance distributions at multiple scales in a sub-

tropical forest. Oikos,121, 236–244.

Chisholm RA, Muller-Landau HC, Abdul Rahman K, et al. (2013) Scale-dependent

relationships between tree species richness and ecosystem function in forests. Jour-

nal of Ecology,101, 1214–1224.

Chisholm RA, Condit R, Rahman KA, et al. (2014) Temporal variability of forest com-

munities: empirical estimates of population change in 4000 tree species (ed He F).

Ecology Letters,17, 855–865.

Choat B, Jansen S, Brodribb TJ, et al. (2012) Global convergence in the vulnerability of

forests to drought. Nature,494, 752–755.

Chuyong GB, Kenfack D, Harms KE, Thomas DW, Condit R, Comita LS (2011) Habi-

tat speciﬁcity and diversity of tree species in an African wet tropical forest. Plant

Ecology,212, 1363–1374.

Clark DB, Clark DA, Oberbauer SF (2010) Annual wood production in a tropical rain

forest in NE Costa Rica linked to climatic variation but not to increasing CO2. Glo-

bal Change Biology,16, 747–759.

Comita LS, Muller-Landau HC, Aguilar S, Hubbell SP (2010 ) Asymmetric density depen-

dence shapes species abundances in a tropical tree communit y. Science,329,330–332.

Condit R (1995) Research in large, long-term tropical forest plots. Trends in Ecology

and Evolution,1,8–22.

Condit RS (1998) Tropical Forest Census Plots - Methods and Results From Barro Colorado

Island, Panama and a Comparison With Other Plots. Springer-Verlag, Berlin, and R G.

Landes Company, Georgetown, TX, USA.

Condit R (2000) Spatial patterns in the distribution of tropical tree species. Science,

288, 1414–1418.

Condit R, Hubbell SP, Foster RB (1992) Recruitment near conspeciﬁc adults and the

maintenance of tree and shrub diversity in a neotropical forest. The American Natu-

ralist,140, 261.

Condit R, Hubbell SP, Foster RB (1995) Mortality rates of 205 neotropical tree and shrub

species and the impactof a severe drought. Ecological Monographs,65,419–439.

Condit R, Hubbell SP, Foster RB (1996) Assessing the response of plant functional

types to climatic change in tropical forests. Journal of Vegetation Science,7, 405–416.

Condit R, Watts K, Bohlman SA, P

erez R, Foster RB, Hubbell SP (2000) Quantifying

the deciduousness of tropical forest canopies under varying climates. Journal of

Vegetation Science,11, 649–658.

Condit R, Aguilar S, Hernandez A, et al. (2004) Tropical forest dynamics across a rain-

fall gradient and the impact of an El Nino dry season. Journal of Tropical Ecology,

20,51–72.

Condit RS, Ashton PS, Balslev H, et al. (2005) Tropical tree alpha-diversity: results

from a worldwide network of large plots. Biologiske Skrifter,55, 565–582.

Condit R, Ashton P, Bunyavejchewin S, et al. (2006) The importance of demographic

niches to tree diversity. Science,313,98–101.

Cushman KC, Muller-Landau HC, Condit RS, Hubbell SP (2014) Improving estimates

of biomass change in buttressed trees using tree taper models. Methods in Ecology

and Evolution,5, 573–582.

Dalling JW, Hubbell SP (2002) Seed size, growth rate and gap microsite conditions as

determinants of recruitment success for pioneer species. Journal of Ecology,90, 557–

568.

Dalling JW, Hubbell SP, Silvera K (1998) Seed dispersal, seedling establishment and

gap partitioning among tropical pioneer trees. Journal of Ecology,86, 674–689.

Davies SJ (2001) Systematic of Macaranga sects. Pachystemon and Pruinosae

(Euphorbiaceae). Harvard Papers in Botany,6, 371–448.

Davies SJ, Bunyavejchewin S, LaFrankie JV (2001) A new giant-leaved Macaranga

(Euphorbiaceae) from the dry seasonal evergreen forests of Thailand. Thai Forest

Bulletin,29,43–50.

Davies SJ, Supardi MNN, LaFrankie JV, Ashton PS (2003) The trees of Pasoh Forest:

stand structure and ﬂoristic composition of the 50-ha forest research plot. In: Pasoh:

Ecology of a Lowland Rain Forest i n Southeast Asia (eds Okunda T, Manokaran N, Mat-

sumoto Y, Niiyama K, ThomasSC, Ashton PS), pp. 35–50. Springer, Japan.

De Oliveira AA, Vicentini A, Chave J, et al. (2014) Habitat specialization and phyloge-

netic structure of tree species in a coastal Brazilian white-sand forest. Journal of

Plant Ecology,7, 134–144.

Dentener F, Drevet J, Lamarque JF, et al. (2006) Nitrogen and sulfur deposition on

regional and global scales: a multimodel evaluation. Global Biogeochemical Cycles,

20, GB4003.

Detto M, Muller-Landau HC (2013) Fitting ecological process models to spatial pat-

terns using scalewise variances and moment equations. The American Naturalist,

181, E68–E82.

DeWalt SJ, Schnitzer SA, Alves LF et al. (2015) Biogeographical patterns of liana

abundance and diversity. In: The Ecology of Lianas, 1st edn (eds Schnitzer SA, Bon-

gers F, Burnham RJ, Putz FE), pp. 133–148. John Wiley & Sons, Ltd., New York.

Dick CW, Kress WJ (2009) Dissecting tropical plant diversity with forest plots and a

molecular toolkit. BioScience,59, 745–755.

Dietze MC, Moorcroft PR (2011) Tree mortality in the eastern and central United

States: patterns and drivers. Global Change Biology,17, 3312–3326.

Dong SX, Davies SJ, Ashton PS, et al. (2012) Variability in solar radiation and temper-

ature explains observed patterns and trends in tree growth rates across four tropi-

cal forests. Proceedings of the Royal Society B: Biological Sciences,279, 3923–3931.

Estes JA, Terborgh J, Brashares JS, et al. (2011) Trophic downgrading of planet earth.

Science,333, 301–306.

Feeley KJ, Joseph Wright S, Nur Supardi MN, Kassim AR, Davies SJ (2007a) Deceler-

ating growth in tropical forest trees. Ecology Letters,10, 461–469.

Feeley KJ, Davies SJ, Ashton PS, et al. (2007b) The role of gap phase processes in the

biomass dynamics of tropical forests. Proceedings of the Royal Society B: Biological

Sciences,274, 2857–2864.

Feeley KJ, Davies SJ, Perez R, Hubbell SP, Foster RB (2011) Directional changes in the

species composition of a tropical forest. Ecology,92, 871–882.

Filewod B, Thomas SC (2014) Impacts of a spring heat wave on canopy processes in a

northern hardwood forest. Global Change Biology,20, 360–371.

Foley JA, DeFries R, Asner GP, et al. (2005) Global consequences of land use. Science,

309, 570–574.

Galloway JN, Townsend AR, Erisman JW, et al. (2008) Transformation of the

nitrogen cycle: recent trends, questions, and potential solutions. Science,320,

889–892.

Gedalof Z, Berg AA (2010) Tree ring evidence for limited direct CO2 fertilization of

forests over the 20th century. Global Biogeochemical Cycles,24, GB3027.

Geddes GA, Murphy JG, Schurman J, Thomas SC (2014) Net ecosystem exchange of

an uneven-aged managed forest in central Ontario, and the impact of a spring heat

wave event. Agricultural and Forest Meteorology,198, 199, 105–115.

Gereau ER, Kenfack D (2000) Le genre Uvariopsis (Annonaceae) en Afrique tropi-

cale, avec la description d’une esp

ece nouvelle du Cameroun. Adansonia,22,

39–43.

Goheen JR, Palmer TM, Charles GK, et al. (2013) Piecewise disassembly of a large-

herbivore community across a rainfall gradient: the UHURU experiment. PLoS

ONE,8, e55192.

Gradowski T, Thomas SC (2006) Phosphorus limitation of sugar maple growth in cen-

tral Ontario. Forest Ecology and Management,226, 104–109.

Gradowski T, Thomas SC (2008) Responses of Acer saccharum canopy trees and sap-

lings to P, K and lime additions under high N deposition. Tree Physiology,28, 173–

185.

Groombridge B (2002) World Atlas of Biodiversity: Earth’s Living Resources in the 21st

Century. UNEP-WCMC, Berkeley, CA. 364 pp

Hansen MC, Potapov PV, Moore R, et al. (2013) High-resolution global maps of 21st-

century forest cover change. Science,342, 850–853.

Hargrove WW, Hoffman FM, Law BE (2003) New analysis reveals representativeness

of the AmeriFlux network. Eos, Transactions American Geophysical Union,84, 529–

535.

Harms KE, Wright SJ, Calder

on O, Hern

andez A, Herre EA (2000) Pervasive density-

dependent recruitment enhances seedling diversity in a tropical forest. Nature,

404, 493–495.

Harms KE, Condit RS, Hubbell SP, Foster RB (2001) Habitat associations of trees and

shrubs in a 50-ha neotropical forest plot. Journal of Ecology,89, 947–959.

Harrison RD, Tan S, Plotkin JB, et al. (2013) Consequences of defaunation for a tropi-

cal tree community. Ecology Letters,16, 687–694.

He F, Hubbell SP (2011) Species-area relationships always overestimate extinction

rates from habitat loss. Nature,473, 368–371.

CTFS-FORESTGEO NETWORK 19

He F, Legendre P (2002) Species diversity patterns derived from species-area model.

Ecology,83, 1185–1198.

Hietz P, Turner BL, Wanek W, Richter A, Nock CA, Wright SJ (2011) Long-term

change in the nitrogen cycle of tropical forests. Science,334, 664–666.

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution

interpolated climate surfaces for global land areas. International Journal of Climatol-

ogy,25, 1965–1978.

Hoffman FM, Kumar J, Mills RT, Hargrove WW (2013) Representativeness-based

sampling network design for the State of Alaska. Landscape Ecology,28, 1567–1586.

Hrcek J, Miller SE, Quicke DLJ, Smith MA (2011) Molecular detection of trophic links

in a complex insect host–parasitoid food web. Molecular Ecology Resources,11, 786–

794.

Hubbell SP (1979) Tree dispersion, abundance, and diversity in a tropical dry forest.

Science,203, 1299–1309.

Hubbell S (2001) The Uniﬁed Neutral Theory of Biodiversity and Biogeography. Princeton

University Press, Princeton.

Hubbell SP, Ahumada JA, Condit R, Foster RB (2001) Local neighborhood effects on

long-term survival of individual trees in a neotropical forest. Ecological Research,

16, 859–875.

Ickes K, Dewalt SJ, Appanah S (2001) Effects of native pigs (Sus scrofa) on woody un-

derstorey vegetation in a Malaysian lowland rain forest. Journal of Tropical Ecology,

17, 191–206.

Ingwell LL, Joseph Wright S, Becklund KK, Hubbell SP, Schnitzer SA (2010) The

impact of lianas on 10 years of tree growth and mortality on Barro Colorado

Island, Panama. Journal of Ecology,98, 879–887.

IPCC (2013) Climate change 2013: the physical science basis. In: Contribution of Work-

ing Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate

Change (eds Stocker TF, Quin D, Plattner G-K, Tignor M, Allen SK, Boschung J,

Nauels A, Xia Y, Bex V, Midgley PM), pp. 1535. Cambridge University Press, Cam-

bridge, UK and New York, NY, USA.

Isbell F, Calcagno V, Hector A, et al. (2011) High plant diversity is needed to maintain

ecosystem services. Nature,477, 199–202.

Jansen PA, Muller-Landau HC, Wright SJ (2010) Bushmeat hunting and climate: an

indirect link. Science,327, 30.

John R, Dalling JW, Harms KE, et al. (2007) Soil nutrients inﬂuence spatial distributions

of tropical tree species. Proceedings of the National Academy of Sciences,104, 864–869.

Jones FA, Erickson DL, Bernal MA, et al. (2011) The roots of diversity: below ground

species richness and rooting distributions in a tropical forest revealed by DNA

barcodes and inverse modeling (ed Shiu S-H). PLoS ONE,6, e24506.

Katabuchi M, Kurokawa H, Davies SJ, Tan S, Nakashizuka T (2012) Soil resource

availability shapes community trait structure in a species-rich dipterocarp forest:

soil resources and community structure. Journal of Ecology,100, 643–651.

Kenfack D, Ewango CEN, Thomas DW (2004) Manilkara lososiana, a new species of

Sapotaceae from Cameroon. Kew Bulletin,59, 609–612.

Kenfack D, Sainge NM, Thomas DW (2006) A new species of Cassipourea (Rhizo-

phoraceae) from western Cameroon. Novon,16,61–64.

Kirtman B, Power SB, Adedoyin JA et al. (2013) Near-term climate change: projections

and predictability. In: Climate Change 2013: The Physical Science Basis. Contribution

of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel

on Climate Change (eds Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Bos-

chung J, Nauels A, Xia Y, Bex V, Midggley PM), pp. 953–1028. Cambridge Univer-

sity Press, Cambridge, UK and New York, NY, USA.

Kraft NJB, Metz MR, Condit RS, Chave J (2010) The relationship between wood density

and mortality in a global tropical forest data set. New Phytologist,188, 1124–1136.

Kress WJ, Erickson DL, Jones FA, Swenson NG, PerezR, Sanjur O, Bermingham E (2009)

Plant DNA barcodes and a community phylogeny of a tropical forest dynamics plot

in Panama. Proceedings of the National Academy of Sciences,106, 18621–18626.

Kress WJ, Erickson DL, Swenson NG, Thompson J, Uriarte M, Zimmerman JK (2010)

Advances in the use of DNA barcodes to build a community phylogeny for tropi-

cal trees in a Puerto Rican forest dynamics plot. PLoS ONE,5, e15409.

LaFrankie JV, Davies SJ, Wang LK, Lee SK, Lum SKY (2005) Forest Trees of Bukit

Timah: Population Ecology in a Tropical Forest Fragment. Simply Green, Singapore,

pp. 178.

Lai J, Coomes DA, Du X, et al. (2013) A general combined model to describe tree-

diameter distributions within subtropical and temperate forest communities. Oi-

kos,122, 1636–1642.

Laurance WF, Oliveira AA, Laurance SG, et al. (2004) Pervasive alteration of tree

communities in undisturbed Amazonian forests. Nature,428, 171–175.

Laurance WF, Nascimento HEM, Laurance SG, et al. (2006) Rapid decay of tree-com-

munity composition in Amazonian forest fragments. Proceedings of the National

Academy of Sciences,103, 19010–19014.

Laurance SGW, Laurance WF, Nascimento HEM, Andrade A, Fearnside PM, Rebello

ERG, Condit R (2009) Long-term variation in Amazon forest dynamics: amazon

forest dynamics. Journal of Vegetation Science,20, 323–333.

Laurance WF, Carolina Useche D, Rendeiro J, et al. (2012) Averting biodiversity col-

lapse in tropical forest protected areas. Nature,489, 290–294.

Lebrija-Trejos E, Wright SJ, Hern

andez A, Reich PB (2013) Does relatedness matter?

Phylogenetic density dependent survival of seedlings in a tropical forest. Ecology,

95, 940–951.

Lee H, Davies S, LaFrankie JV, Tan S, Itoh A, Yamakura T, Ashton PS (2002) Flo-

ristic and structural diversity of 52 hectares of mixed dipterocarp forest in

Lambir Hills National Park, Sarawak, Malaysia. Journal of Tropical Forest Science,

14, 379–400.

Leidner AK, Haddad NM, Lovejoy TE (2010) Does tropical forest fragmentation

increase long-term variability of butterﬂy communities? PLoS ONE,5, e9534.

Leigh E, Condit R, Lao S (2004) The neutral theory of forest ecology. In: Tropical Forest

Diversity and Dynamism: Findings From a Large-Scale Plot Network (eds Losos EC,

Leigh EG), pp. 244–263. University of Chicago Press, Chicago.

Lewis SL, Lopez-Gonzalez G, Sonke B, et al. (2009a) Increasing carbon storage in

intact African tropical forests. Nature,457, 1003.

Lewis SL, Lloyd J, Sitch S, Mitchard ETA, Laurance WF (2009b) Changing ecology of

tropical forests: evidence and drivers. Annual Review of Ecology, Evolution, and Sys-

tematics,40, 529–549.

Lewis SL, Brando PM, van der Phillips OL, Heijden GMF, Nepstad D (2011) The 2010

Amazon drought. Science,331, 554–554.

Li B, Hao Z, Bin Y, Zhang J, Wang M (2012) Seed rain dynamics reveals strong dis-

persal limitation, different reproductive strategies and responses to climate in a

temperate forest in northeast China. Journal of Vegetation Science,23, 271–279.

Likens GE, Driscoll CT, Buso DC (1996) Long-term effects of acid rain: response and

recovery of a forest ecosystem. Science,272, 244–246.

Lin D, Lai J, Muller-Landau HC, Mi X, Ma K (2012) Topographic variation in above-

ground biomass in a subtropical evergreen Broad-Leaved forest in China (ed Hec-

tor A). PLoS ONE,7, e48244.

Liu XJ, Swenson NG, Wright SJ, et al. (2012) Covariation in plant functional traits and

soil fertility within two species-Rich forests (ed Reinhart KO). PLoS ONE,7, e34767.

Losos EC, Leigh EG (2004) Tropical Forest Diversity and Dynamism: Findings From a

Large-Scale Network. University of Chicago Press, Chicago, IL; London.

Lutz JA, Larson AJ, Freund JA, Swanson ME, Bible KJ (2013) The importance of large-

diameter trees to forest structural heterogeneity (ed Newsom LA). PLoS ONE,8,

e82784.

Lutz JA, Larson AJ, Furniss TJ, et al. (2014) Spatially non-random tree mortality and

ingrowth maintain equilibrium pattern in an old-growth Pseudotsuga-Tsuga for-

est. Ecology,95, 2047–2054.

Maddalena D, Hoffman F, Kumar J, Hargrove W (2014) Landscape Characterization and

Representativeness Analysis for Understanding Sampling Network Coverage. Climate

Change Science Institute (CCSI), Oak Ridge National Laboratory (ORNL), Oak

Rdige, TN (US); DOI:10.15149/1148699.

Magnani F, Mencuccini M, Borghetti M, et al. (2007) The human footprint in the car-

bon cycle of temperate and boreal forests. Nature,447, 849–851.

Makana J-R, Ewango CN, McMahon SM, Thomas SC, Hart TB, Condit R (2011)

Demography and biomass change in monodominant and mixed old-growth forest

of the Congo. Journal of Tropical Ecology,27, 447–461.

Manokaran N, LaFrankie JV, Kochummen K, et al. (1990) Methodology for the ﬁfty

hectare research plot at Pasoh Forest Reserve. Research Pamphlet, Forest Research

Institute of Malaysia,104,1–69.

Martin AR, Thomas SC (2011) A reassessment of carbon content in tropical trees.

PLoS ONE,6, e23533.

Mascaro J, Detto M, Asner GP, Muller-Landau HC (2011) Evaluating uncertainty in

mapping forest carbon with airborne LiDAR. Remote Sensing of Environment,115,

3770–3774.

Matson PA, McDowell WH, Townsend AR, Vitousek PM (1999) The globalization of N

deposition: ecosystem consequences in tropical environments. Biogeochemistry,46,

67–83.

Mayor JR, Wright SJ, Schuur EAG, Brooks ME, Turner BL (2014a) Stable nitrogen iso-

tope patterns of trees and soils altered by long-term nitrogen and phosphorus

addition to a lowland tropical rainforest. Biogeochemistry,119, 293–306.

Mayor JR, Wright SJ, Turner BL (2014b) Species-speciﬁc responses of foliar nutrients

to long-term nitrogen and phosphorus additions in a lowland tropical forest. Jour-

nal of Ecology,102,36–44.

McGarvey JC, Bourg NA, Thompson JR, McShea WJ, Shen X (2013) Effects of twenty

years of deer exclusion on woody vegetation at three life-history stages in a Mid-

Atlantic Temperate Deciduous forest. Northeastern Naturalist,20, 451–468.

20 K. J. ANDERSON-TEIXEIRA et al.

McMahon SM, Parker GG (2014) A general model of intra-annual tree growth using

dendrometer bands. Ecology and Evolution, in press.

McMahon SM, Parker GG, Miller DR (2010) Evidence for a recent increase in forest

growth. Proceedings of the National Academy of Sciences,107, 3611–3615.

Medvigy D, Wofsy SC, Munger JW, Hollinger DY, Moorcroft PR (2009) Mechanistic

scaling of ecosystem function and dynamics in space and time: ecosystem

Demography model version 2. Journal of Geophysical Research: Biogeosciences,114,

G01002.

Melillo JM, Butler S, Johnson J, et al. (2011) Soil warming, carbon-nitrogen interac-

tions, and forest carbon budgets. Proceedings of the National Academy of Sciences,

108, 9508–9512.

Moorcroft PR, Hurtt GC, Pacala SW (2001) A method for scaling vegetation dynamics:

the ecosystem demography model (ED). Ecological Monographs,71, 557–586.

Mori AS, Furukawa T, Sasaki T (2013) Response diversity determines the resilience of

ecosystems to environmental change. Biological Reviews,88, 349–364.

Muller-Landau HC, Condit RS, Chave J, et al. (2006a) Testing metabolic ecology the-

ory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol-

ogy Letters,9, 575–588.

Muller-Landau HC, Condit RS, Harms KE, et al. (2006b) Comparing tropical forest

tree size distributions with the predictions of metabolic ecology and equilibrium

models. Ecology Letters,9, 589–602.

Muller-Landau HC, Wright SJ, Calder

on O, Condit R, Hubbell SP (2008) Interspeciﬁc

variation in primary seed dispersal in a tropical forest. Journal of Ecology,96, 653–

667.

Muller-Landau H, Detto M, Chisholm RA, Hubbell SP, Condit RS (2014) Detecting

and projecting changes in forest biomass from plot data. In: Forests and Global

Change (eds Coomes DA, Burslem DFRP, Simonson WD), pp. 381–415. Cambridge

University Press, Cambridge, UK.

Ngo KM, Turner BL, Muller-Landau HC, Davies SJ, Larjavaara M, Nik Hassan NFB,

Lum S (2013) Carbon stocks in primary and secondary tropical forests in Singa-

pore. Forest Ecology and Management,296,81–89.

Norby RJ, DeLucia EH, Gielen B, et al. (2005) Forest response to elevated CO2 is con-

served across a broad range of productivity. Proceedings of the National Academy of

Sciences of the United States of America,102, 18052–18056.

Novotny V, Basset Y (2005) Host speciﬁcity of insect herbivores in tropical forests.

Proceedings of the Royal Society B: Biological Sciences,272, 1083–1090.

Novotny V, Basset Y, Miller SE, Weiblen GD, Bremer B, Cizek L, Drozd P (2002) Low

host speciﬁcity of herbivorous insects in a tropical forest. Nature,416, 841–844.

Novotny V, Miller SE, Baje L, et al. (2010) Guild-speciﬁc patterns of species richness

and host specialization in plant–herbivore food webs from a tropical forest. Journal

of Animal Ecology,79, 1193–1203.

Olson DM, Dinerstein E, Wikramanayake ED, et al. (2001) Terrestrial ecoregions of

the world: a new map of life on earth. BioScience,51, 933.

Pan Y, Birdsey RA, Phillips OL, Jackson RB (2013) The structure, distribution, and

biomass of the world’s forests. Annual Review of Ecology, Evolution, and Systematics,

44, 593–622.

Pau S, Wolkovich EM, Cook BI, Nytch CJ, Regetz J, Zimmerman JK, Joseph Wright S

(2013) Clouds and temperature drive dynamic changes in tropical ﬂower produc-

tion. Nature Climate Change,3, 838–842.

Peay KG, Kennedy PG, Davies SJ, Tan S, Bruns TD (2010) Potential link between plant

and fungal distributions in a dipterocarp rainforest: community and phylogenetic

structure of tropical ectomycorrhizal fungi across a plant and soil ecotone. New

Phytologist,185, 529–542.

Pei NC, Lian J-Y, Erickson DL, Swenson NG, Kress WJ, Ye W-H, Ge X-J (2011) Explor-

ing tree-habitat associations in a chinese subtropical forest plot using a molecular

phylogeny generated from DNA barcode loci. PLoS ONE,6, e21273.

Potts MD (2003) Drought in a Bornean everwet rain forest. Journal of Ecology,91, 467–

474.

R

ejou-M

echain M, Muller-Landau HC, Detto M, et al. (2014) Local spatial structure of

forest biomass and its consequences for remote sensing of carbon stocks. Biogeo-

sciences Discussions,11, 5711–5742.

Ricklefs RE (2007) The Economy of Nature. 6th edn. W. H. Freeman, New York, NY.

R€

uger N, Berger U, Hubbell SP, Vieilledent G, Condit R (2011) Growth strategies of

tropical tree species: disentangling light and size effects (ed Scalas E). PLoS ONE,

6, e25330.

R€

uger N, Wirth C, Wright SJ, Condit R (2012) Functional traits explain light and size

response of growth rates in tropical tree species. Ecology,93, 2626–2636.

Russo SE, Davies SJ, King DA, Tan S (2005) Soil-related performance variation and

distributions of tree species in a Bornean rain forest. Journal of Ecology,93, 879–889.

Russo SE, Kochsiek A, Olney J, Thompson L, Miller AE, Tan S (2013) Nitrogen uptake

strategies of edaphically specialized Bornean tree species. Plant Ecology,214, 1405–

1416.

Santiago LS, Wright SJ (2007) Leaf functional traits of tropical forest plants in relation

to growth form. Functional Ecology,21,19–27.

Schlesinger WH (2012) Biogeochemistry: An Analysis of Global Change, (3rd edn). Aca-

demic Press, London.

Schmitz OJ, Raymond PA, Estes JA, et al. (2013) Animating the carbon cycle. Ecosys-

tems,17, 344–359.

Schnitzer SA (2005) A mechanistic explanation for global patterns of liana abundance

and distribution. The American Naturalist,166, 262–276.

Schnitzer SA, Bongers F (2011) Increasing liana abundance and biomass in tropical

forests: emerging patterns and putative mechanisms. Ecology Letters,14, 397–406.

Shen G, Yu M, Hu X-S, Mi X, Ren H, Sun I-F, Ma K (2009) Species-area relationships

explained by the joint effects of dispersal limitation and habitat heterogeneity.

Ecology,90, 3033–3041.

Sherwood S, Fu Q (2014) A drier future? Science,343, 737–739.

Slot M, Rey-S

anchez C, Winter K, Kitajima K (2014) Trait-based scaling of tempera-

ture-dependent foliar respiration in a species-rich tropical forest canopy. Func-

tional Ecology, in press.

Snyder PK, Delire C, Foley JA (2004) Evaluating the inﬂuence of different vegetation

biomes on the global climate. Climate Dynamics,23, 279–302.

Soil Survey Staff (1999) Soil taxonomy: a basic system of soil classiﬁcation for making and

interpreting soil surveys, 2nd edn. Natural Resources Conservation Service, U.S

Department of Agriculture Handbook 436.

Soja AJ, Tchebakova NM, French NHF, et al. (2007) Climate-induced boreal forest

change: predictions versus current observations. Global and Planetary Change,56,

274–296.

Sonk

e B, Kenfack D, Robbrecht E (2002) A new species of the Tricalysia atherura

group (Rubiaceae) from southwestern Cameroon. Adansonia,24, 173–177.

Stephenson NL, Das AJ, Condit R, et al. (2014) Rate of tree carbon accumulation

increases continuously with tree size. Nature,507,90–93.

Sukumar R, Suresh HS, Dattaraja HS, Srinidhi S, Nath C (2005) The dynamics of a

tropical dry forest in India: climate, ﬁre, elephants and the evolution of life-history

strategies. In: Biotic Interactions in the Tropics: Their Role in the Maintenance of Species

Diversity (eds Burslem DFRP, Pinard M, Hartley S), pp. 510–529. Cambridge Uni-

versity Press, Cambridge, UK.

Suresh HS, Dattaraja HS, Sukumar R (2010) Relationship between annual rainfall and

tree mortality in a tropical dry forest: results of a 19-year study at Mudumalai,

southern India. Forest Ecology and Management,259, 762–769.

Swenson NG, Stegen JC, Davies SJ, et al. (2011) Temporal turnover in the composition

of tropical tree communities: functional determinism and phylogenetic stochastici-

ty. Ecology,93, 490–499.

Swenson NG, Stegen JC, Davies SJ, et al. (2012) Temporal turnover in the composition

of tropical tree communities: functional determinism and phylogenetic stochastici-

ty. Ecology,93, 490–499.

Tan S, Yamakura T, Tani M, et al. (2009) Review of soils on the 52 ha long term eco-

logical research plot in mixed dipterocarp forest at Lambir, Sarawak, Malaysian

Borneo. Tropics,18,61–86.

Tans P, Keeling CD (2014) Trends in atmospheric carbon dioxide, National Oceanic &

Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL)

& Scripps Institution of Oceanography, http://www.esrl.noaa.gov/gmd/ccgg/

trends/index.html#mlo_growth.

Thomas D, Burnham RJ, Chuyong GB, Kenfack D, Sainge NM (2015) Liana abun-

dance and diversity in Cameroon’s Korup National Park. In: The Ecology of Lianas,

ﬁrst edn (eds Schnitzer SA, Bongers F, Burnham RJ, Putz FE), pp. 13–22. John Wi-

ley & Sons, Ltd, New York.

Turner IM, T Corlett R (1996) The conservation value of small, isolated fragments of

lowland tropical rain forest. Trends in Ecology & Evolution,11, 330–333.

Uriarte M, Canham CD, Thompson J, Zimmerman JK (2004) A neighborhood analysis

of tree growth and survival in a hurricane-driven tropical forest. Ecological Mono-

graphs,74, 591–614.

Uriarte M, Canham CD, Thompson J, Zimmerman JK, Brokaw N (2005) Seedling

recruitment in a hurricane-driven tropical forest: light limitation, density-depen-

dence and the spatial distribution of parent trees. Journal of Ecology,93, 291–304.

U.S. DOE (2012) Research priorities for tropical ecosystems under climate change

workshop report, DOE/SC-0153.

Usinowicz J, Wright SJ, Ives AR (2012) Coexistence in tropical forests through asyn-

chronous variation in annual seed production. Ecology,93, 2073–2084.

CTFS-FORESTGEO NETWORK 21

Valencia R, Foster RB, Mu~

noz GV, et al. (2004) Tree species distributions and local

habitat variation in the Amazon: large forest plot in eastern Ecuador. Journal of

Ecology,92, 214–229.

Valencia R, Condit R, Muller-Landau HC, Hernandez C, Navarrete H (2009) Dissect-

ing biomass dynamics in a large Amazonian forest plot. Journal of Tropical Ecology,

25, 473–482.

Van Nieuwstadt MGL, Sheil D (2005) Drought, ﬁre and tree survival in a Borneo rain

forest, East Kalimantan, Indonesia. Journal of Ecology,93, 191–201.

Vitousek PM, Mooney HA, Lubchenco J, Melillo JM (1997a) Human domination of

earth’s ecosystems. Science,277, 494–499.

Vitousek PM, Aber JD, Howarth RW, et al. (1997b) Human alteration of the glo-

bal nitrogen cycle: sources and consequences. Ecological Applications,7, 737–

750.

Wang X, Wiegand T, Wolf A, Howe R, Davies SJ, Hao Z (2011) Spatial patterns of tree

species richness in two temperate forests: detecting underlying mechanisms con-

trolling species-area relationships. Journal of Ecology,99, 1382–1393.

Westbrook JW, Kitajima K, Burleigh JG, Kress WJ, Erickson DL, Wright SJ (2011)

What makes a leaf tough? patterns of correlated evolution between leaf toughness

traits and demographic rates among 197 shade-tolerant woody species in a Neo-

tropical forest. The American Naturalist,177, 800–811.

Whittaker R (1975) Communities and Ecosystems. Macmillan, New York.

Wiegand T, Gunatilleke CVS, Gunatilleke IAUN, Huth A (2007a) How individual

species structure diversity in tropical forests. Proceedings of the National Academy of

Sciences,104, 19029–19033.

Wiegand T, Gunatilleke S, Gunatilleke N (2007b) Species associations in a

heterogeneous Sri Lankan Dipterocarp Forest. The American Naturalist,170,

E77–E95.

Wright SJ (2010) The future of tropical forests. Annals of the New York Academy of Sci-

ences,1195,1–27.

Wright SJ, Calderon O (2006) Seasonal, El Nino and longer term changes in ﬂower

and seed production in a moist tropical forest. Ecology Letters,9,35–44.

Wright SJ, Carrasco C, Calder

on O, Paton S (1999) The El Ni~

no Southern Oscilla-

tion, variable fruit production, and famine in a tropical forest. Ecology,80,

1632–1647.

Wright SJ, Muller-Landau HC, Calderon O, Hernandez A (2005) Annual and spatial varia -

tion in seedfall and seedling recruitment in a neotropical forest. Ecology,86,848–860.

Wright SJ, Hernand

ez A, Condit R (2007) The bushmeat harvest alters seedling banks

by favoring lianas, large seeds, and seeds dispersed by bats, birds, and wind. Bio-

tropica,39, 363–371.

Wright SJ, Kitajima K, Kraft NJB, et al. (2010) Functional traits and the growth–mor-

tality trade-off in tropical trees. Ecology,91, 3664–3674.

Wright SJ, Yavitt JB, Wurzburger N, et al. (2011) Potassium, phosphorus, or nitrogen

limit root allocation, tree growth, or litter production in a lowland tropical forest.

Ecology,92, 1616–1625.

Yu G, Chen Z, Piao S, et al. (2014) High carbon dioxide uptake by subtropical forest

ecosystems in the East Asian monsoon region. Proceedings of the National Academy

of Sciences,111, 4910–4915.

Zalasiewicz J, Williams M, Steffen W, Crutzen P (2010) The new world of the Anthro-

pocene1. Environmental Science & Technology,44, 2228–2231.

Zalasiewicz J, Williams M, Haywood A, Ellis M (2011) The Anthropocene: a new

epoch of geological time? Philosophical Transactions of the Royal Society A: Mathemat-

ical, Physical and Engineering Sciences,369, 835–841.

Zhou L, Tian Y, Myneni RB, et al. (2014) Widespread decline of Congo rainforest

greenness in the past decade. Nature,509,86–90.

Zimmerman JK, Wright SJ, Calder

on O, Pagan MA, Paton S (2007) Flowering and fru-

iting phenologies of seasonal and aseasonal neotropical forests: the role of annual

changes in irradiance. Journal of Tropical Ecology,23, 231.

Zomer RJ (2007) Trees and water: smallholder agroforestry on irrigated lands in Northern

India. IWMI, Colombo, Sri Lanka.

Zomer RJ, Trabucco A, Bossio DA, Verchot LV (2008) Climate change mitigation: A

spatial analysis of global land suitability for clean development mechanism affor-

estation and reforestation. Agriculture, Ecosystems & Environment,126,67–80.

Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Appendix S1. Data sources and analysis methods.

Appendix S2. CTFS-ForestGEO measurement protocols.

Table S1. Geographic coordinates, elevation data, and refer-

ences to site descriptions for all CTFS-ForestGEO sites.

Table S2. 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.

22 K. J. ANDERSON-TEIXEIRA et al.

Teixeira GCB 2014 Suppinfo

Data

September 2014

Kristina J. Anderson-Teixeira · Stuart J Davies · Amy C Bennett · Erika Gonzalez-Akre · Jess Zimmerman

Download

The rise of fintech: Bibliometric analysis of global growth, emerging themes and the quality of scientific contributions

Article

Full-text available

Mar 2025

This study conducts a comprehensive bibliometric analysis of fintech adoption research from 2016 to 2024, identifying key trends, influential contributors, and emerging themes. To achieve this goal, we leverage data from Scopus and the SCImago Journal Rank to analyze the evolution of fintech scholarship, focusing on publication growth, geographic distribution, and scientific impact through the H-index and journal quartile rankings. In addition, advanced visualization tools, such as VOSviewer, are utilized to map keyword co-occurrences, collaboration networks, and citation dynamics. Our findings reveal that fintech-related research has experienced a substantial surge, particularly since 2020, primarily due to accelerated digital transformation and the COVID-19 pandemic. Moreover, Asia and Europe have emerged as leading contributors to both research output and innovation. Specifically, the dominant thematic clusters include blockchain, artificial intelligence, financial inclusion, and regulatory challenges. However, critical issues such as cybersecurity, trust, and regulatory uncertainty persist, which underscores the need for further investigation. From a practical perspective, these insights offer valuable guidance to policymakers and financial institutions in formulating adaptive regulatory frameworks and promoting responsible financial innovation. Furthermore, this study underscores the importance of interdisciplinary collaboration in fostering fintech ecosystems. Mapping both achievements and research gaps provides a strategic roadmap for future academic inquiry and policy development, ultimately reinforcing fintech’s transformative impact on the financial sector.

Natural Regeneration Pattern and Driving Factors of Mixed Forest in the Reclaimed Area of Antaibao Open-Pit Coal Mine, Pingshuo

Article

Full-text available

Apr 2025

This study was conducted at a fixed monitoring site in the southern dump of the large-scale Antaibao open-pit coal mine of China Coal Pingshuo, using long-term monitoring methods. Based on data from 2019 and 2024 in the reclaimed area of the Pingshuo open-pit coal mine, all seedlings and saplings within the Robinia pseudoacacia L. + Ulmus pumila L. + Ailanthus altissima (Mill.) Swingle mixed forests were studied to analyze changes in their abundance and the driving factors influencing their survival rates from 2019 to 2024. The main conclusions are as follows: (1) The species composition of seedlings and saplings remained unchanged but the number of seedlings increased significantly. The majority of newly recruited seedlings were U. pumila., accounting for 92.22% of the total new seedlings, whereas R. pseudoacacia had the highest mortality rate among seedlings. The distribution patterns of seedling-to-sapling transition, sapling-to-tree transition, and seedling–sapling mortality were generally consistent with the overall distribution of seedlings and saplings at the community level. (2) At both the community and species levels, the optimal models for seedling and sapling survival were the height model and the biological factor model. Overall, survival rates of both seedlings and saplings showed a significant positive correlation with height. (3) The biological factors affecting the survival of U. pumila saplings were the basal area (BA) at breast height and the number of conspecific adult trees. The former was significantly negatively correlated with U. pumila seedling survival, while the latter was positively correlated. For R. pseudoacacia seedlings, the key biological factors were the number of heterospecific adult trees and the number of heterospecific seedlings. The former was significantly negatively correlated with survival, whereas the latter was significantly positively correlated. The primary factor influencing sapling survival was sapling height, which showed a significant positive correlation.

Patterns of floristic diversity structure, biomass and carbon stock of woody species at permanent plots in different protected forest stands across north western Himalayan, India

Preprint

Apr 2025

Forest degradation of the Himalayan ecosystem has become an issue of concern, and the increasing human-induced activities are responsible for it. The management and conservation of endemic species in the IHR can play a significant role in forest restoration works and simultaneously help sequester carbon, which is essential to mitigate climate change. Therefore, the present study aims to determine the long-term structural and functional behavior of the endemic species through long-term monitoring of permanent plots in Shimla Water Catchment Wildlife Sanctuary and Shikari Devi Wildlife Sanctuary across northwestern Himalayan using CTFS protocol. The results show that a total of 2,936 individuals over 1 cm dbh in Cedrus deodara forest stand at Shimla Water Catchment Wildlife Sanctuary, 953 individuals in the Abies pindrow dominant, and 1452 individuals in the pure Quercus semecarpifolia dominant were recorded in Shikari Devi Wildlife Sanctuary. The Cedrus deodara (2085 individuals) accounting for 71.0 % of the Relative abundance (RA) and (176.3) Important Value Index (IVI) in Shimla Water Catchment Wildlife Sanctuary. However, Abies pindrow (836 individuals) accounted for 87.7 % (RA) and (201.1) IVI in the first plot and Quercus semecarpifolia 1366 individuals, accounting for 94.08 % (RA) and 231.1 (IVI) in the second plot at Shikari Devi Wildlife Sanctuary, The above-ground biomass (AGB) of Abies pindrow dominant forest stand contributed a more significant amount of biomass 479Mgha⁻¹ and carbon stock 239Mg ha⁻¹, followed by the Cedrus deodara, 346.9 Mgha⁻¹ and 173.4 Mgha⁻¹, and Quercus semecarpifolia 334.10, Mg ha⁻¹ and 167.5 Mg ha⁻¹, respectively.

A comprehensive multigene phylogeny of Phylloporia (Hymenochaetaceae, Basidiomycota), with an emphasis on tropical African species

Article

Full-text available

Apr 2025
Persoonia

Phylloporia (Hymenochaetaceae) is becoming a very large and complex genus and the species definition is becoming a challenge, as for many other groups of Hymenochaetaceae. Phylloporia comprises 79 species up to date. However, this number is probably still largely underestimated. A comprehensive, multigene phylogeny of Phylloporia (Hymenochaetaceae, Basidiomycota), inferred from the large subunit nuclear ribosomal region (LSU), portions of the translation elongation factor 1-α (TEF-1α), and second largest subunit of RNA polymerase II (RPB2) genes is presented and discussed. The multigene phylogeny reveals several undescribed paleotropical or neotropical phylogenetic species. On this basis, complemented by both morphological and ecological data, six new species from tropical Africa are described: P. afropectinata, P. cinnamomea, P. memecyli, P. miomboensis, P. pseudoweberiana, and P. warneckeicola. A new combination, P. microspora (basionym Inonotus microsporus), is also proposed. A synthesis of the taxonomic and ecological knowledge of Phylloporia in tropical Africa is presented, with an identification key for the African species known to date. Although the multigene phylogenetic inferences do not resolve the backbone structure within Phylloporia, it reveals two main lineages, a basal A and a core B lineage. The basal lineage contains six species whereas the core lineage comprises most of the described species and a number of unnamed taxa. The multigene phylogenetic inferences also resolved several well-supported, multiple species lineages within the core lineage. These lineages are predominantly biogeographically structured with a dichotomy Neotropics vs Paleotropics. They are discussed in relation to the morpho-ecological types. Citation: Jerusalem M, Amalfi M, Yombiyeni P, Castillo G, Decock C (2025). A comprehensive multigene phylogeny of Phylloporia (Hymenochaetaceae, Basidiomycota), with an emphasis on tropical African species. Persoonia 54: 1-46.

The diversity of mangrove forests and geographical biases in their research

Article

Jan 2024
B MAR SCI

Dan Friess

Mangrove forests show amazing diversity in structure and process across scales, from site-scale patterns of vegetation distribution to global-scale biogeographic patterns of species, Gross Primary Productivity, above-ground biomass, and ecosystem service provision. Spatial patterns are apparent in mangroves because of the myriad links between biophysical forcings (e.g., hydrodynamics, geomorphic setting, climate) and species-specific physiological thresholds and tolerances, culminating in a diverse set of mangrove ecosystem forms across their pantropical range. This Perspective celebrates the spatial diversity of mangrove forests, though a review of the geographic distribution of >2400 peer-reviewed articles suggests that mangrove research may not be adequately encompassing such spatial variation, with 10 countries accounting for 67.3% of all research locations, skewed to Australia, USA, and China. A substantial proportion of this research is conducted without local collaborators and their knowledge of their systems. The mismatch in ecosystem variation vs research location has implications for how we theorize mangroves, for example the paradigm that mangroves are land builders or land consolidators was stymied for decades because early research was conducted in areas where mangroves are peat-forming, though this represents <10% of the world’s mangrove forests. There are several pathways to matching spatial patterns of mangroves with the geographic distribution of research, including collaborative international science, support for publishing from underrepresented regions, and changing citation practices. If done ethically and avoiding parachute science, then such efforts can lead to a more holistic, nuanced, and inclusive understanding of this special intertidal ecosystem.

Measuring Them all: Individual-Based Functional Spatial Patterns in Mountain Grasslands

Article

Full-text available

Mar 2025
J VEG SCI

Questions Spatial patterns of plant traits have rarely been studied at distances below 10 cm. Is it possible to detect nonrandom functional patterns at a very fine scale in mountain secondary grasslands? An analysis in terms of trait similarity, magnitude and density correlation can highlight the importance of different biotic and abiotic processes at these scales. We expect species identity to be of secondary importance if all individuals are identified by their measured traits, resulting in consistent patterns whether it is considered or not, especially if ITV (intraspecific trait variability) and functional overlap are high. Location Natural reserve “Montagna di Torricchio,” a strict reserve in the Marche region, central Apennines, Italy. Methods Plant height, leaf area, and specific leaf area have been measured for each individual (1094 ramets) in 10 quadrats, divided into two grasslands differing in canopy cover. Functional redundancy and ITV were evaluated with overlap measures and variance partitioning. Marked point pattern statistics have been used to test for non-randomness of trait patterns either by considering all individuals at once or by excluding conspecific pairs. Results At distances below 8 cm, we found evidence of trait convergence, pairs smaller than expected and negative density correlation. Above 8 cm, we found trait divergence and larger than expected pairs. We suggest biotic and abiotic causes for this, linked to physical packing or similarity in soil depth, respectively. The results differed between traits and between grasslands. The results were consistent whether conspecific pairs were excluded or not. There is a high functional overlap among species, and ITV has a large contribution to variability. Conclusions We found nonrandom functional patterns in grasslands below 10 cm, an almost unexplored scale range in any vegetation. The approach used showed that taxonomic identity is less important than the functional setting of individuals at this scale.

Global Availability of Plant DNA Barcodes as Genomic Resources to Support Basic and Policy-Relevant Biodiversity Research

Article

Full-text available

Feb 2025
MOL ECOL

Genetic technologies such as DNA barcoding make it easier and less expensive to monitor biodiversity and its associated ecosystem services, particularly in biodiversity hotspots where traditional assessments are challenging. Successful use of these data‐driven technologies, however, requires access to appropriate reference data. We reviewed the >373,584 reference plant DNA barcodes in public repositories and found that they cumulatively cover a remarkable quarter of the ~435,000 extant land plant species (Embryophyta). Nevertheless, coverage gaps in tropical biodiversity hotspots reflect well‐documented biases in biodiversity science – most reference specimens originated in the Global North. Currently, at least 17% of plant families lack any reference barcode data whatsoever, affecting tropical and temperate regions alike. Investigators often emphasise the importance of marker choice and the need to ensure protocols are technically capable of detecting and identifying a broad range of taxa. Yet persistent geographic and taxonomic gaps in the reference datasets show that these protocols rely upon risk undermining all downstream applications of the strategy, ranging from basic biodiversity monitoring to policy‐relevant objectives – such as the forensic authentication of materials in illegal trade. Future networks of investigators could work strategically to improve data coverage, which will be essential in global efforts to conserve biodiversity while advancing more fair and equitable access to benefits arising from genetic resources.

Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Article

Feb 2025

Long‐Term Fire and Vegetation Change at Cocha Cashu Biological Station, Peru

Article

Apr 2025
BIOTROPICA

Past human influence from the pre‐Columbian and colonial periods may have played a role in shaping modern Amazonian vegetation. Here, we assessed past human activities and vegetation change from a well‐studied research station in the Peruvian Amazon using charcoal and phytoliths recovered from soil cores. The moderate seasonality has contributed to its high diversity, while its remoteness has generally led to assumptions of minimal past land modification by humans. We asked: (i) Is there evidence of past human influence, including cultivation, forest opening, or plant enrichment/depletion, in the forests around Cocha Cashu Biological Station? and (ii) was there a consistent increase in palm phytolith abundances through time as has been documented in the aseasonal forests of northwestern Amazonia? Only 38 (14%) of the 279 samples analyzed contained charcoal, highlighting the rarity of past fire at Cocha Cashu. The two charcoal fragments large enough for ¹⁴ C dating had ages of 570–670 and 1350–1520 calibrated years before present. No cultivar phytoliths were found. Spheroid echinate phytoliths, produced by the palm genera Attalea, and Oenocarpus , and Euterpe , were more abundant in past samples than in modern samples. There was no increase in palm phytolith abundances from the bottom to top of the core, contrasting with recent findings from northwestern Amazonia. Our results support ideas that Cocha Cashu is composed of old growth forests and suggest that gradients of past human activities exist on local and regional scales in western Amazonia.

Aboveground Biomass and Tree Mortality Revealed Through Multi-Scale LiDAR Analysis

Article

Full-text available

Feb 2025

Accurately monitoring aboveground biomass (AGB) and tree mortality is crucial for understanding forest health and carbon dynamics. LiDAR (Light Detection and Ranging) has emerged as a powerful tool for capturing forest structure across different spatial scales. However, the effectiveness of LiDAR for predicting AGB and tree mortality depends on the type of instrument, platform, and the resolution of the point cloud data. We evaluated the effectiveness of three distinct LiDAR-based approaches for predicting AGB and tree mortality in a 25.6 ha North American temperate forest. Specifically, we evaluated the following: GEDI-simulated waveforms from airborne laser scanning (ALS), grid-based structural metrics derived from unmanned aerial vehicle (UAV)-borne lidar data, and individual tree detection (ITD) from ALS data. Our results demonstrate varying levels of performance in the approaches, with ITD emerging as the most accurate for AGB modeling with a median R² value of 0.52, followed by UAV (0.38) and GEDI (0.11). Our findings underscore the strengths of the ITD approach for fine-scale analysis, while grid-based forest metrics used to analyze the GEDI and UAV LiDAR showed promise for broader-scale monitoring, if more uncertainty is acceptable. Moreover, the complementary strengths across scales of each LiDAR method may offer valuable insights for forest management and conservation efforts, particularly in monitoring forest dynamics and informing strategic interventions aimed at preserving forest health and mitigating climate change impacts.

Host specificity of insect herbivores in tropical forests

Article

Full-text available

Jan 2005

Studies of host specificity in tropical insect herbivores are evolving from a focus on insect distribution data obtained by canopy fogging and other mass collecting methods, to a focus on obtaining data on insect rearing and experimentally verified feeding patterns. We review this transition and identify persisting methodological problems. Replicated quantitative surveys of plant–herbivore food webs, based on sampling efforts of an order of magnitude greater than is customary at present, may be cost-effectively achieved by small research teams supported by local assistants. Survey designs that separate historical and ecological determinants of host specificity by studying herbivores feeding on the same plant species exposed to different environmental or experimental conditions are rare. Further, we advocate the use of host-specificity measures based on plant phylogeny. Existing data suggest that a minority of species in herbivore communities feed on a single plant species when alternative congeneric hosts are available. Thus, host plant range limits tend to coincide with those of plant genera, rather than species or suprageneric taxa. Host specificity among tropical herbivore guilds decreases in the sequence: granivoresO leaf-minersOfructivoresOleaf-chewersZsap-suckersOxylophagesOroot-feeders, thus paralleling patterns observed in temperate forests. Differences in host specificity between temperate and tropical forests are difficult to assess since data on tropical herbivores originate from recent field studies, whereas their temperate counterparts derive from regional host species lists, assembled over many years. No major increase in host specificity from temperate to tropical communities is evident. This conclusion, together with the recent downward revisions of extremely high estimates of tropical species richness, suggest that tropical ecosystems may not be as biodiverse as previously thought.

Detecting and projecting changes in forest biomass from plot data

Chapter

Full-text available

Feb 2014

Forests hold a significant proportion of global biodiversity and terrestrial carbon stocks and are at the forefront of human-induced global change. The dynamics and distribution of forest vegetation determines the habitat for other organisms, and regulates the delivery of ecosystem services, including carbon storage. Presenting recent research across temperate and tropical ecosystems, this volume synthesises the numerous ways that forests are responding to global change and includes perspectives on: • the role of forests in the global carbon and energy budgets • historical patterns of forest change and diversification • contemporary mechanisms of community assembly and implications of underlying drivers of global change • the ways in which forests supply ecosystem services that support human lives. The chapters represent case studies drawn from the authors' expertise, highlighting exciting new research and providing information that will be valuable to academics, students, researchers and practitioners with an interest in this field.

Biogeographic patterns in liana abundance and diversity in tropical forests

Chapter

Full-text available

Jan 2015

Liana abundance and species diversity are higher in tropical than temperate forests, but substantial variation exists in liana community structure within tropical forests, particularly among biogeographical regions and along gradients in altitude, precipitation, and edaphic characteristics. We used liana surveys from across the world, which we compiled into the Global Liana Database (GLD), to examine patterns in liana abundance and diversity among continents, between islands and continental regions, and along climatic gradients. We focused particularly on the change in tropical liana abundance and diversity with mean annual precipitation and dry season length, both of which appear to be strong predictors of liana community structure. Tropical sites in the GLD provide a wide environmental gradient, spanning gradients in mean annual rainfall of 850–7000mm and seasonality (dry season length) of 0–7 months. The GLD presents a higher resolution alternative to the global dataset of 0.1 ha transects that Alwyn Gentry compiled in the 1980s and 1990s because the replication at each site is much greater, thus yielding a better estimate of liana abundance and diversity per site. Using the GLD, we found that neotropical and African forests have almost twofold greater liana density than forests in Asia, and that African liana diversity is 50% greater than sites in South America and 140% greater than sites in Central America and Asia, after accounting for climatic conditions. Within each biogeographical region, liana density declines with increasing mean annual precipitation and increases with seasonality in precipitation. Liana diversity, in contrast, shows a tendency for a hump-shaped relationship with mean annual precipitation but no relationship with dry season length. Differences in liana density and diversity among biogeographical regions in the GLD mirror those found in Gentry’s global dataset, but patterns of abundance and diversity in relation to mean annual precipitation and dry season length did not. Documentation of pantropical biogeographical patterns in liana abundance and diversity is a leap forward in the study of liana ecology because it provides the basis for the generation of hypotheses about pantropical drivers of liana proliferation and speciation.

The dynamics of a tropical dry forest in India: climate, fire, elephants and the evolution of life-history strategies

Chapter

Sep 2005

Tropical ecosystems house a significant proportion of global biodiversity. To understand how these ecosystems function we need to appreciate not only what plants, animals and microbes they contain, but also how they interact with each other. This volume, first published in 2005, synthesises the state of knowledge in this area, with chapters providing reviews or case studies drawn from research conducted in both Old and New World tropics and including biotic interactions among taxa at all trophic levels. In most chapters plants (typically trees) are the starting point, but, taken together, the chapters consider interactions of plants with other plants, with micro-organisms and with animals, and the inter-relationships of human-induced disturbance with interactions among species. An underlying theme of the volume is the attempt to understand the maintenance of high diversity in tropical regions, which remains one of the most significant unexplained observations in ecological studies.

THE EL NIÑO SOUTHERN OSCILLATION, VARIABLE FRUIT PRODUCTION, AND FAMINE IN A TROPICAL FOREST

Article

Jul 1999

Forest trees of Bukit Timah: Population ecology in a tropical forest remnant

Article

Jan 2005

The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

Article

Jan 2013

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Article

Apr 2014
BGD

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+. Though broad scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass (AGB) at spatial grains ranging from 5 to 250 m (0.025–6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100 m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Increasing biomass in Amazonian forest plots

Article

Mar 2004

A previous study by Phillips et al. of changes in the biomass of permanent sample plots in Amazonian forests was used to infer the presence of a regional carbon sink. However, these results generated a vigorous debate about sampling and methodological issues. Therefore we present a new analysis of biomass change in old-growth Amazonian forest plots using updated inventory data. We find that across 59 sites, the above-ground dry biomass in trees that are more than 10 cm in diameter (AGB) has increased since plot establishment by 1.22 ± 0.43 Mg per hectare per year (ha-1 yr-1), where 1 ha = 104 m2), or 0.98 ± 0.38 Mg ha-1 yr-1 if individual plot values are weighted by the number of hectare years of monitoring. This significant increase is neither confounded by spatial or temporal variation in wood specific gravity, nor dependent on the allometric equation used to estimate AGB. The conclusion is also robust to uncertainty about diameter measurements for problematic trees: for 34 plots in western Amazon forests a significant increase in AGB is found even with a conservative assumption of zero growth for all trees where diameter measurements were made using optical methods and/or growth rates needed to be estimated following fieldwork. Overall, our results suggest a slightly greater rate of net stand-level change than was reported by Phillips et al. Considering the spatial and temporal scale of sampling and associated studies showing increases in forest growth and stem turnover, the results presented here suggest that the total biomass of these plots has on average increased and that there has been a regional-scale carbon sink in old-growth Amazonian forests during the previous two decades.

Soil Taxonomy; A Basic System of Soil Classification for Making and Interpreting Soil Surveys

Article