DOI: 10.1111/geb.12747 RESEARCH PAPER Global importance of large-diameter trees James A. Lutz1* Tucker J. Furniss1* Daniel J. Johnson2 Stuart J. Davies3,4 David Allen5 Alfonso Alonso6 Kristina J. Anderson-Teixeira3,7 Kendall M. L. Becker1 Ana Andrade8 Jennifer Baltzer9 Erika M. Blomdahl1 Norman A. Bourg7,10 Sarayudh Bunyavejchewin11 David F. R. P. Burslem12 C. Alina Cansler13 Ke Cao14 Min Cao15 Dairon C ardenas16 Li-Wan Chang17 Kuo-Jung Chao18 Wei-Chun Chao19 Jyh-Min Chiang20 Chengjin Chu21 George B. Chuyong22 Keith Clay23 Richard Condit24,25 Susan Cordell26 Handanakere S. Dattaraja27 Alvaro Duque28 Corneille E. N. Ewango29 Gunter A. Fischer30 Christine Fletcher31 James A. Freund13 Christian Giardina26 Sara J. Germain1 Gregory S. Gilbert32 Zhanqing Hao33 Terese Hart34 Billy C. H. Hau35 Fangliang He36 Andrew Hector37 Robert W. Howe38 Chang-Fu Hsieh39 Yue-Hua Hu14 Stephen P. Hubbell40 Faith M. Inman-Narahari26 Akira Itoh41 David Janík42 Abdul Rahman Kassim31 David Kenfack3,4 Lisa Korte6 Kamil Kr al42 Andrew J. Larson43 YiDe Li44 Yiching Lin45 Shirong Liu46 Shawn Lum47 Keping Ma14 Jean-Remy Makana29 Yadvinder Malhi48 R. Memiaghe50 Sean M. McMahon49 William J. McShea7 Herve Xiangcheng Mi14 Michael Morecroft48 Paul M. Musili51 Jonathan A. Myers52 Perry Ong56 Vojtech Novotny53,54 Alexandre de Oliveira55 David A. Orwig57 Rebecca Ostertag58 Geoffrey G. Parker59 Rajit Patankar60 Richard P. Phillips23 Glen Reynolds61 Lawren Sack40 Guo-Zhang M. Song62 Sheng-Hsin Su17 Raman Sukumar63 I-Fang Sun64 Hebbalalu S. Suresh27 Mark E. Swanson65 Sylvester Tan66 Duncan W. Thomas67 Jill Thompson68 Maria Uriarte69 Renato Valencia70 Alberto Vicentini55 Tom a s Vr ska42 Xugao Wang33 George D. Weiblen71 Amy Wolf38 Shu-Hui Wu72,73 Han Xu44 Takuo Yamakura41 Sandra Yap56 Jess K. Zimmerman74 *Authors contributed equally. Global Ecol Biogeogr. 2018;1–16. wileyonlinelibrary.com/journal/geb C 2018 John Wiley & Sons Ltd V 1 2 1 Wildland Resources Department, Utah State University, Logan, Utah 2 Biology Department, Utah State University, Logan, Utah 3 Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama, Republic of Panama 4 Department of Botany, National Museum of Natural History, Washington, DC 5 Department of Biology, Middlebury College, Middlebury, Vermont 6 Center for Conservation and Sustainability, Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC 7 Conservation Ecology Center, Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC gica de Fragmentos Florestais, Instituto Nacional de Pesquisas da Amazo ^nia - INPA, Petro polis, Manaus, Amazonas, Brazil Projeto Din^amica Biolo 8 9 Biology Department, Wilfrid Laurier University, Waterloo, Ontario, Canada 10 U.S. Geological Survey, Hydrological-Ecological Interactions Branch, Water Mission Area, Reston, Virginia 11 Royal Thai Forest Department, Kasetsart and Mahidol Universities, Bangkok, Thailand 12 School of Biological Sciences, University of Aberdeen, Aberdeen, United Kingdom 13 School of Environmental and Forest Science, University of Washington, Seattle, Washington 14 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 15 Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan nico de Investiagciones Científicas Sinchi, Bogot a, D.C., Colombia Instituto Amazo 16 17 Taiwan Forestry Research Institute, Taipei 18 International Master Program of Agriculture, National Chung Hsing University, Taichung 19 Department of Forestry and Natural Resources, National Chiayi University, Chiayi City 20 Department of Life Science, Tunghai University, Taichung 21 Department of Ecology and Evolution, Sun Yat-sen University, Guangzhou 22 Department of Botany and Plant Physiology, University of Buea, Buea, Cameroon 23 Department of Biology, Indiana University, Bloomington, Indiana 24 Field Museum of Natural History, Chicago, Illinois 25 Morton Arboretum, Lisle, Illinois 26 Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, Hawaii 27 Centre for Ecological Sciences, Indian Institute of Science, Bangalore, Karnataka, India 28 Departamento de Ciencias Forestales, Universidad Nacional de Colombia Sede Medellín, Medellín, Colombia 29 Centre de Formation et de Recherche en Conservation Forestière, Gombe, Democratic Republic of Congo 30 Kadoorie Farm & Botanic Garden Corporation, Hong Kong 31 Forest Environmental Division, Forest Research Institute of Malaysia, Kepong, Malaysia 32 Environmental Studies Department, University of California, Santa Cruz, Santa Cruz, California 33 Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 34 Wildlife Conservation Society, Ituri, Democratic Republic of Congo 35 School of Biological Sciences, University of Hong Kong, Hong Kong 36 Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada 37 Plant Sciences, University of Oxford, Oxford, United Kingdom 38 Department of Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, Wisconsin 39 Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei 40 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California 41 Graduate School of Science, Osaka City University, Osaka, Japan 42 Department of Forest Ecology, Silva Tarouca Research Institute, Brno, Czech Republic 43 Department of Forest Management, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, Montana 44 Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 45 Life Science Department, Tunghai University, Taichung 46 Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 47 Asian School of the Environment, Nanyang Technological University, Singapore, Singapore 48 School of Geography and the Environment, Oxford University, Oxford, United Kingdom LUTZ ET AL. LUTZ ET AL. 49 Center for Tropical Forest Science-Forest Global Earth Observatory, Forest Ecology Group, Smithsonian Environmental Research Center, Edgewater, Maryland 50 Institut de Recherche en Ecologie Tropicale, Centre National de la Recherche Scientifique et Technologique, Libreville, Gabon 51 East African Herbarium, Botany Department, National Museum of Kenya, Nairobi, Kenya 52 Department of Biology & Tyson Research Center, Washington University in St. Louis, St. Louis, Missouri 53 New Guinea Binatang Research Centre, Madang, Papua New Guinea 54 Biology Centre, Academy of Sciences of the Czech Republic and Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic Department of Ecology, University of S~ao Paulo, S~ao Paulo, Brazil 55 56 Institute of Arts and Sciences, Far Eastern University Manila, Manila, Philippines 57 Harvard Forest, Harvard University, Petersham, Massachusetts 58 Department of Biology, University of Hawaii, Hilo, Hawaii 59 Forest Ecology Group, Smithsonian Environmental Research Center, Edgewater, Maryland 60 National Ecological Observatory Network (NEON) Inc., Denton, Texas 61 The Royal Society SEARRP (UK/Malaysia), Danum Valley, Malaysia 62 Department of Soil and Water Conservation, National Chung Hsing University, Taichung 63 Centre for Ecological Sciences and Divecha Centre for Climate Change, Indian Institute of Science, Bangalore, Karnataka, India 64 Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualian 65 School of the Environment, Washington State University, Pullman, Washington 66 Sarawak Forest Department, Kuching, Sarawak, Malaysia 67 School of Biological Sciences, Washington State University, Vancouver, Washington 68 Center for Ecology and Hydrology, Bush Estate, Penicuik Midlothian, Edinburgh, United Kingdom 69 Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, New York lica del Ecuador, Quito, Ecuador School of Biological Sciences, Pontificia Universidad Cato 70 71 Department of Plant & Microbial Biology, University of Minnesota, St. Paul, Minnesota 72 Taiwan Forestry Research Institute, Council of Agriculture, Taipei 73 Department of Biological Sciences, National Sun Yat-sen University, Kaohsiung 74 Department of Environmental Sciences, University of Puerto Rico, Rio Piedras, Puerto Rico Correspondence James A. Lutz, Wildland Resources Department, Utah State University, 5230 Old Main Hill, Logan, UT 84322. Email: james.lutz@usu.edu Funding information Utah Agricultural Experiment Station, Grant/Award Number: 1153; National Natural Science Foundation of China; National Science Foundation, Grant/Award Number: 1354741 and 1545761 Abstract Aim: To examine the contribution of large-diameter trees to biomass, stand structure, and species richness across forest biomes. Location: Global. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: We examined the contribution of large trees to forest density, richness and biomass using a global network of 48 large (from 2 to 60 ha) forest plots representing 5,601,473 stems Editor: Andrew Kerkhoff across 9,298 species and 210 plant families. This contribution was assessed using three metrics: the largest 1% of trees 1 cm diameter at breast height (DBH), all trees 60 cm DBH, and those rank-ordered largest trees that cumulatively comprise 50% of forest biomass. Results: Averaged across these 48 forest plots, the largest 1% of trees 1 cm DBH comprised 50% of aboveground live biomass, with hectare-scale standard deviation of 26%. Trees 60 cm DBH comprised 41% of aboveground live tree biomass. The size of the largest trees correlated with total forest biomass (r2 5 .62, p < .001). Large-diameter trees in high biomass forests represented far fewer species relative to overall forest richness (r2 5 .45, p < .001). Forests with more diverse large-diameter tree communities were comprised of smaller trees (r2 5 .33, p < .001). Lower largediameter richness was associated with large-diameter trees being individuals of more common species (r2 5 .17, p 5 .002). The concentration of biomass in the largest 1% of trees declined with increasing absolute latitude (r2 5 .46, p < .001), as did forest density (r2 5 .31, p < .001). Forest structural complexity increased with increasing absolute latitude (r2 5 .26, p < .001). 3 4 LUTZ ET AL. Main conclusions: Because large-diameter trees constitute roughly half of the mature forest biomass worldwide, their dynamics and sensitivities to environmental change represent potentially large controls on global forest carbon cycling. We recommend managing forests for conservation of existing large-diameter trees or those that can soon reach large diameters as a simple way to conserve and potentially enhance ecosystem services. KEYWORDS forest biomass, forest structure, large-diameter trees, latitudinal gradient, resource inequality, Smithsonian ForestGEO 1 INTRODUCTION Lindenmayer et al., 2012). The dynamics of large-diameter trees is dependent on at least two factors: (a) presence of species capable of Concentration of resources within a few individuals in a community is attaining a large size, and (b) conditions, including disturbance regimes, a pervasive property of biotic systems (West, Brown, & Enquist, 1997), that permit the development of large-diameter individuals. If the spe- whether marine (Hixon, Johnson, & Sogard, 2014), terrestrial (Enquist, cies richness of the large-diameter assemblage is high, a forest may be Brown, & West, 1998) or even anthropogenic (Saez & Zucman, 2016). better able to respond to perturbations (Musavi et al., 2017) and main- The concentration of total forest biomass in a few large-diameter tain its structure and ecological function. However, if the large- trees is no exception (Pan, Birdsley, Phillips, & Jackson, 2013). diameter species richness is low, then a forest could be susceptible to Large-diameter trees in forests take many decades or even centuries to any change that affected those few species. develop, but human or natural disturbances can decrease their Surprisingly, the specific roles of large-diameter trees are not well abundance, rapidly changing forest structure (Allen et al., 2010; anchored in two widely referenced theories of global vegetation. Both Lindenmayer, Laurance, & Franklin, 2012; Lutz, van Wagtendonk, & the unified neutral theory of biodiversity (Hubbell, 2001) and metabolic Franklin, 2009; van Mantgem et al., 2009). scaling theory (West, Enquist, & Brown, 2009) propose that plants Despite the recognized ecological significance of large-diameter have a degree of functional equivalency. The unified neutral theory trees within individual forest types, relatively little is known about the makes predictions about the rank-order abundance of species in a for- distribution and abundance of large-diameter trees at the global scale. est, but it makes no specific predictions about the rank order of large- Previous studies have showed that large-diameter trees comprise a diameter species or even if large-diameter individuals are members of large fraction of the biomass of many forests (Bastin et al., 2015; common or rare species. Metabolic scaling theory does predict the Brown et al., 1995; Clark & Clark, 1996; Lutz, Larson, Swanson, & abundance of large-diameter trees, and empirical tests of the theory Freund, 2012) and that they modulate stand-level leaf area, microcli- for more abundant, smaller-diameter individuals are generally good. mate and water use (Martin et al., 2001; Rambo & North, 2009). However, metabolic scaling theory often tends to under-predict Large-diameter trees contribute disproportionately to reproduction the abundance of large-diameter trees in temperate forests (Ander- (van Wagtendonk & Moore, 2010), influence the rates and patterns of son-Teixeira, McGarvey, et al., 2015; their fig. 2) and rather regeneration and succession (Keeton & Franklin, 2005), limit light and over-predict the abundance of large-diameter trees in tropical forests water available to smaller trees (Binkley, Stape, Bauerle, & Ryan, 2010), (Muller-Landau et al., 2006; their table 2) and in some temperate and contribute to rates and causes of mortality of smaller individuals by forests (Lutz et al., 2012; their fig. 2). Metabolic scaling theory also crushing or injuring sub-canopy trees when their bole or branches fall advances its predictions as continuous functions, and the departure to the ground (Chao, Phillips, Monteagudo, Torres-Lezama, & V asquez from theory (i.e., the spatial variation) at discrete grain sizes remains Martínez, 2009; Das, Stephenson, & Davis, 2016). Large-diameter trees unquantified. Accordingly, these theories alone cannot fully explain (and large-diameter snags and large-diameter fallen woody debris) global patterns of forest species diversity or the larger portion of the make the structure of primary forests and mature secondary forests size distribution (Coomes, Duncan, Allen, & Truscott, 2003; LaManna unique (Spies & Franklin, 1991). Large-diameter trees occur at low et al., 2017; Lutz et al., 2012; Muller-Landau et al., 2006). stem densities, yet influence spatial patterns over long inter-tree dis- However, studies do suggest that a greater generalization of forest tances (Das, Larson, & Lutz, 2018; Enquist, West, & Brown, 2009; Lutz structure in the tropical, subtropical, temperate and boreal forests of et al., 2014). Consequently, to elucidate the patterns, mechanisms and the world may indeed be possible (i.e., Gilbert et al., 2010; Ostertag, consequences of large-diameter tree ecology requires sample plots Inman-Narahari, Cordell, Giardina, & Sack, 2014; Slik et al., 2013). To 1 ha (Das, Battles, Stephenson, & van Mantgem, 2011; Lutz, 2015; the extent that forests share structural attributes either globally or jou-Me chain et al., 2014). Re regionally, our ability to model forest change may be improved by Changes in climate, disturbance regimes and logging are accelerat- focusing on global patterns in structure rather than individual species ing the decline of large-diameter trees (e.g., Bennett, McDowell, life-history traits. We expected that latitudinal trends in the concentra- Allen, & Anderson-Teixeira, 2015; Lindenmayer & Laurence, 2016; tion of biomass in the largest trees would follow trends in forest LUTZ ET AL. 5 density (with more stems in the largest diameter classes, relative bio- stems 1 cm diameter at breast height (1.3 m along the main stem; mass should be higher). We also expected that relative richness of the DBH). A representativeness analysis showed that the ForestGEO large-diameter cohort would be lower in forests with high stem density includes most major forest types of the world, albeit with some excep- because the large trees would be a smaller fraction of stems and thus a tions (see Anderson-Teixeira, Davies, et al., 2015 for details). We ana- smaller fraction of species. Our principal hypothesis was that only a lysed 48 plots in primary or older secondary forest spanning 86.48 of small proportion of the largest trees are responsible for the preponder- latitude (Figure 1), covering 1,278 ha (median size 24 ha), and including ance of forest biomass, and that the abundance and variation of these 5,601,473 stems representing 9,298 species and 210 plant families large-diameter trees reflect latitudinal gradients of forest structure. (Figure 1, Table 1, Supporting Information Table S3.1). Specifically we set out to ask four interrelated questions: There is no universal definition for what constitutes a largediameter tree. Generally, a large-diameter tree is of reproductive stature, 1. Are there global relationships between large-diameter trees (defined various ways) and forest biomass? 2. Does the richness of the large-diameter cohort depend on the richness or biomass of the forest? 3. Are there latitudinal gradients in forest density, biomass, concentration of biomass, or structural complexity? 4. Are large-diameter trees members of common or rare species in forests? is tall enough to reach the upper canopy layer of the forest, and is larger than the majority of woody stems in the forest. In any forest, the largest trees relative to the rest of the stand contribute disproportionately to ecological function and represent some of the longest-lived and most fecund components of their respective forests. The definition of large-diameter inherently depends on species and forest type. In cold, continental forests, a large-diameter tree may only be 20 cm DBH (Baltzer, Venes, Chasmer, Sniderhan, & Quinton, 2014). In productive temperate or tropical forests, a large-diameter tree may be > 100 cm DBH (Lutz et al., 2012; Lutz, Larson, Freund, Swanson, & Bible, 2013). To compare dissim- 2 MATERIALS AND METHODS We used data from the Forest Global Earth Observatory (ForestGEO; Anderson-Teixeira, Davies, et al., 2015) network of forest dynamics ilar ecosystems, we used three metrics for defining large diameter trees: 1. 99th percentile diameter (the largest 1% of trees 1 cm DBH in the forest). plots coordinated by the Smithsonian Institution, which includes major 2. Fixed diameter. We used a fixed threshold for large-diameter trees €ppen climate zones of cold, temperate and tropiforest types in the Ko of 60 cm DBH, a diameter reached by at least some trees in cal forests (Figure 1, Supporting Information Table S3.1). Forests almost all plots. included in the ForestGEO network include undisturbed primary forests or older secondary forests meeting the United Nations Food and Agricultural Organization definition of forest (trees > 5 m tall and canopy cover > 10% occurring in patches > 0.5 ha; Forest Resource 3. The large-diameter threshold. We defined the large-diameter threshold to be that diameter such that trees greater than or equal to that diameter constituted half of the aboveground live biomass of the plot. Assessment, 2015). The ForestGEO plots feature consistent field methods (Condit, 1998) and data representation (Condit, Lao, Singh, Esufali, We calculated the density, basal area, and biomass of stems 1 cm & Dolins, 2014). Importantly, these plots include all standing woody DBH and tabulated them within each square hectare (100 m 3 100 m) FIGURE 1 Location of the 48 plots affiliated with the Smithsonian Forest Global Earth Observatory (ForestGEO) used in this study 6 LUTZ of the 48 plots. Because the distribution of large-diameter trees within ET AL. diameter species (i.e., if the third most abundant species was a ‘large- forests is often not homogeneous (e.g., Lutz et al., 2013), we used the diameter species’, it would receive rank 5 3). We then used the median 1-ha scale to capture variation in structure across the plots without rank for all large-diameter species ranks within each plot, and normal- introducing the spurious high or low values of biomass that could be ized this value across plots by dividing rank by the total number of spe- jou-Me chain et al., 2014). We calcuassociated with small extents (Re cies (i.e., in a plot with 60 species, a median rank of 18 becomes 0.3). lated biomass for tropical forests (absolute latitude 23.58) by the To validate our results, we calculated structural accumulation methods of Chave et al. (2014), which uses a generic equation to pre- curves for each plot, calculating the area required to estimate forest dict biomass based on diameter, climate and wood density. The Chave density and aboveground live biomass to within 5% of the entire plot et al. (2014) equations are of the form: value. Within each plot, for each of density and biomass, we used ran- AGB5exp½21:80320:976E10:976ln ðqÞ 12:676ln ðDBHÞ20:0299ln ðDBHÞ2 dom sampling of 400 m2 quadrats with replacement (from the available (1) quadrats), beginning with a random sample of n 5 1 quadrat and ending with a random sample of n 5 total number of quadrats in each plot. where q is wood density and E is the environmental parameter. Wood This process was repeated based on the number of quadrats in each specific gravity was taken from Zanne et al. (2009), and we used the plot, which allowed us to calculate a mean and standard deviation for values hierarchically, taking species-specific values where defined, then each value of n. A percent deviation metric was calculated as: Percent difference5 abs meann 2meanplot 1SDn =meanplot genus-specific values, then family-specific values. If there was no wood specific gravity data for the plant family, or if the stem was unidenti- (3) fied, we used the global average of 0.615 g/cm3. Values for the envi- where meann is the mean of a random sampling of n quadrats, meanplot ronmental parameter E are listed in Supporting Information Table S3.1. is the mean for the entire plot, and SDn is the standard deviation for We calculated biomass for cold and temperate plots (absolute the random sample of n quadrats. latitude > 23.58) using the composite taxa-specific equations of Chojnacky, Heath, & Jenkins (2014). Those equations are of the form ln ðbiomassÞ5b0 1b1 3ln ðDBHÞ 3 RESULTS (2) where b0 and b1 are listed in Chojnacky et al. (2014; their table 5). Average stem density in the plots ranged from 608 stems/ha Species not represented by specific biomass equations were (Mudumalai, India) to 12,075 stems/ha (Lanjenchi, Taiwan) with most defaulted to an equation or wood density value for the genus or high-density plots occurring in the tropics (Tables 1 and 2, plot charac- the family. We used site-specific allometric equations for Palamanui teristics in Table S3.1 and Appendix). Aboveground live tree biomass (Ostertag et al., 2014), Laupahoehoe (Ostertag et al., 2014), Lanjenchi ranged from 13 Mg/ha (Mpala, Kenya) to 559 Mg/ha (Yosemite, USA). (Aiba & Nakashizuka, 2009) and Changbaishan (Wang, 2006). The biomass of trees 60 cm DBH ranged from 0 Mg/ha (Mpala, We further analysed the diameter–abundance relationships of Kenya, Palamanui, USA, and Scotty Creek, Canada) to 447 Mg/ha each plot based on six tree diameter classes (1 cm DBH < 5 cm, 5 (Yosemite, USA). The large-diameter tree threshold (separating the plot cm DBH < 10 cm, 10 cm DBH < 30 cm, 30 cm DBH < 60 cm, 60 aboveground forest biomass into two equal parts) varied from 2.5 cm cm DBH < 90 cm and DBH 90 cm). Diameter classes were selected (Palamanui, USA) to 106.5 cm (Yosemite, USA). Variation in the abun- to include recognized differences in tree life-history traits (Memiaghe, dance of trees of different diameter classes at the 1-ha scale was high Lutz, Korte, Alonso, & Kenfack, 2016). We performed non-metric multi- globally (Supporting Information Tables S3.2 and S3.3), and coefficient dimensional scaling (NMDS; Kenkel & Orloci, 1986) analyses on the of variation (CV) of the 1-ha stem densities was highest in the cold density of each diameter class of each 100 m 3 100 m area. We used temperate/boreal plots and lowest in the tropics (Table 2). the Bray–Curtis dissimilarity index and performed the NMDS ordina- There was a strong positive relationship between the large- tions in three dimensions using the version 2.4-4 of the vegan package diameter threshold and overall forest biomass (r2 5 .62, p < .001; Figure (Oksanen, Kindt, & Simpson, 2016) in R version 3.3.1 (R Development 2a). This relationship held for all three of our definitions for large- Core Team, 2016). We used the three-dimensional coordinates of each diameter trees (Figure 2a–c). The relationship for large-diameter 1-ha in NMDS space to create a metric for structural complexity. For threshold was strongest, but the biomass of the largest 1% of trees the 1-ha structural ordination values for each plot, we fit a one stand- also predicted total biomass (r2 5 .35, p < .001; Figure 2b) as did the ard deviation ellipsoid using the orglellipse function from the vegan3d density of stems 60 cm DBH (r2 5 .49, p < .001; Figure 2c). Results package (Oksanen, 2017). We then calculated the volume of that ellip- based on basal area were similar to those for biomass (Supporting soid as a metric of structural difference (i.e., complexity) to compare Information Figure S1.1). There was a negative relationship between the relative differences between 100 m 3 100 m areas within the plot. large-diameter species richness and total biomass (r2 5 .45, p < .001; To examine commonness of species that can reach large diame- Figure 2d), which was consistent with the negative relationship ters, we ranked all species according to their abundance within each between plot. We then identified large-diameter species as species that had 1 (r2 5 .33, p < .001; Figure 2e) and the negative relationship between large-diameter threshold and large-diameter richness individual with a DBH greater than or equal to the large-diameter large-diameter richness and the biomass of the largest 1% of trees threshold, and determined the species rank for each of these large- (r2 5 .61, p < .001; Figure 2f). In other words, plots with high biomass LUTZ ET AL. TA BL E 1 Structural characteristics of global forests Plot Large-diameter threshold (cm) Density (stems/ha) (SD) Biomass (Mg/ha) (SD) Total species (n) Large-diameter species (n) Large-diameter richness (%) Biomass of the 1% (%) Density 60 cm DBH (stems/ha) Yosemite 106.5 1399 (266) 559 (130) 14 3 21 46 52 Wind River 92.9 1207 (273) 532 (161) 26 5 19 33 72 Zofín 78.0 2404 (982) 248 (66) 11 4 36 56 41 Ituri Lenda 72.0 7553 (829) 467 (62) 396 25 6 83 34 Danum Valley 65.7 7573 (526) 486 (152) 784 62 8 72 27 65.4 2086 (792) 299 (49) 79 25 32 40 40 63.4 3925 (859) 241 (45) 22 2 9 58 37 Santa Cruz 62.3 1945 (593) 361 (102) 31 7 23 41 34 Cocoli 60.1 2164 (248) 281 (37) 170 9 5 59 32 Huai KhaKhaeng SERC a Laupahoehoe a 59.9 2506 (674) 258 (65) 284 80 28 57 20 a 59.7 1850 (1637) 259 (43) 64 22 34 31 35 Ituri Edoro 59.3 8956 (1270) 375 (46) 426 63 15 80 23 Changbaishan 56.2 1230 (188) 288 (33) 52 15 29 22 34 Bukit Timah 55.6 6273 (180) 363 (140) 353 18 5 73 19 Rabi 54.7 7988 (926) 323 (74) 346 74 21 73 14 Lambir 51.9 7635 (1233) 495 (99) 1387 223 16 69 27 51.2 4938 (463) 257 (49) 297 80 27 67 17 51.2 1112 (441) 214 (29) 34 19 56 22 20 Xishuangbanna 49.8 4565 (650) 280 (81) 450 93 21 57 19 Wanang 49.6 5523 (520) 324 (61) 581 170 29 61 14 Palanan 49.4 4981 (489) 414 (119) 324 41 13 62 27 Pasoh 48.5 5735 (631) 324 (55) 926 194 21 63 13 47.5 1981 (515) 192 (25) 44 16 36 26 14 45.4 1601 (751) 176 (16) 45 18 40 24 10 44.8 1016 (309) 310 (46) 23 13 57 23 18 Korup 42.9 7283 (920) 345 (88) 485 143 29 67 10 Manaus 42.2 6234 (441) 344 (54) 1529 260 17 59 9 Cedar Breaks 41.9 1542 (961) 168 (53) 17 8 47 34 13 Mudumalai 41.7 608 (210) 205 (33) 72 35 49 18 12 Jianfengling 40.8 6526 (993) 392 (37) 290 116 40 48 24 La Planada 40.8 4030 (243) 270 (30) 241 74 31 43 8 Fushan 39.2 4478 (1139) 224 (25) 106 33 31 46 14 Sherman 38.5 3662 (550) 275 (41) 224 31 14 53 13 Amacayacu 37.6 4948 (518) 268 (33) 1233 326 26 49 7 Kenting 36.1 3760 (410) 255 (38) 92 40 43 36 7 Lienhuachih 35.7 6131 (1760) 170 (25) 145 49 34 51 10 Harvard Foresta 35.5 3104 (2600) 260 (66) 55 17 31 23 7 Luquillo 35.5 2903 (626) 283 (53) 133 47 35 39 12 SCBI Barro Colorado Lilly Dickey a Michigan Woods Tyson 7 a Wytham Woods a (Continues) 8 LUTZ TA BL E 1 ET AL. (Continued) Plot Large-diameter threshold (cm) Density (stems/ha) (SD) Biomass (Mg/ha) (SD) Total species (n) Large-diameter species (n) Large-diameter richness (%) Biomass of the 1% (%) Density 60 cm DBH (stems/ha) Heishiding 34.5 5277 (706) 149 (27) 213 59 28 43 12 Wabikona 31.1 1692 (1017) 111 (14) 31 15 48 17 1 Gutianshan 31.0 5833 (1580) 185 (27) 159 40 25 34 2 Ilha do Cardoso 31.0 4660 (578) 148 (17) 135 43 32 41 7 Yasuni 29.1 5834 (692) 261 (48) 1075 343 32 50 8 28.6 5860 (1056) 142 (20) 172 43 25 39 3 Lanjenchi 17.2 12075 (2795) 113 (7) 128 72 56 29 1 Mpala 10.0 2963 (2902) 13 (8) 68 35 51 30 0 Scotty Creek 7.6 4136 (1407) 22 (11) 11 7 64 15 0 Palamanui 2.5 8205 (1084) 30 (5) 16 11 69 13 0 Hong Kong a Note. Values for density and biomass include trees 1 cm diameter at breast height (DBH) within each square hectare (100 m 3 100 m) of the plots, with the mean and SD calculated for each full hectare. The large-diameter threshold represents the diameter where half the biomass is contained within trees above that threshold. The biomass of the 1% indicates the proportion of total live aboveground tree biomass contributed by the largest 1% of trees 1 cm DBH. Plots are listed by declining large-diameter threshold. For additional details of the plots and forest characteristics, see Supporting Information Tables S3.1-S3.3 and references in the Appendix. a Mature secondary forest. SERC - Smithsonian Environmental Research Center; SCBI - Smithsonian Conservation Biology Institute. had high large-diameter thresholds and relatively low species richness (Figure 3a, Table 1, Supporting Information Table S3.2). However, lati- within this large-diameter structural class. tudinal gradients were not present for biomass (Figure 3b) or the large- The amount of aboveground forest biomass contained within the diameter threshold (Figure 3d). largest 1% of trees averaged among the 48 plots was 50% (weighted The three metrics for large-diameter trees were not perfectly cor- by the forest biomass of each plot, 45% as an unweighted average of related (Supporting Information Figure S1.2). The large-diameter the 48 plots), representing an average of 23% of the total species rich- threshold and the density of stems 60 cm DBH had a linear relation- ness (Table 1). The average large-diameter threshold was 47.7 cm DBH ship (r2 5 .80, p < .001), even though some forests did not have trees (half of the biomass of the 48 plots was contained within trees 60 cm DBH. The relationship between the biomass of the 1% of 47.7 cm DBH). The average portion of biomass contained within largest diameter trees and both the density of stems 60 cm DBH trees 60 cm DBH in the 48 plots was 41%. Forest density gradually and the large-diameter threshold was significant for tropical plots but decreased with increasing absolute latitude (r 5 .31, p < .001; Figure not for temperate plots. 2 3a), as did the proportion of tree biomass accounted for by the largest NMDS ordinations of the abundance of trees in the six diameter 1% of trees (r2 5 .46, p < .001; Figure 3c), following our expectations classes in each 100 m 3 100 m area showed that tropical forests have and partially a reflection of the higher stem densities in the tropics a higher degree of structural similarity than temperate or boreal forests The effect of geographical region on tree density and biomass and their variation at 1-ha scale and the abundance of largediameter trees as measured by the three metrics of proportion of biomass in the largest 1% of trees, density of trees 60 cm diameter at breast height (DBH), and large-diameter threshold TA BL E 2 Zone Plots (n) Density (trees/ ha) Density SD Density CV Biomass (Mg/ha) Biomass SD Biomass CV Biomass of the 1% (%) Density trees 60 cm DBH (trees/ha) Large-diameter threshold (cm) Cold temperate/boreal 6 2,281 1,114 47 174 98 24 23 11 37 Temperate 16 3,339 2,193 31 266 126 18 38 24 53 All Tropics Tropical Africa Tropical Asia Tropical Latin America Tropical Oceania 26 5 10 8 3 5,735 6,949 5,767 4,339 5,884 1,072 2,317 3,149 1,410 2,162 18 29 16 12 15 278 305 330 280 198 57 172 124 27 152 20 27 21 15 18 61 76 53 54 61 16 16 18 13 17 44 48 47 42 38 SD 5 standard deviation; CV 5 coefficient of variation. Note. The SD of density and the SD of biomass represent the within-region (between-plot) variation. The CV of density and CV of biomass represent the average of the individual plot 1-ha CVs, with each plot weighted equally. LUTZ ET AL. 9 F I G U R E 2 Contribution of large-diameter trees to forest structure of 48 large forest plots. Aboveground live tree biomass increases with increasing large-diameter threshold (a). The large-diameter threshold reflects the tree diameter that segments biomass into two equal parts. Below the large-diameter threshold are a large number of small-diameter trees, and above the large-diameter threshold are a smaller number of large-diameter trees. Aboveground live biomass also increases with the concentration of biomass in the largest 1% of trees (b) and the density of stems 60 cm diameter at breast height (DBH; c). Large-diameter richness declines with increasing biomass (d), which is consistent with the declining relationship between large-diameter threshold and large-diameter richness (e). The concentration of biomass in the largest 1% of trees has a strong negative relationship with large-diameter richness (f). Colours indicate increasing absolute latitude from red to green. Grey areas around regression lines indicate 95th percentile confidence intervals based on their position in the ordination (Figure 4a,b). The 1-ha scale Palamanui, USA (water limited, limited soil development and with lim- variation for tropical plots also showed a high degree of similarity both ited species complement). The structural complexity of forests (varia- globally (clustering and high overlap of red ellipses in Figure 4c,d) and tion in abundance of the six diameter classes) at 1-ha scale increased locally (smaller size of individual red ellipses). The volumes occupied by with increasing absolute latitude (Figure 5a). the 1-ha NMDS points of temperate plots, conversely, covered a wide Large-diameter trees consisted primarily of common species range in ordination space, indicating greater structural variability both (rank < 0.5; Figure 5b), and rarer species reached large diameter in among and within the plots (greater size and dispersion of green ellip- plots with higher large-diameter richness (r2 5 .17; p 5 .002). The ses in Figure 4c,d, three-dimensional animation in Supporting Informa- absolute numbers of species that reached the local large-diameter tion Figure S2). This phenomenon was also mirrored by coefficients of threshold ranged from two in tropical Laupahoehoe, USA, to 343 in variation of density and biomass of 1-ha quadrats, which differed Yasuni, Ecuador (Table 1). Tropical plots generally had > 25 species among regions and were higher in temperate and boreal forests than in reaching the large-diameter threshold (minimum nine species in tropical plots (Table 2). The grouping of plots with no trees 60 cm Cocoli, Panama). Temperate plots generally had < 10 species that DBH (left of Figure 4a,b; Supporting Information Table S3.2) shows a reached the large-diameter threshold (maximum 25 species in Smith- structural equivalency of forests growing in stressful environments. sonian Ecological Research Center (SERC), USA). On a percentage Those forests include Scotty Creek, Canada (temperature, nitrogen and basis, large-diameter richness ranged from 5% (Cocoli, Panama and hydrologically limited), Mpala, Kenya (water and herbivory limited) and Bukit Timah, Singapore) to 69% (Palamanui, USA). The relative 10 LUTZ ET AL. (Figure 2a) best explained the correlation among the 48 plots, although we would expect an upper limit based on maximum tree heights (Koch, Sillett, Jennings, & Davis, 2004) or biomass (Sillett, Van Pelt, Kramer, Carroll, & Koch, 2015; Van Pelt, Sillett, Kruse, Freund, & Kramer, 2016). The generally high proportion of biomass represented by the largest 1% of trees reinforces the importance of these individuals to carbon sequestration and productivity (e.g., Stephenson et al., 2014). Larger numbers of small- and medium-diameter trees cannot provide equivalent biomass to a few large-diameter trees, although small and medium sized trees can contribute significantly to carbon cycling (Fauset et al., 2015; Meakem et al., 2017). The implication from scaling theory (West et al., 2009) is that large-diameter trees are taller and have heavier crowns, and occupy growing space not available to smaller trees (i.e., at the top of the canopy; Van Pelt et al., 2016; West et al., 2009). Temperate forests featured a higher density of trees 60 cm DBH (Table 1), consistent with the presence of the very largest species of trees in cool, temperate forests (Sillett et al., 2015; Van Pelt et al., 2016). Temperate forests also exhibited considerably lower densities of small trees (e.g., 1 cm DBH < 5 cm; Supporting Information Table S3.2) and lower total stem density. In tropical forests, high overall stem densities are mostly due to trees with diameters 10 cm DBH (Table Gradients of forest structural attributes by absolute latitude for 48 forest plots in the ForestGEO network. Absolute latitudinal gradients in density (a) and concentration of biomass in the largest 1% of trees (c) were significant. The relationships for biomass (b; r2 5 .04, p 5 .106) and the large-diameter threshold (d; r2 5 .01, p 5 .551) were not. Colours indicate increasing absolute latitude from red to green. Grey areas around regression lines indicate 95th percentile confidence intervals FIGURE 3 2, Supporting Information Table S3.2). Metabolic scaling theory does predict the diameter–abundance relationship throughout much of the middle of the diameter range in many forest types (Anderson-Teixeira, McGarvey, et al., 2015; Lutz et al., 2012; Muller-Landau et al., 2006). However, the dichotomy between temperate forests and tropical forests, where temperate forests have lower densities of small trees and higher densities of large trees (and tropical forests the reverse), reinforces the need to examine departures from the theory’s predictions. In richness of the large-diameter assemblage was highest in plots with tropical forests, the lower proportional richness of large-diameter trees low biomass, while plots with high biomass had a lower proportion likely has at least two explanations. First, tropical forests contain many of richness represented by the large-diameter trees (Figure 2d, Table more stems per ha (Supporting Information Table S3.2) with much 1). In general, forests with lower total richness had a higher propor- higher small-diameter understorey diversity (LaFrankie et al., 2006). tion of that richness retained in the large-diameter class. Unsurpris- Secondly, not all of the species capable of reaching large diameters in ingly, plots with lower large-diameter thresholds (< 60 cm DBH) had that region may be present even in the large ForestGEO plots, and a higher proportion of species represented in the large-diameter thus even the extensive ForestGEO network may have sampling assemblage (mean 34%), whereas plots with large-diameter thresh- limitations. olds 60 cm DBH had a lower proportion of species represented in the large-diameter guild (mean 18%). The grouping of plots with only small-diameter trees (Figure 4a) shows that forests in markedly different environments can exhibit convergent structure based on different limiting factors. Large-diameter 4 DISCUSSION trees can be abundant in any region (Supporting Information Table S3.1), but different factors may limit the ability of an ecosystem to sup- The relationship between the large-diameter threshold and overall bio- port a high level of aboveground live biomass. In addition to environ- mass (Figure 2a) suggests that forests cannot sequester large amounts mental limits, ecosystems that are environmentally quite productive in of aboveground carbon without large trees, irrespective of the richness terms of annual growth may be limited by frequent, severe disturbance or density of large-diameter trees. Species capable of attaining large (e.g., typhoons in Fushan and hurricanes in Luquillo). Finally, the diameters are relatively few (Figure 2) but individuals of these species regional species pool may not contain species that can attain large are relatively abundant (Figure 5b). The relationships among biomass diameters in the local combination of climate and resource availability and richness across plots held over a range of stem densities (608 to (e.g., Palamanui, USA). The higher levels of structural complexity at 1- 12,075 stems/ha) and among trees of varying wood densities (0.10 to ha scales in temperate forests may be due to higher proportions of the 3 1.08 g/cm ). A linear relation of biomass to large-diameter threshold forests where small trees predominate and large-diameter trees are LUTZ ET AL. 11 F I G U R E 4 Three-dimensional non-metric multidimensional scaling (NMDS) results for density of trees organized into six diameter classes in 1260, 100 m 3 100 m hectares of 48 forest plots in the ForestGEO network (a, b). The structural classes (diameter bins) used in the NMDS ordination are superimposed in black text (a, b). The within-plot variation in structure for each plot is shown by depiction of the SD ellipses of the individual 100 m 3 100 m hectares within each plot [c, d; where (c) reflects the variation of NMDS1 versus NMDS2 (a) and (d) reflects the variation of NMDS1 versus NMDS3 (b)]. Ordination stress 5 0.047. Colours indicate increasing absolute latitude from red to green, with plot centroids numbered (a, b). See Supporting Information Figure S2 for a three-dimensional animation of the structural ordination generally excluded (i.e., swamps, rocky outcrops), supported by the heterogeneity) with increasing absolute latitude (Figure 5a) may in fact higher coefficient of variation of density in temperate and cold forests be hump-shaped, with decreasing complexity at higher latitudes than (Table 2). The trend of increasing structural complexity (i.e., 1-ha the 61.38N of the Scotty Creek, Canada, plot. 12 LUTZ ET AL. F I G U R E 5 The 1-ha scale structural complexity of 48 forest plots in the ForestGEO network as a function of absolute latitude (a). The metric of structural complexity is the volume of the three-dimensional ellipsoid generated from the non-metric multidimensional scaling (NMDS) ordination of abundance in structural classes (see Figure 4 for two-dimensional projections and Supporting Information Figure S2 for a three-dimensional animation). The rank order of large-diameter species in 48 forest plots (b). Rank order is normalized to the range from zero to one to compare plots with differing species richness. Lower proportions of large-diameter species rank correspond to more abundant species (median large-diameter species rank < 0.5 for all 48 forest plots). Species attaining large-diameters were the more common species in the forest plots. Colours indicate increasing absolute latitude from red to green There is still considerable uncertainty as to what will happen to tree richness was highest in tropical forests, suggesting that the large- large-diameter trees in the Anthropocene when so much forest is being diameter tree guild may have different adaptations that will allow at felled for timber and farming, or is being affected by climate change. least some species to persist (Musavi et al., 2017). Secondly, the pool Bennett et al. (2015) suggested that the current large-diameter trees of species that can reach large diameters may have been undersampled are more susceptible to drought mortality than smaller-diameter trees. in the plots used here, implying an even higher level of richness may Larger trees, because of their height, are susceptible to sapwood cavi- exist in some forests than captured in these analyses. tation and are also exposed to high radiation loads (Allen, Breshears, & The finding that large-diameter trees are members of common McDowell, 2015; Allen et al., 2010), but vigorous large-diameter indi- species groups (Figure 5b) contradicts the neutral theory’s assumption viduals may also still be sequestering more carbon than smaller trees of functional equivalency (Hubbell, 2001). Similarly the different struc- (Stephenson et al., 2014). Both Allen et al. (2015) and Bennett et al. tural complexity of forests worldwide (Figure 5a) contradicts the (2015) suggested that larger trees will be more vulnerable to increasing assumptions of universal size–abundance relationships of metabolic drought than small trees, and Luo and Chen (2013) suggested that scaling theory (Enquist et al., 1998, 2009). The presence of a latitudinal although the rate of mortality of larger trees will continue to increase gradient in forest density (Figure 3a) and the lack of a latitudinal gradi- because of global climate change, smaller trees will experience more ent in forest biomass (Figure 3b) suggest that size–abundance relation- drought-related mortality. These last two conclusions need not be in ships are not universal but depend on region or site conditions conflict as the background mortality rates for smaller trees are higher (Table 2). than those of larger trees within mature and old-growth forests (Larson Characterizing forest structural variation did require these large & Franklin, 2010). What remains generally unanswered is whether the plots (Supporting Information Figure S1.3), a finding consistent with increasing mortality rates of large-diameter trees will eventually be off- jou-Me chain et al., 2014). other studies examining forest biomass (Re set by regrowth of different individuals of those same (or functionally With large plot sizes and global distribution, ForestGEO is uniquely similar) species. Any reduction in temperate zone large-diameter tree suited to capture structural variation (i.e., the heterogeneity in the abundance may be compounded by the low large-diameter tree diver- abundance of trees of all diameter classes). The relatively large area sity in temperate forests (temperate forests had high relative large- required (6.5 ha, on average) to estimate biomass to within 5% of the diameter richness, but low absolute large-diameter richness). Large- entire plot value reinforces conclusions that the distribution of large- diameter tree richness in tropical forests suggests more resilience to diameter trees is not homogeneous within forests (e.g., Table 2; Fur- projected climate warming in two ways. First, absolute large-diameter niss, Larson, & Lutz, 2017; Lutz et al., 2012, 2013). We note that this LUTZ ET AL. calculation of the size of the plot required is a measure of spatial varia- Alfonso Alonso tion within the forest, and does not depend on the accuracy of the allo- Kristina J. Anderson-Teixeira metric equations used for calculating each tree’s biomass. Allometric Kendall M. L. Becker http://orcid.org/0000-0001-6860-8432 http://orcid.org/0000-0001-8461-9713 http://orcid.org/0000-0002-7083-7012 http://orcid.org/0000-0002-2614-821X equations can be imprecise for large-diameter trees, both because of Erika M. Blomdahl their structural variability and the enormous sampling effort, and there- Kuo-Jung Chao http://orcid.org/0000-0003-4063-0421 fore our estimates of overall biomass could be off by 6 15% (Lutz Sara J. Germain http://orcid.org/0000-0002-5804-9793 et al., 2017). Keping Ma Although temperate plots had much lower overall species diversity 13 http://orcid.org/0000-0001-9112-5340 Jonathan A. Myers compared to the tropical plots, tropical plots had much more homoge- Perry Ong neous structure, both within and across plots (Figure 4), potentially sug- Richard P. Phillips gesting greater structural equivalency among the many species present. Xugao Wang http://orcid.org/0000-0002-2058-8468 http://orcid.org/0000-0002-1597-1921 http://orcid.org/0000-0002-1345-4138 http://orcid.org/0000-0003-1207-8852 We found that the largest 1% of trees constitute 50% of the biomass (and hence, carbon), supporting our hypothesis of their significance, at R EFE R ENC E S least in primary forests or older secondary forests. The conservation of Aiba, M., & Nakashizuka, T. (2009). Architectural differences associated with adult stature and wood density in 30 temperate tree species. Functional Ecology, 23, 265–273. large-diameter trees in tropical and temperate forests is therefore imperative to maintain full ecosystem function, as the time necessary for individual trees to develop large sizes could preclude restoration of full ecosystem function for centuries following the loss of the oldest and largest trees (Lindenmayer et al., 2012). Clearly, areas that have been recently logged lack large-diameter trees, and therefore have less structural heterogeneity than older forests. That the largest individuals belong to relatively few common species in the temperate zone means that the loss of large-diameter trees could alter forest function – if species that can attain large diameters disappear, forests will feature greatly reduced structural heterogeneity (e.g., Needham et al., 2016), biomass, and carbon storage. In the tropical zones, the larger absolute numbers of species reaching large diameters may buffer those forests against structural changes. Policies to conserve the tree species whose individuals can develop into large, old trees (Lindenmayer et al., 2014) could promote retention of aboveground biomass globally as well as maintenance of other ecosystem functions. AC KNOW LEDG MENT S Funding for workshops during which these ideas were developed was provided by NSF grants 1545761 and 1354741 to SD Davies. This research was supported by the Utah Agricultural Experiment Station, Utah State University, and approved as journal paper number 8998. Acknowledgements for the global support of the thousands of people needed to establish and maintain these 48 plots can be found in Supporting Information Appendix S4. References to locations refer to geographical features and not to the boundaries of any country or territory. DAT A ACC ES SIBI LI TY Data for plots in the ForestGEO network are available through the online portal at: http://www.forestgeo.si.edu ORCI D James A. Lutz Tucker J. Furniss Daniel J. 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LUTZ is an Assistant Professor of Forest Ecology at Utah State University. He studies forest ecosystems to contribute to sciencebased conservation and management with particular emphasis on demography and spatial patterns of tree mortality and the effects of fire on old-growth forest communities. TUCKER J. FURNISS is a Ph.D. student at Utah State University. He studies spatial patterns of trees and demographic processes. The ForestGEO Network includes the senior investigators who collaborated on this research. The Smithsonian ForestGEO network conducts long-term, large-scale research on forests around the world. This collaborative effort seeks to increase scientific understanding of forest ecosystems, guide sustainable forest management and natural-resource policies, monitor the impacts of global change and build capacity in forest science. SU PP ORT ING INF OR MATI ON Additional Supporting Information may be found online in the supporting information tab for this article. 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