Diversity and carbon storage across the tropical forest biome

TLDR: In this article, a pan-tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests.

Abstract: Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to protect forest to fulfil biodiversity and climate mitigation policy targets, but the conservation strategies needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where carbon stocks in aboveground biomass and species identifications have been simultaneously and robustly quantified. Here, we compile a unique pan-tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent (Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity effects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships at scales relevant to conservation planning means that carbon-centred conservation strategies will inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree diversity and carbon stocks both require explicit consideration when optimising policies to manage tropical carbon and biodiversity.

Full text

1
Scientific RepoRts | 7:39102 | DOI: 10.1038/srep39102
www.nature.com/scientificreports
Diversity and carbon storage across
the tropical forest biome
Martin J. P. Sullivan
1,*
, Joey Talbot
1,*
, Simon L. Lewis
1,2,*
, Oliver L. Phillips
1,*
, Lan Qie
1
,
Serge K. Begne
1,3
, Jerôme Chave
4
, Aida Cuni-Sanchez
2
, Wannes Hubau
1
, Gabriela Lopez-
Gonzalez
1
, Lera Miles
5
, Abel Monteagudo-Mendoza
6,7
, Bonaventure Sonké
3
,
Terry Sunderland
8,9
, Hans ter Steege
10,11
, Lee J. T. White
12,13,14
, Ko Aum-Baoe
15
, Shin-
ichiro Aiba
16
, Everton Cristo de. Almeida
17
, Edmar Almeida de Oliveira
18
, Patricia Alvarez-
Loayza
19
, Esteban Álvarez Dávila
20
, Ana Andrade
21
, Luiz E. O. C. Aragão
22
, Peter Ashton
23
,
Gerardo A. Aymard C.
24
, Timothy R. Baker
1
, Michael Balinga
25
, Lindsay F. Banin
26
,
Christopher Baraloto
27
, Jean-Francois Bastin
28,29
, Nicholas Berry
30
, Jan Bogaert
31
,
Damien Bonal
32
, Frans Bongers
33
, Roel Brienen
1
, José Luís C. Camargo
34
, Carlos Cerón
35
,
Victor Chama Moscoso
7
, Eric Chezeaux
36
, Connie J. Clark
37
, Álvaro Cogollo Pacheco
38
,
James A. Comiskey
39,46
, Fernando Cornejo Valverde
40
, Eurídice N. Honorio Coronado
41
,
Greta Dargie
1
, Stuart J. Davies
42
, Charles De Canniere
43
, Marie Noel Djuikouo K.
44
,
Jean-Louis Doucet
45
, Terry L. Erwin
46
, Javier Silva Espejo
7
, Corneille E. N. Ewango
47,48
,
Sophie Fauset
1,49
, Ted R. Feldpausch
22
, Rafael Herrera
50,51
, Martin Gilpin
1
, Emanuel Gloor
1
,
Jeerson S. Hall
52
, David J. Harris
53
, Terese B. Hart
54,55
, Kuswata Kartawinata
56,57
,
Lip Khoon Kho
58
, Kanehiro Kitayama
59
, Susan G. W. Laurance
60
, William F. Laurance
60
,
Miguel E. Leal
61
, Thomas Lovejoy
62
, Jon C. Lovett
1
, Faustin Mpanya Lukasu
63
, Jean-
Remy Makana
47
, Yadvinder Malhi
64
, Leandro Maracahipes
65
, Beatriz S. Marimon
18
,
Ben Hur Marimon Junior
18
, Andrew R. Marshall
66,67
, Paulo S. Morandi
18
,
John Tshibamba Mukendi
63
, Jaques Mukinzi
47,68
, Reuben Nilus
69
, Percy Núñez Vargas
7
,
Nadir C. Pallqui Camacho
7
, Guido Pardo
70
, Marielos Peña-Claros
33,71
, Pascal Pétronelli
72
,
Georgia C. Pickavance
1
, Axel Dalberg Poulsen
73
, John R. Poulsen
37
, Richard B. Primack
74
,
Hari Priyadi
75,76
, Carlos A. Quesada
21
, Jan Reitsma
77
, Maxime Réjou-Méchain
4
,
Zorayda Restrepo
78
, Ervan Rutishauser
79
, Kamariah Abu Salim
80
, Rafael P. Salomão
81
,
Ismayadi Samsoedin
82
, Douglas Sheil
8,83
, Rodrigo Sierra
84
, Marcos Silveira
85
, J. W. Ferry Slik
79
,
Lisa Steel
86
, Hermann Taedoumg
3
, Sylvester Tan
87
, John W. Terborgh
37
, Sean C. Thomas
88
,
Marisol Toledo
71
, Peter M. Umunay
89
, Luis Valenzuela Gamarra
6
, Ima Célia Guimarães Vieira
81
,
Vincent A. Vos
70,90
, Ophelia Wang
91
, Simon Willcock
92,93
& Lise Zemagho
3
Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to
protect forest to full biodiversity and climate mitigation policy targets, but the conservation strategies
needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon
storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where
carbon stocks in aboveground biomass and species identications have been simultaneously and
robustly quantied. Here, we compile a unique pan-tropical dataset of 360 plots located in structurally
intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale
evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all
plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent
(Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity
eects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships
at scales relevant to conservation planning means that carbon-centred conservation strategies will
inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree
diversity and carbon stocks both require explicit consideration when optimising policies to manage
tropical carbon and biodiversity.
received: 15 July 2016
accepted: 26 October 2016
Published: 17 January 2017
OPEN

www.nature.com/scientificreports/
2
Scientific RepoRts | 7:39102 | DOI: 10.1038/srep39102
Biodiversity is threatened by the loss of natural habitats and climate change
1–3
. Tropical forests are under particular
pressure, whilst also being the most diverse biomes on the planet
4
. By legally protecting areas, tropical countries
can safeguard ecosystems with high biodiversity value
5
, and so address their policy targets to reduce biodiversity
loss
6
. Likewise, carbon losses from the conversion of forest to other land-uses represent major emission sources
for many tropical countries
7
, and so incentives such as the UN REDD+ policy framework have emerged to help
safeguard areas with high carbon stocks
8
. Yet the potential for protection of carbon-rich areas to directly benet
1
School of Geography, University of Leeds, Leeds, UK.
2
Department of Geography, University College London,
London, UK.
3
Plant Systematic and Ecology Laboratory, University of Yaounde I, Cameroon.
4
Université Paul Sabatier
CNRS, Toulouse, France.
5
United Nations Environment Programme World Conservation Monitoring Centre,
Cambridge, UK.
6
Jardín Botánico de Missouri, Oxapampa, Perú.
7
Universidad Nacional de San Antonio Abad del
Cusco, Cusco, Perú.
8
CIFOR, Bogor, Indonesia.
9
College of Marine and Environmental Sciences, James Cook University,
Cairns, Australia.
10
Naturalis Biodiversity Center, Leiden, Netherlands.
11
Ecology and Biodiversity Group, Utrecht
University, Utrecht, Netherlands.
12
Agence Nationale des Parcs Nationaux, Libreville, Gabon.
13
Institut de Recherche
en Ecologie Tropicale, Libreville, Gabon.
14
School of Natural Sciences, University of Stirling, Stirling, UK.
15
Mensuration
Unit, Forestry Commission of Ghana, Kumasi, Ghana.
16
Graduate School of Science and Engineering, Kagoshima
University, Japan.
17
Instituto de Biodiversidade e Floresta, Universidade Federal do Oeste do Pará, Santarém, Brazil.
18
Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil.
19
Center for Tropical Conservation, Duke University,
Durham, NC, USA.
20
Red para la Mitigación y Adaptación al Cambio Climático de la UNAD, Bogota, Colombia.
21
Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil.
22
Geography, College of Life and Environmental
Sciences, University of Exeter, Exeter, UK.
23
Department of Organismic and Evolutionary Biology, Harvard University,
Cambridge, MA, USA.
24
Programa de Ciencias del Agro y el Mar, Herbario Universitario, Barinas, Venezuela.
25
CIFOR,
Conakry, Guinea.
26
Centre for Ecology and Hydrology, Penicuik, UK.
27
International Center for Tropical Botany,
Department of Biological Sciences, Florida International University, Miami, FL, USA.
28
UMR AMAP, IRD, Montpellier,
France.
29
UPR BSEF, CIRAD, Montpellier, France.
30
The University of Edinburgh, School of GeoSciences, Edinburgh,
UK.
31
Biodiversity and Landscape Unit, Gembloux Agro-Bio Tech, Université de Liège, Gembloux, Belgium.
32
INRA,
UMR EEF, Champenoux, France.
33
Forest Ecology and Forest Management group, Wageningen University,
Wageningen, The Netherlands.
34
Instituto Nacional de Pesquisas da Amazônia, Projeto Dinâmica Biológica de
Fragmentos Florestais, Manaus, Brazil.
35
Herbario Alfredo Paredes, Universidad Central del Ecuador, Quito, Ecuador.
36
Rougier-Gabon, Libreville, Gabon.
37
Nicholas School of the Environment, Duke University, Durham, NC, USA.
38
Jardín Botánico Joaquín Antonio Uribe, Medellín, Colombia.
39
Inventory & Monitoring Program, National Park
Service, Fredericksburg, VA, USA.
40
Andes to Amazon Biodiversity Program, Puerto Maldonado, Perú.
41
Instituto de
Investigaciones de la Amazonia Perúana, Iquitos, Perú.
42
Smithsonian Tropical Research Institute, Washington, DC,
USA.
43
Landscape Ecology and Vegetal Production Systems Unit, Universite Libre de Bruxelles, Brussels, Belgium.
44
Department of Botany & Plant Physiology, Faculty of Science, University of Buea, Buea, Cameroon.
45
Forest
Ressources Management, Gembloux Agro-Bio Tech, University of Liege, Belgium.
46
Smithsonian Institution,
Washington, DC, USA.
47
Wildlife Conservation Society-DR Congo, Kinshasa I, Democratic Republic of Congo.
48
Centre
de Formation et de Recherche en Conservation Forestiere (CEFRECOF), Democratic Republic of Congo.
49
Institute of
Biology, UNICAMP, Campinas, Brazil.
50
Centro de Ecologia, Instituto Venezolano de Investigaciones Cientificas,
Caracas, Venezuela.
51
Institut für Geographie und Regionalforschung, Geoökologie, University of Vienna, Vienna,
Austria.
52
Smithsonian Tropical Research Institute, Panamá, Republic of Panama.
53
Royal Botanic Garden Edinburgh,
Edinburgh, UK.
54
Lukuru Wildlife Research Foundation, Kinshasa, Gombe, Democratic Republic of Congo.
55
Division
of Vertebrate Zoology, Yale Peabody Museum of Natural History, New Haven, CT, USA.
56
Herbarium Bogoriense,
Indonesian Institute of Sciences, Bogor, Indonesia.
57
Integrative Research Center, The Field Museum, Chicago, IL,
USA.
58
Tropical Peat Research Institute, Biological Research Division, Malaysian Palm Oil Board, Selangor, Malaysia.
59
Kyoto University, Kyoto, Japan.
60
Centre for Tropical Environmental and Sustainability Sciences and College of
Science and Engineering, James Cook University, Cairns, Australia.
61
Wildlife Conservation Society, Kampala, Uganda.
62
Department of Environmental Science and Policy, George Mason University, Fairfax, VA, USA.
63
Faculté des Sciences
Agronomiques, Université de Kisangani, Kisangani, Democratic Republic of Congo.
64
School of Geography and the
Environment, University of Oxford, Oxford, UK.
65
Universidade Federal de Goiás, Goiânia, Brazil.
66
Flamingo Land Ltd,
Kirby Misperton, UK.
67
CIRCLE, Environment Department, University of York, York, UK.
68
Salonga National Park,
Kinshasa I, DR Congo.
69
Sabah Forestry Department, Sabah, Malaysia.
70
Universidad Autónoma del Beni, Riberalta,
Bolivia.
71
Instituto Boliviano de Investigación Forestal, Santa Cruz de la Sierra, Bolivia.
72
CIRAD, UMR Ecologie des
Forêts de Guyane, Sinamary, French Guiana, France.
73
Natural History Museum, University of Oslo, Oslo, Norway.
74
Department of Biology, Boston University, Boston, MA, USA.
75
CIFOR, Bogor, Indonesia.
76
Southern Swedish Forest
Research Center, Swedish University of Agricultural Sciences, Alnarp, Sweden.
77
Bureau Waardenburg, The
Netherlands.
78
Fundación Con Vida, Medellín, Colombia.
79
Carboforexpert, Geneva, Switzerland.
80
Environmental and
Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Brunei, Darussalam.
81
Museu Paraense Emilio Goeldi,
Belém, Brazil.
82
FORDA, The Ministry of Forestry and Environment, Bogor, Indonesia.
83
Norwegian University of Life
Sciences, Aas, Norway.
84
GeoIS, Quito, Ecuador.
85
Museu Universitário, Universidade Federal do Acre, Brazil.
86
World
Wildlife Fund, Washington, DC, USA.
87
CTFS-AA Asia Program, Harvard University, Cambridge, MA, USA.
88
Faculty of
Forestry, University of Toronto, Toronto, Canada.
89
Yale School of Forestry & Environmental Studies, New Haven, CT,
USA.
90
Centro de Investigación y Promoción del Campesinado - Regional Norte Amazónico, Riberalta, Bolivia.
91
School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff AZ, USA.
92
Biological Sciences, University of Southampton, Southampton, UK.
93
School of Environment, Natural Resources and
Geography, Bangor University, Bangor, UK.
*
These authors contributed equally to this work. Correspondence and
requests for materials should be addressed to M.J.P.S. (email: m.j.sullivan@leeds.ac.uk)

www.nature.com/scientificreports/
3
Scientific RepoRts | 7:39102 | DOI: 10.1038/srep39102
biodiversity, and vice versa, depends critically on the relationship between biomass carbon and tree diversity, at
relevant scales. A positive relationship would indicate potential synergies while a negative relationship would
indicate dicult trade-os between biodiversity and carbon conservation
9
. In the absence of any relationship,
optimal solutions for protected area placement need to carefully and separately consider the distribution of carbon
stocks and the distribution of biodiversity
10
. Understanding these distributions and potential carbon-biodiversity
trade-os is important, as protecting some forest can divert threats onto other unprotected areas
11
.
e expected form of diversity-carbon relationships in tropical forests and the strength and scale-dependence
of any underlying mechanisms are uncertain. Numerous experimental studies have demonstrated that plant
diversity promotes biomass production, with niche partitioning and positive species interactions allowing diverse
communities to exploit available resources more eciently
12,13
. Diversity can also increase productivity through
selection eects, where communities that contain a larger sample of the species pool are more likely to contain
high functioning species that contribute strongly to ecosystem productivity
14
. Positive diversity-productivity
relationships have been found in low diversity mid-latitude forests
15–17
, potentially due to increased canopy pack-
ing through complimentary canopy architecture in higher diversity forests
18
. Yet, it is unclear how signicant
such mechanisms are in diverse tropical forests, as experimental and theoretical work indicates that the posi-
tive eect of diversity may saturate at high species richness
12,19
. Furthermore, additional traits associated with
high-productivity species could conceivably lead to a positive diversity-biomass mortality relationship, as highly
productive stands tend to be composed of trees with shorter biomass residence times
20
. Overall, this alongside
high-productivity stands consisting of smaller, lighter-wooded trees
21
, may lead to a negative diversity-biomass
carbon storage relationship.
Previous studies investigating the tree diversity-carbon stock relationship in tropical forests have reported a
positive relationship at ne spatial scales
22,23
. However, the form of the relationship at the stand-level (i.e. among
1 ha plots) is less clear (Table1), as some studies report a continued positive diversity-carbon relationship among
sampling locations
23–25
, while one other did not detect a relationship among 1 ha subplots within 25 larger plots
22
.
us, while there is some evidence that higher tree diversity promotes higher carbon stocks per unit area in
diverse tropical forests
22–24
, it is unknown whether any positive eect is strong enough for carbon and diversity to
co-vary at scales relevant to conservation planning.
Here we analyse a unique dataset of 360 inventory plots across the three major tropical forest blocs in the
Americas, Africa, and the Sundaland biogeographic region in Southeast Asia (subsequently referred to as Asia).
Importantly, this dataset greatly improves sampling of the two most extensive contiguous areas of tropical forest
in the world, centred on the Amazon and Congo Basins (Table1). Each plot was surveyed by standardised meth-
ods and is of uniform size, allowing robust quantication of co-located aboveground live carbon and tree diversity
estimates. We analyse this standardised, multi-continental dataset at three spatial scales. Firstly, we explore forest
carbon and diversity patterns within South America, Africa and Asia, in order to characterise among-continent
variations in tree alpha diversity, beta diversity, and carbon stocks. Secondly, we assess diversity-carbon relation-
ships across each of the continents, initially by looking at the bivariate association of tree diversity metrics and
carbon stocks per unit area, and then re-examining the relationships aer controlling for potentially confound-
ing environmental variation and residual spatial autocorrelation. Finally, we investigate ne-scale relationships
Study
Geographical
scope
Number of plots Number of sampling locations
Taxonomic
level Diversity measures
Minimum
identication
level
Diversity-carbon
relationship
1 ha 0.04 ha Tot al Amazon Congo Borneo
Within
stand
Among
stands
is study Tropics 360 6536 166 77 52 18
Species,
genus and
family
Richness, rareed
richness, Shannon
diversity, Simpson
diversity, Fisher’s
alpha and functional
diversity
80% stems to
genus, 60% to
species
+ None
Ref.22
Tropical and
temperate
688
a
17200
a
25 2 1 1 Species Richness
b
None given + [None]
Ref.24 Tropics 59 NA 11 3 2 0 Genus
Richness, Shannon
diversity, functional
diversity
80% stems to
family
NA +
Ref.23
Tropical
America
294 1975
d
59 47 0 0 Species
Richness, rareed
richness and Shannon
diversity
None given + +
e
Table 1. Pan-tropical and continental studies assessing the diversity-carbon relationship. Sampling locations
are groups of plots in close proximity to each other (individual large plots in ref. 22, TEAM study sites in ref. 24,
“forest sites” in ref. 23, groups of plots within 5 km of each other in this study). e number of sampling locations
in the largest blocs of forest in each continent are given, these are the Amazon basin and surrounding contiguous
forest, the Congo basin and surrounding contiguous forest, and Borneo. + indicates a positive diversity-carbon
relationship, NA indicates the relationship was not studied at the given scale. In this study, ref. 22 and ref. 24
all stems ≥ 10 cm d.b.h. were measured, in ref. 23 the minimum stem diameter measured varied among plots
(either 5 cm or 10 cm).
a
Sample size not stated, so maximum possible number of 1 ha and 0.04 ha subplots given.
b
Stem density was included as a covariate in analysis.
c
Relationship analysed among 1 ha plots within sampling
locations, not among sampling locations.
d
0.1 ha not 0.04 ha.
e
Relationship among sampling locations.

www.nature.com/scientificreports/
4
Scientific RepoRts | 7:39102 | DOI: 10.1038/srep39102
between tree diversity and carbon within 0.04 ha subsections of 1 ha plots, where environmental dierences that
may obscure a positive diversity eect on carbon are accounted for. is approach allows us to (1) examine basic
patterns of diversity and carbon across the biome, (2) test if more diverse tropical forests are also in fact more
carbon dense, and (3) explore whether relationships between diversity and carbon-storage, aer accounting for
the eect of potentially confounding variables, are consistent with tree diversity having a positive eect on carbon
in tropical forests. We conduct additional analyses to assess support for the operation of selection eects and
niche complementarity at dierent spatial scales. We focus on carbon in aboveground live biomass derived using
allometric relationships, and diversity metrics relating to taxon richness. We also repeat analyses using alternative
diversity metrics that consider species abundance and functional diversity for which results and inferences are
similar (see Supplementary Information).
Results
Pantropical forest carbon and diversity. Our standardised methods of inventory reveal great variation
in both aboveground live carbon stocks and tree diversity within continents and across the humid tropical forest
biome. While it is possible to nd almost any combination of both parameters (Fig.1), the plots reveal large dif-
ferences in carbon and diversity amongst the three continents (Table2). African tropical forests are characterised
by high carbon storage per unit area and consistently low alpha-diversity (even the most species-rich African plot
had fewer species than the median species richness recorded in South America and Asia). By contrast, in South
American plots carbon storage per unit area was lower than in African forests (Fig.1). Nevertheless both diversity
and carbon vary greatly within South America, as reects previously reported gradients in species richness
26
and
biomass
27,28
, with some stands in the Guiana Shield region containing carbon stocks comparable to forests in the
paleotropics (Fig.1). Asian forests dier again, having on average both high carbon storage per unit area and high
tree diversity. ese dierences in diversity amongst continents remain when diversity metrics are standardised
per 300 stems (Table2), and when the analysis was repeated only including plots with > 90% of stems identied to
species level (SupplementaryTable3), thus are robust to diering stem numbers (lower in Africa, negative bino-
mial GLM χ
2
= 188.6, P < 0.001), and are unaected by our levels of tree identication (not dierent amongst
continents, Kruskal-Wallis test H = 2.1, P = 0.335). is pantropical assessment of forest carbon stocks and diver-
sity is consistent with previous reports from individual continents, indicating high biomass in forests in Africa
29
and Borneo
30,31
, high diversity in central and western Amazonia
32
and low diversity in Africa
33,34
. Our analysis
demonstrates that forests across the Sundaland region of Southeast Asia are not only amongst the most diverse in
the tropics, as noted elsewhere
33
, but also amongst the most carbon-dense.
Beta-diversity also showed contrasting patterns amongst continents. Tree communities in neighbouring for-
ests were least similar in Asia and most similar in Africa, where diversity rapidly saturates over geographic dis-
tance and plots (Fig.2, SupplementaryFig.11). However while similarity in species composition decayed most
050 100 150 200250 300
50
100
150
200
250
300
350
Species richness
Carbon (Mg.ha
−1
)
South America (SA)
Africa (AF)
Asia (AS)
AS
AF
SA
AS
AF
SA
Figure 1. No relationship across the tropical forest biome between carbon stocks per unit area and tree
species richness. Green circles = plots in South America (n = 158), orange squares = Africa (n = 162) and
purple triangles = Asia (n = 40). Boxplots show variation in species richness and biomass carbon stocks in
each continent. Both carbon and species richness diered signicantly between continents (Table2), but no
signicant correlation exists between carbon and species richness, neither within each continent (τ ≤ 0.132,
P ≥ 0.12), nor across all three (linear regression weighted by sampling density in each continent, β < − 0.001,
t = 0.843, P = 0.4, weights = 1.2 for South America, 0.6 for Africa and 1.8 for Asia). Results for other diversity
metrics are similar (SupplementaryFig.S13).

www.nature.com/scientificreports/
5
Scientific RepoRts | 7:39102 | DOI: 10.1038/srep39102
strongly with distance in South America, there was weaker distance decay in Asia (Fig.2, SupplementaryFig.12).
As a result, while adjacent stands dier most in Asia, at distances > 1,000 km plots in Asia are no more dissimilar
than equidistant plot pairs in South America. Dierences in beta diversity could have been driven by dierences
in gamma diversity
35
. However, local tree communities remained more similar in Africa than other continents
when null models were used to account for variation in gamma diversity (SupplementaryFig.13). Gamma diver-
sity was comparable in South America and Asia
33
, so was also unlikely to drive dierences in the distance decay
of tree community similarity in those continents.
Large-scale diversity-carbon relationships. Notably, aboveground carbon stocks in live biomass per unit
area was unrelated to tree species richness amongst 1 ha plots, whether analysed within continents or when com-
bining all data in a pan-tropical analysis (Fig.1, Table3). Correlations with other diversity metrics varied in sign
but were also non-signicant (Table3, SupplementaryFig.14). us, in tropical forests high values of diversity and
biomass carbon are associated neither at the biome nor the continental scale; instead they vary independently. We
note that while in both South America and Africa there is sucient statistical power to detect even small eects of
diversity had they existed, in Asia power was only sucient to detect relatively large eect sizes (Table2).
Variable South America Africa Asia
Carbon (Mg ha
−1
) 140 (133–148)
A
183 (176–190)
B
197 (180–215)
B
Fisher’s α 80 (71–88)
B
28 (26–30)
A
84 (73–96)
B
Species richness (ha
−1
) 152 (141–163)
B
74 (70–78)
A
162 (147–177)
B
(300 stems
−1
) 109 (102–116)
B
65 (62–69)
A
120 (111–130)
B
Genus richness (ha
−1
) 91 (86–96)
B
59 (56–62)
A
87 (81–93)
B
(300 stems
−1
) 72 (68–75)
B
54 (51–56)
A
71 (66–75)
B
Family richness (ha
−1
) 38 (37–39)
B
28 (27–28)
A
40 (38–42)
B
(300 stems
−1
) 33 (32–34)
B
26 (25–27)
A
35 (34–37)
B
Table 2. Mean carbon stocks per unit area and tree diversity in forest inventory plots in South America
(n = 158), Africa (n = 162) and Asia (n = 40). 95% condence limits derived from 10,000 bootstrap resamples
of the data (sampling with replacement) are shown in parentheses. Dierent letters indicate signicant
dierences between continents (ANOVA and subsequent Tukey’s all-pair comparison, P < 0.05). Data for other
diversity metrics shown in SupplementaryTable2.
Figure 2. Decay in similarity (Sørensen index) of tree communities with distance in South
America (green), Africa (orange) and Asia (purple). Solid lines show tted relationships of the form
ln(similarity) = α + β × distance + ε . Estimated α and β parameters for each continent are given in
SupplementaryFig.S12, ε denotes binomial errors. Dierences in the α parameter indicate dierences in the
similarity of neighbouring stands, while dierences in the β parameter indicate dierences in the distance decay
of tree community similarity. Filled polygons show 95% condence intervals derived from 10000 bootstrap
resamples. Data underlying these relationships are shown in insets, with contours (0.05 and 0.25 quantiles)
overlain to show the density of points following kernel smoothing.