Environmental gradients and the evolution of
successional habitat specialization: a test case with 14
Neotropical forest sites
Susan G. Letcher1*, Jesse R. Lasky2,3, Robin L. Chazdon4, Natalia Norden5,6, S. Joseph
Wright7, Jorge A. Meave8, Eduardo A. P
erez-Garc
ıa8, Rodrigo Mu~noz8, Eunice Romero-
P
erez8, Ana Andrade9, Jos
e Luis Andrade10, Patricia Balvanera11, Justin M. Becknell12,
Tony V. Bentos9, Radika Bhaskar13, Frans Bongers14, Vanessa Boukili4, Pedro H. S.
Brancalion15, Ricardo G. C
esar15, Deborah A. Clark16, David B. Clark16, Dylan Craven17,
Alexander DeFrancesco4, Juan M. Dupuy10, Bryan Finegan18, Eugenio Gonz
alez-
Jim
enez19, Jefferson S. Hall7, Kyle E. Harms20, Jos
e Luis Hern
andez-Stefanoni10, Peter
Hietz21, Deborah Kennard22, Timothy J. Killeen23, Susan G. Laurance24, Edwin E. Lebrija-
Trejos7,8, Madelon Lohbeck11,14, Miguel Mart
ınez-Ramos11, Paulo E. S. Massoca9, Rita C.
G. Mesquita9, Francisco Mora11, Robert Muscarella3,25, Horacio Paz10, Fernando Pineda-
Garc
ıa26, Jennifer S. Powers27, Ruperto Quesada-Monge28, Ricardo R. Rodrigues29,
Manette E. Sandor4, Luc
ıa Sanaphre-Villanueva10, Elisabeth Sch€uller30, Nathan G.
Swenson31, Alejandra Tauro11, Mar
ıa Uriarte3, Michiel van Breugel7,32, Orlando Vargas-
Ram
ırez33, Ricardo A. G. Viani34, Amanda L. Wendt4 and G. Bruce Williamson9,20
1Department of Environmental Studies, Purchase College (SUNY), 735 Anderson Hill Road, Purchase, New York
10577, USA; 2Earth Institute, Columbia University, 405 Low Library, MC 4335, 535 West 116th Street, New York, NY
10027, USA; 3Department of Ecology, Evolution and Environmental Biology, Columbia University, 1200 Amsterdam
Ave., Manhattan, NY 10027, USA; 4Department of Ecology and Evolutionary Biology, University of Connecticut, 75 N.
Eagleville Road, Storrs, CT 06269-3043, USA; 5Fundaci
on Cedrela, Diagonal 40A # 18A - 09, Bogot
a, Colombia;
6Programa de Biolog
ıa, Facultad de Ciencias Naturales y Matem
aticas, Universidad del Rosario, Cra 24 # 63C-69,
Bogot
a, Colombia; 7Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Republic of Panama;
8Departamento de Ecolog
ıa y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Aut
onoma de M
exico,
Ciudad Universitaria, Circuito Exterior s/n, M
exico 04510 D.F., Mexico; 9Coordenac


~ao de Din^amica Ambiental,
Instituto Nacional de Pesquisas da Amaz^onia, Av. Andr
e Ara
ujo 1756, Petr
opolis, Manaus, Amazonas 69067-375,
Brazil; 10Unidad de Recursos Naturales, Centro de Investigaci
on Cient
ıfica de Yucat
an, Calle 43 No. 130, Col.
Chuburn
a de Hidalgo, C.P. 97200 M
erida, Yucat
an, M
exico; 11Instituto de Investigaciones en Ecosistemas y
Sustentabilidad, Universidad Nacional Aut
onoma de M
exico Unidad Morelia, Antigua Carretera a P
atzcuaro 8701
58190, Morelia, Michoac
an, M
exico; 12Department of Biology, University of Alabama, Box 870344, Tuscaloosa, AL
35487, USA; 13Environmental Change Initiative, Brown University, Box 1951, Providence, RI 02912, USA; 14Forest
Ecology and Forest Management, Wageningen University, 6708 PB Wageningen, The Netherlands; 15Departamento
de Ci^encias Florestais, Escola Superior de Agricultura Luiz de Queiroz, University of S~ao Paulo, Av. P
adua Dias, 11,
Piracicaba S~ao Paulo 13418-900, Brazil; 16Department of Biology, University of Missouri – St. Louis, 1 University Blvd,
St. Louis, MO 63121, USA; 17Synthesis Centre for Biodiversity Sciences, German Centre for Biodiversity Research,
Deutscher Platz 5e, 04103, Leipzig, Germany; 18Production and Conservation in Forests Programme, Centro
Agron
omico Tropical para Investigaci
on y Ense~nanza, 7170 Cartago, Turrialba 30501, Costa Rica; 19Soltis Center for
Research and Education in Costa Rica, Texas A&M University, San Isidro de Pe~nas Blancas, Apto. 80-4417, Fortuna,
San Carlos, Costa Rica; 20Department of Biological Sciences, Louisiana State University, 202 Life Science Bldg.,
Baton Rouge, LA 70803, USA; 21Institute of Botany, University of Natural Resources and Life Sciences, Gregor-
Mendel-Straße 33, 1180 Vienna, Austria; 22Department of Physical and Environmental Sciences, Colorado Mesa
University, 1100 North Ave, Grand Junction, CO 81501, USA; 23Museo de Historia Natural Noel Kempff Mercado, Av.
Irala No. 565, Santa Cruz, Bolivia; 24School of Marine and Tropical Biology, James Cook University, 1 James Cook
Dr, Townsville, Qld 4811, Australia; 25Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University,
8000 C Aarhus, Denmark; 26Escuela Nacional de Estudios Superiores, Unidad Morelia, Universidad Nacional
Aut
onoma de M
exico, Antigua Carretera a P
atzcuaro 8701, 58190, Morelia, Michoac
an, Mexico; 27Department of
*Correspondence author: E-mail: susan.letcher@purchase.edu
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society
Journal of Ecology doi: 10.1111/1365-2745.12435
Ecology, Evolution, and Behavior and Department of Plant Biology, University of Minnesota, 100 Ecology Building,
1987 Upper Buford Circle, Saint Paul, MN 55108, USA; 28Escuela de Ingenier
ıa Forestal, Instituto Tecnol
ogico de
Costa Rica, Apto. 159-7050, Cartago, Costa Rica; 29Department of Biological Sciences, ESALQ, University of S~ao
Paulo, P
adua Dias Avenue, 11, Piracicaba, S~ao Paulo 13418-900, Brazil; 30Division of Viticulture and Pomology,
University of Natural Resources and Life Sciences, Gregor-Mendel-Straße 33, 1180, Vienna, Austria; 31Department of
Plant Biology, Michigan State University, 612 Wilson Road, East Lansing, MI 48824, USA; 32Yale-NUS College,
National University of Singapore, 6 College Ave. E, 138614 Singapore, Singapore; 33Organization for Tropical Studies,
La Selva Biological Station, Apto. 676-2050, San Pedro de Montes de Oca, Costa Rica; and 34Departamento de
Biotecnologia e Produc


~ao Vegetal e Animal, Universidade Federal de S~ao Carlos, Rod. Anhanguera, Km 174, CEP
13600-970, Araras, S~ao Paulo, Brazil
Summary
1. Successional gradients are ubiquitous in nature, yet few studies have systematically examined the
evolutionary origins of taxa that specialize at different successional stages. Here we quantify succes-
sional habitat specialization in Neotropical forest trees and evaluate its evolutionary lability along a
precipitation gradient. Theoretically, successional habitat specialization should be more evolution-
arily conserved in wet forests than in dry forests due to more extreme microenvironmental differenti-
ation between early and late-successional stages in wet forest.
2. We applied a robust multinomial classification model to samples of primary and secondary forest
trees from 14 Neotropical lowland forest sites spanning a precipitation gradient from 788 to
4000 mm annual rainfall, identifying species that are old-growth specialists and secondary forest
specialists in each site. We constructed phylogenies for the classified taxa at each site and for the
entire set of classified taxa and tested whether successional habitat specialization is phylogenetically
conserved. We further investigated differences in the functional traits of species specializing in sec-
ondary vs. old-growth forest along the precipitation gradient, expecting different trait associations
with secondary forest specialists in wet vs. dry forests since water availability is more limiting in
dry forests and light availability more limiting in wet forests.
3. Successional habitat specialization is non-randomly distributed in the angiosperm phylogeny, with
a tendency towards phylogenetic conservatism overall and a trend towards stronger conservatism in
wet forests than in dry forests. However, the specialists come from all the major branches of the
angiosperm phylogeny, and very few functional traits showed any consistent relationships with suc-
cessional habitat specialization in either wet or dry forests.
4. Synthesis. The niche conservatism evident in the habitat specialization of Neotropical trees sug-
gests a role for radiation into different successional habitats in the evolution of species-rich genera,
though the diversity of functional traits that lead to success in different successional habitats compli-
cates analyses at the community scale. Examining the distribution of particular lineages with respect
to successional gradients may provide more insight into the role of successional habitat specializa-
tion in the evolution of species-rich taxa.
Key-words: determinants of plant community diversity and structure, functional traits, life-history
evolution, phylogeny, pioneer species, precipitation gradient, tropical dry forest, tropical wet forest
Introduction
Environmental gradients provide opportunities for understand-
ing the evolution of habitat specialization (Grime 1977; Grubb
1977). Many of the gradients observed in ecosystems are asso-
ciated with the process of succession, as communities reassem-
ble after disturbances (Pickett 1976; Sousa 1984). Successional
habitat specialization – the radiation of lineages to take advan-
tage of particular points on this successional gradient – is
potentially a strong driver of plant evolution (van Steenis 1958;
G
omez-Pompa 1971; Pickett 1976). Grubb (1977) reiterated
the importance of the regeneration niche in community assem-
bly, and many studies have explored the potential of distur-
bance for maintaining diversity (Sousa 1984; Ricklefs 1987;
Tilman & Pacala 1993), yet relatively few studies have exam-
ined successional gradients as an evolutionary force.
Neotropical forests are a fascinating laboratory for studying
plant evolution due to their floristic diversity and environmen-
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
2 S. G. Letcher et al.
tal heterogeneity (Gentry 1988). The specialization of tropical
trees into successional habitats has long been recognized.
Pioneer tree species – the species that establish and dominate
early in succession – form an important component of the trop-
ical forest flora, particularly in wet forests (van Steenis 1958;
G
omez-Pompa 1971; Whitmore 1989). G
omez-Pompa (1971)
proposed that the evolution of many tropical plant taxa could
only be understood through the lens of secondary forest succes-
sion. He postulated that species richness and taxonomic diver-
sification in the tropics could be explained, at least partially, by
strong selection for growth and survival in early successional
habitats following large-scale disturbances. Pioneer species
evolved to take advantage of natural disturbances such as fires,
floods, landslides, cyclones and volcanic eruptions, and they
have proliferated in recent millennia with the expansion of
anthropogenic disturbances (Whitmore 1989; Chazdon 2003,
2014). Yet the exact definition of pioneer species has remained
somewhat ambiguous (Swaine & Whitmore 1988; Gitay &
Noble 1997), and relatively few studies have examined the
evolutionary origins of the taxa that dominate early succes-
sional forests vs. the taxa that are more abundant in undis-
turbed forest (Gentry 1988; Dalling, Hubbell & Silvera 1998).
The massive expansion of available molecular data in
recent decades (Wikstr€om, Savolainen & Chase 2001; Ma-
gall
on & Castillo 2009; Stevens 2014) gives us unprecedented
power to examine the distribution of traits on the angiosperm
phylogeny and test hypotheses about their evolutionary ori-
gins. Like extinction risk (Fritz & Purvis 2010), successional
habitat specialization is a complex emergent characteristic of
a species, stemming from the interaction of many traits and
the environment. Here, we develop a new conceptual model
of how the strength of environmental gradients during succes-
sion affects the evolution of successional habitat specializa-
tion. We test the model by evaluating the phylogenetic
conservatism of habitat specialization in tropical forest trees
across a precipitation gradient.
Successional processes involve changes in resource avail-
ability and abiotic conditions (Fig. 1). The magnitude of these
changes depends on the intensity of the disturbance that
begins the succession (Sousa 1984) and also on the similarity
between the starting point and the endpoint in succession
(Pickett 1976). In ecosystems where the early successional
and late-successional stages are structurally similar (e.g. grass-
lands, shrublands and open forests), these resource/condition
gradients are less strong than in ecosystems where succes-
sional stages differ greatly in structure (e.g. tropical wet for-
ests). Changes in conditions and resource availability during
succession mediate the success of individuals that arrive at a
site. When successional gradients are weak, the valley
between adaptive peaks on the fitness landscape is shallow:
being a specialist at one end of the successional gradient
involves little reduction in fitness at the other end of the gra-
dient (Fig. 1). When successional gradients are strong,
though, being a specialist at one end of the gradient has a
considerable fitness cost at the other end of the gradient.
Thus, we expect to see fewer independent origins of succes-
sional habitat specialization where gradients are strong.
Instead, we expect to see conservatism of habitat specializa-
tion; specialist lineages consist of close relatives that radiated
within that habitat rather than crossing the fitness barrier
between habitats (Fig. 1).
Neotropical forest succession involves numerous environ-
mental gradients. As succession proceeds, light availability
decreases, temperatures (and diel temperature fluctuations)
decrease, and relative humidity increases (Bazzaz & Pickett
1980; Swaine & Whitmore 1988; Finegan 1996; Guariguata
& Ostertag 2001; Ruiz, Fandi~no & Chazdon 2005). These
environmental gradients are present in every forest during
succession, but the relative strength of the gradients depends
on the similarity in abiotic conditions between early succes-
sional and late-successional habitats, which in turn is a func-
tion of habitat structure. Precipitation is a major driver of
structural differences among Neotropical forests. Tropical dry
forests have lower stature and greater canopy openness than
wet forests (Ewel 1977; Holbrook, Whitbeck & Mooney
1995). In dry forests, light conditions differ less strongly
between early successional and late-successional habitats
(Holbrook, Whitbeck & Mooney 1995; Lebrija-Trejos et al.
2011). In mature wet forests, generally 1–2% of the incident
sunlight reaches the forest floor except in canopy gaps (Chaz-
don & Fetcher 1984). In dry forests, 5–10% of the incident
sunlight reaches the forest floor in the wet season, with higher
values during the dry season when many species are leafless
(Coomes & Grubb 2000; Poorter 2009). Canopy closure in
dry forests during succession is slower than in wet forests
(Ewel 1977; Ruiz, Fandi~no & Chazdon 2005; Letcher &
Chazdon 2009; Lebrija-Trejos et al. 2010), potentially leaving
a longer window of time for pioneer species to colonize in
dry forests. The microclimatic stressors that plant seedlings
face are markedly different between open areas and forest un-
derstorey in wet forests and less distinct in dry forests (Ewel
1977). The precipitation gradient in Neotropical forests there-
fore produces a gradient in the environmental dissimilarity of
early successional and late-successional habitats.
The ability to thrive in particular successional habitats is a
composite trait resulting from many physiological, anatomical
and phenological adaptations (Poorter & Markesteijn 2008;
Lebrija-Trejos et al. 2010), many of which are likely to be phy-
logenetically conserved (Prinzing 2001; Losos 2008). Based on
our model of the evolution of successional habitat specializa-
tion (Fig. 1), we expect to observe different patterns of succes-
sional habitat specialization in wet and dry forests due to the
depth of the ‘valley’ between adaptive peaks in the fitness land-
scape (Fig. 1). Specifically, we predict that there will be lower
levels of successional habitat specialization in dry forest floras,
with specialists in each group (SG and OG) originating from
many lineages and not necessarily being closely related. In wet
forests, by contrast, where the adaptive peaks are separated by a
deeper ‘valley’, we predict that there will be fewer origins of
the specialist strategies, but that the lineages that do evolve into
specialists will undergo adaptive radiations. Thus, we predict
that the successional habitat specialists in wet forest will be clo-
sely related and that habitat specialization is more likely to be a
phylogenetically conserved trait in wetter forests.
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
Successional gradients shape plant evolution 3
Because the mechanisms driving habitat specialization in
wet and dry forests are likely to depend on plant functional
traits rather than species phylogenetic relatedness per se, we
predict that the functional traits characterizing the early suc-
cessional specialist species will differ between wet and dry
forests (Lohbeck et al. 2013). Early successional habitats in
wet forests are presumed to favour tree species with traits
associated with fast growth and cheap construction costs:
rapid height growth rates, high rates of photosynthesis, high
leaf nutrient concentration, thin leaves – high SLA, small eas-
ily dispersed seeds and low density wood (Bazzaz & Pickett
1980; Guariguata & Ostertag 2001; Poorter et al. 2004; Poor-
ter & Rose 2005). In dry forests, the most important early
successional traits are likely to be related to desiccation toler-
ance; thus, dry forest SG specialists should have lower
growth rates and photosynthetic rates, lower SLA and higher
wood density than wet forest SG specialists (Bazzaz & Pick-
ett 1980; Poorter 2009; Lebrija-Trejos et al. 2010).
Analysis of functional trait variation among specialist
groups will provide critical insights into the selective pres-
sures that may have led to the evolution of successional spe-
cialization of trees in tropical forest regions. This synthesis
paper is the first to test the hypothesis of phylogenetic conser-
vatism of successional habitat affinities of plant lineages, with
a robust comparison among fourteen regions spanning wet
and seasonally dry forests in the tropical Americas. The data
sets we analyse form an unprecedented base of information
for understanding habitat specialization in relation to phylog-
eny and functional traits in Neotropical forests.
Materials and methods
Based on existing woody vegetation surveys in a range of wet and
seasonally dry Neotropical lowland forests, we examined the phyloge-
netic distribution and functional traits of trees that are specialists in
successional vs. mature forest. We tested whether patterns in succes-
sional habitat specialization and trait conservatism differ across forest
regions along a rainfall gradient. We identified 14 sites across the
Neotropics (Fig. 2), all in lowland forests (< 1000 m asl), represent-
ing a precipitation gradient from 788 to 4000 mm per year. We
selected sites with extensive sampling of both old-growth and second-
ary forest with good species-level identification of at least 75% of
stems. For 12 of the 14 sites, functional trait data are available for a
large fraction of the species present (Table 1). Many of these sites
include ongoing monitoring projects. When using data from moni-
tored sites, we selected a single year of data to focus on, generally
the most recent year of data available with full taxonomic resolution
(Table 1). In 12 of the 14 sites, the samples are spread over a fairly
small geographic range (Table S1), but larger regional sampling areas
were necessary in the Atlantic forest sites (FES and FOD) due to the
heavily fragmented nature of the remaining forest patches in that
landscape (Rodrigues et al. 1989; Rodrigues 1999).
The data incorporated in this paper include columnar cacti, palms
and woody trees; we omitted lianas, understorey shrubs and other
growth forms. At all sites, trees were sampled using plot-based meth-
ods (see Table S1 and references cited in Table 1 for details), and
Re
la
tiv
e 
fit
ne
ss
Succession 
SG specialists
OG specialists
Succession 
Weaker successional gradients              Stronger successional gradients
Pa
tte
rn
s o
f e
vo
lu
tio
n 
in
ha
bi
ta
t s
pe
cia
liz
at
io
n
High lability of SG specialization;  
multiple origins and low conservatism 
of specialization since fitness peaks 
for SG and OG habitats are not 
strongly separated
Low lability of SG specialization; few  
origins and high conservatism of 
specialization since fitness peaks for 
SG and OG habitats are far apart
Increasing dissimilarity of early and late successional habitats
Increasing conservatism of successional habitat specialization
= SG specialist
= OG specialist
= SG specialist
= OG specialist
Fig. 1. Resource availability and abiotic
conditions change during succession. The
species that are prevalent early in succession
contend with different stressors and take
advantage of different resources than later
successional species. In successional systems
where the endpoint is structurally similar to
the starting point, these resource/condition
gradients are weak, and where the endpoint is
structurally distinct, the gradients are strong.
Species that are adapted to take advantage of
early successional environments (second-
growth specialists, indicated here as ‘SG
specialist’) are less successful in late-
successional conditions, producing a ‘valley’
between adaptive peaks on the fitness
landscape. This trade-off is particularly
pronounced when successional gradients are
strong. Thus, assuming that the traits
underlying successional habitat specialization
are conserved on the angiosperm phylogeny,
we expect to see fewer origins and greater
conservatism of SG specialization in
successional systems with stronger
environmental gradients. Old-growth
specialization should follow a similar pattern.
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
4 S. G. Letcher et al.
unknown individuals were collected for identification using local and
international herbarium collections. Trees at each site that could be
identified as belonging to a distinct genus but could not be matched
to described species were given internally consistent morphospecies
names at that site (e.g. Dussia sp. A_SAR). Individuals that were
not identified below the family level were omitted from analysis. We
also limited the taxonomic scope of our study to angiosperms, omit-
ting tree ferns and gymnosperms, which contributed a negligible
amount of biomass in these sites. At all sites, multiple-stemmed indi-
viduals were only counted once. For nine sites, we include all indi-
viduals ≥ 5 cm diameter at 1.3 m height (DBH), and for two sites
(SAR and GUA), we include individuals ≥ 10 cm DBH. At FES
and FOD, we include all individuals ≥ 4.8 cm DBH since this was
the diameter threshold used in sampling. At 13 out of 14 sites, we
were able to use the same size classes in old-growth and secondary
forest, but at BRA, we include stems ≥ 5 cm DBH in secondary for-
ests and stems ≥ 10 cm DBH in old-growth forests due to con-
straints of data availability. If anything, the size class sampling
discrepancy at BRA will detect a greater abundance of old-growth-
adapted species in the second-growth forest at that wet forest site,
since the tree species that are characteristic of old growth are often
present in small size classes in the undergrowth of older secondary
forests (Chazdon 2008). Since the sites where we used different stem
size class criteria span the precipitation gradient (Table 1), we can
be fairly confident that any effect of size class sampling is not con-
founded with the effect of precipitation.
We compiled a species list from all 14 sites and checked synon-
ymy using http://www.tropicos.org (Missouri Botanical Garden
2014). We applied the multinomial model of Chazdon et al. (2011)
to classify the trees at each site according to their successional habi-
tat affiliations. Given species abundances in two different habitats,
the model classifies species into habitat 1 specialists, habitat 2 spe-
cialists, generalists or too rare to classify with confidence. One major
advantage of this method over prior classification algorithms is the
ability to make a statistical determination of whether a species is
common enough to be classified with confidence (Chazdon et al.
2011). Another advantage of this classification algorithm is that it
explicitly corrects for differences in sampling intensity across habi-
tats, thus avoiding the bias inherent in other classification methods
when incomplete samples are used (Chazdon et al. 2011; Parker
2013; Chao et al. 2015). Particularly in species-rich assemblages, the
observed relative abundance of species i in a given sample (pi) is a
biased estimator of its actual relative abundance, due to the contribu-
tion of undetected species to the denominator of the actual relative
abundance (Chazdon et al. 2011; Chao et al. 2015). This bias is
more severe for rare species than for common species (Chao et al.
2015). The multinomial classification algorithm of Chazdon et al.
(2011) uses Turing-Good coverage estimation (Good 1953; Chazdon
et al. 2011 Appendix B) to correct pi for the contribution of unde-
tected species to the assemblage.
In this study, we used secondary forest and nearby old-growth for-
est as the two types of habitats. Species in each region were divided
into second-growth (SG) specialists, old-growth (OG) specialists, gen-
eralists and too rare to classify. We used the settings recommended
for the model given the structure of our data set (Chazdon et al.
2011): a simple majority threshold (K = 1/2) since sample sizes of
individuals at each site are large (Table 2) and P = 0.005 to correct
for the family-wise error rate since each site had a large number of
species to be classified. In all of our further analyses, we used only
the species that were abundant enough to be classified with confi-
dence in at least one site.
Phylogenies were constructed using the method of Letcher et al.
(2012) with updated information on node ages. The species list was
mapped onto a maximally resolved supertree of angiosperms
(R20120829) using Phylomatic version 3 (http://phylodiversity.net/
phylomatic/; Webb & Donoghue 2005). Branchlengths were assigned
using the bladj algorithm in Phylocom 4.2.1 (Webb, Ackerly &
Kembel 2008). Node ages were based on Wikstr€om, Savolainen &
Chase (2001), employing the corrections for node names in Phylomat-
ic trees identified by Gastauer & Meira-Neto (2013) and updated node
ages from Magall
on & Castillo (2009) where available. We con-
structed one supertree of all classifiable taxa (SG specialists, OG spe-
cialists and generalists), applied the branchlength corrections and then
used Phylomatic (Webb & Donoghue 2005) to take subsets of this
tree for the taxa at each site.
We examined the phylogenetic signal in species’ habitat special-
izations using the D statistic (Fritz & Purvis 2010), a metric of phy-
logenetic conservatism that is robust to differences in phylogenetic
tree size and the prevalence of the trait being examined. D assesses
the phylogenetic signal of a binary trait. It was first used to investi-
gate the relative strength of phylogenetic signal in extinction risk
caused by different types of environmental threats (Fritz & Purvis
2010). Like extinction risk, successional habitat specialization is a
complex emergent trait that is related to many underlying characters.
One feature that makes D particularly useful for our analysis is that
it is specifically designed to investigate the relative strength of phy-
logenetic signal in a binary trait among different groups, with pro-
gressively lower values indicating a higher level of phylogenetic
signal (Fritz & Purvis 2010). Values below zero indicate extreme
clustering, zero indicates clustering (Brownian expectation), one indi-
cates randomness, and values above one indicate overdispersion. The
method gives two P values, one from a test whether the trait is dis-
tributed randomly on the phylogeny and one from a test whether the
trait is clumped as would be expected from Brownian motion des-
cent with modification (Fritz & Purvis 2010). Most measures of
phylogenetic signal focus on assessing the significance, rather than
the relative strength, of the phylogenetic signal in binary traits, but
since D is comparable across data sets, it can be used to assess
whether particular features of the groups examined – or particular
environmental features – are linked to greater phylogenetic signal
strength (Fritz & Purvis 2010). In our analysis, we test whether
there is a relationship between average annual rainfall and the
Fig. 2. The 14 sites span a wide range of locations in the Neotropical
lowlands.
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
Successional gradients shape plant evolution 5
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© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
6 S. G. Letcher et al.
strength of phylogenetic signal in successional habitat specialization.
Based on the predictions in Fig. 1, we would expect to see lower
values of D (i.e. greater phylogenetic signal strength) in sites with
higher rainfall.
In order to examine the relative strength of the phylogenetic signal
in our data, we analysed D for the whole data set using the tree of all
classifiable taxa, and for each site using the site-specific phylogeny.
Because D is only applicable to binary traits, we conducted two dif-
ferent tests in each case: one for SG specialists vs. [OG special-
ists + generalists] and one for OG specialists vs. [SG
specialists + generalists]. We used the phylo.d function in the pack-
age caper (Orme et al. 2013) to calculate D, using R 2.13.1 (R
Development Core Team 2013).
We examined a wide range of functional traits related to seeds,
leaves, wood density and plant stature (Table 3). Not every trait was
available for every species; for instance, we did not measure wood or
leaf traits for cacti. The number of taxa included for each trait at each
site is given in Table 3. For two sites (SAR and OSA), seed length
was reported as categorical values (seed length: < 1, 1–6, 6–15,
15–30, 30–50, 50–100 and > 100 mm). Tree height at OSA was a
categorical variable (canopy or subcanopy). All other traits were
reported as continuous variables: seed dry mass (mg), seed length
(mm), specific leaf area (SLA; cm2 g
1), leaf dry matter content
(LDMC; mg g
1), wood specific gravity (WSG; unitless) or wood
density (g cm
1), and maximum height (m). Leaf and seed traits were
collected according to standard protocols (Cornelissen et al. 2003b).
Wood density was reported at NIZ, CHM, FES and FOD, while
WSG was reported at KIU, GUA, CAR, PAN, LUQ and SAR.
Because of differences in measurement protocols including core diam-
eter and length, drying temperature, and whether corrections were
made for radial variation, WD and WSG measurements are not
directly comparable among sites (Williamson & Wiemann 2010).
To examine the associations of particular functional traits with suc-
cessional habitat specialization, we used functions developed by Pur-
vis & Rambaut (1995) that calculate phylogenetically independent
contrasts for a set of variables and then test for evolutionary relation-
ships in the contrasts using linear models. We used the functions
crunch (for continuous data) and brunch (for categorical data) in
caper (Orme et al. 2013) to model the relationship between habitat
specialization, coded as a binary variable and each of the functional
traits measured. Again, we did two sets of tests for each site–trait
combination, one using SG specialists vs. [OG specialists + general-
ists] and one using OG specialists vs. [SG specialists + generalists].
Results
Our 14 sites contained a total of 162 532 individuals and
2654 species, from 87 families and 28 orders (higher taxa
following APG III, Stevens 2014). Three hundred and ele-
ven of these species, accounting for 1306 individuals
(0.8%), were morphospecies; the rest were identified at the
species level and linked to a valid name. The number of
species present at each site ranged from 62 in the dry forest
at CAR to 743 in the moist forest at BRA (Table 2). Across
the whole data set, 921 species (34.7%) were common
enough to be classified with confidence using the multino-
mial model (Chazdon et al. 2011) in at least one site. Of
these, 355 species were SG specialists, 52 of which were
classified as SG specialists at multiple sites. A total of 443
species were classified as OG specialists, 56 at multiple
sites. Only, 10 species had conflicting classifications at dif-
ferent sites. The algorithm classified between 12.5 and
72.5% of species at each site with confidence (Table 2).
However, since the species that are classified with confi-
dence are the common species (Chazdon et al. 2011), these
relatively low percentages of species made up large percent-
ages of the individuals at each site; > 60% in every site,
with a median of 90.0% (Table 2).
Although the classification algorithm can be sensitive to the
age of the secondary forests sampled (Chazdon et al. 2011),
we did not find evidence of such sensitivity in this data set:
there was no relationship between the median secondary for-
est age and the per cent of individuals classified as secondary
forest specialists (R2 = 0.033, P = 0.56). There was also no
relationship between median secondary forest age and rainfall
(R2 = 0.093, P = 0.33; Table 1), suggesting that these vari-
ables are not confounded in our data set.
Table 2. The number of species and individuals at each site, and the per cent of species and individuals that were classified with confidence
using the multinomial model of Chazdon et al. (2011). The remaining species at each site were classified as too rare for reliable estimates of hab-
itat specialization
Site No. spp.
No. individuals
in secondary
forest
No. individuals
in old-growth
forest
% of spp. classified
with confidence
% of individuals
classified with
confidence
No. secondary
forest specialist
spp.
No. old-growth
specialist spp.
No. generalist
spp.
CHM 91 496 533 29.7 81.3 10 5 12
NIZ 110 629 892 30.9 87.2 9 21 4
KIU 137 8712 686 31.4 93.0 10 7 26
CHI 115 1269 7947 43.5 96.2 16 25 3
GUA 168 3587 1027 39.3 89.5 23 22 21
FES 350 15 817 1556 30.3 90.5 40 33 32
CAR 62 1043 720 46.8 95.3 11 8 9
FOD 378 12 766 835 28.8 84.3 31 61 16
BRA 743 2010 2272 12.5 63.0 28 51 10
PAN 349 12 849 47 814 72.5 99.3 93 129 31
CHJ 149 2403 257 16.1 75.3 11 11 2
LUQ 133 5383 8161 54.1 98.4 29 28 14
SAR 507 5921 7768 29.6 87.7 38 78 38
OSA 359 3420 7273 42.9 94.7 57 49 43
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
Successional gradients shape plant evolution 7
Table 3. Results from the brunch/crunch analysis (Purvis & Rambaut 1995) of the relationship between successional habitat specialization and
functional traits. ‘SG’ stands for second-growth specialization; ‘OG’ is old-growth specialization (shaded rows). N is the number of species that
were classifiable with the multinomial model (Chazdon et al. 2011) at each site and habitat category for which the trait was measured. For seed
length and maximum height, a dagger (†) indicates that the trait was measured as categorical. For wood traits, a double dagger (††) indicates that
wood density was measured instead of wood specific gravity. See text for details. Functional trait data were available for all sites except CHI and
BRA. R is the brunch/crunch-corrected correlation coefficient between habitat specialization and trait values; for instance, the negative coefficient
for seed length at CHM indicates that SG specialists had lower seed size than generalists/OG specialists at that site
Site
Forest
age
Seed
mass (mg)
Seed length (mm)
(† = categorical) SLA
LDMC
(mg g
1)
Max. height (m)
(† = categorical)
WSG
(†† = WD)
No.
species
No. valid
contrasts
CHM SG N = 24
NS
N = 22
R = 
 0.57
P = 0.008
N = 26
NS
– N = 27
R = 
 0.44
P = 0.008
††N = 26
NS
27 19
OG N = 20
NS
N = 19
NS
N = 20
NS
– N = 21
NS
N = 20
R = 0.316
P = 0.05
21 19
NIZ SG – – N = 20
NS
– N = 20
NS
††N = 20
NS
26 19
OG – – N = 16
NS
– N = 16
NS
††N = 16
NS
29 19
KIU SG – N = 25
R = 
 0.41
P = 0.048
N = 31
NS
N = 31
NS
– N = 35
NS
43 22
OG – N = 24
NS
N = 30
NS
N = 30
NS
– N = 35
NS
42 22
GUA SG – – N = 43
NS
– – N = 43
NS
61 40
OG – – N = 35
NS
– – N = 35
NS
52 40
FES SG – – N = 52
NS
N = 15
NS
N = 74
NS
††N = 100
NS
117 54
OG – – N = 62
R = 
 0.51
P = 0.012
N = 22
NS
N = 52
R = 0.35
P = 0.052
††N = 138
NS
306 54
CAR SG N = 22
NS
– N = 23
NS
– N = 22
R = 
 0.70
P = 0.001
N = 23
NS
25 21
OG N = 22
NS
– N = 23
NS
– N = 22
NS
N = 23
NS
25 21
FOD SG – – N = 24
NS
– N = 52
R = 
 0.56
P = 0.03
††N = 93
NS
128 35
OG N = 14
R = 
 0.74
P = 0.01
– N = 27
NS
†† N = 51
R = 0.40
P = 0.015
324 35
PAN SG N = 125
NS
– N = 195
NS
N = 195
NS
N = 150
R = 
 0.25
P = 0.032
N = 191
R = 
 0.34
P = 0.003
200 72
OG N = 164
NS
– N = 202
NS
N = 202
NS
N = 201
NS
N = 201
NS
207 72
CHJ SG N = 14
NS
N = 14
NS
N = 12
NS
N = 14
NS
N = 14
NS
N = 14
R = 
 0.62
P = 0.031
17 11
OG N = 12
NS
N = 12
R = 0.70
P = 0.011
N = 10
NS
N = 12
NS
N = 14
R = 0.58
P = 0.046
N = 12
R = 0.89
P < 0.0001
17 11
LUQ SG N = 49
NS
– N = 50
NS
– N = 50
NS
N = 50
NS
63 41
OG N = 48
NS
– N = 48
NS
– N = 47
R = 
 0.49
P < 0.0001
N = 48
NS
60 41
SAR SG – †N = 125
R = 
 0.31
P = 0.0038
N = 125
R = 
 0.25
P = 0.028
N = 125
R = 0.28
P = 0.013
N = 125
R = 
 0.23
P = 0.048
N = 125
NS
125 82
(continued)
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
8 S. G. Letcher et al.
Successional habitat specialization in our data set was
significantly non-random, with D values lower than 1,
suggesting a tendency towards phylogenetic clustering (SG
specialization: D = 0.7641, Prandom < 0.0001, PBrownian <
0.0001; OG specialization: D = 0.7159, Prandom < 0.0001,
PBrownian < 0.0001). For SG specialization, values of D for
individual sites fell between 0.158 (clustering) and 1.11 (slight
overdispersion). There was a trend towards lower D values (i.e.
stronger phylogenetic signal) in the wetter forest sites (Fig. 3a),
although the correlation between D values and precipitation
was weak and not significant (R2 = 0.212, P = 0.09). For OG
specialization, values ranged from 
0.35 to 1.48. There was
also a tendency towards lower D values in wetter forest sites
(Fig. 3b) except for the dry forest at NIZ, which had the lowest
value of all. The relationship here was not significant
(R2 = 0.065, P = 0.37). There were also suggestive trends in
the proportion of stems that were specialists along the precipita-
tion gradient: wetter sites had a higher proportion of individuals
classified as specialists (Fig. 4), although again the correlations
were weak and not significant (R2 = 0.243, P = 0.105 for SG
specialists, R2 = 0.095, P = 0.282 for OG specialists).
Considering the relationship between functional traits and
secondary habitat specialization, relatively few traits showed a
strong relationship with habitat specialization when phyloge-
netically independent contrasts were taken into account
(Table 3). Seed length (measured in five sites) showed signifi-
cant relationships with SG specialization in three sites and
OG specialization in one site. The slope of all the significant
relationships was negative in SG and positive in OG, indicat-
ing that seed length tends to be shorter in SG specialists and
longer in OG specialists (Table 3). However, seed mass (mea-
sured in five sites) showed no significant relationship with
habitat specialization in any site (Table 3). Leaf traits (SLA
and LDMC) also showed relatively few significant relation-
ships with habitat specialization. SLA (measured in 11 sites)
was negatively related to OG specialization at two sites and
positively related to SG specialization in one site; LDMC
(measured in five sites) was positively related to SG special-
ization in only one site. Maximum height (measured in 10
sites) had a significant negative relationship to SG specializa-
tion in three sites and a significant positive relationship with
OG specialization in two sites, indicating that maximum
height is lower in SG specialists, but there was also a signifi-
cant negative relationship between OG specialization and
maximum height in one site (LUQ). Wood specific gravity/
wood density (measured in 11 sites; see Table 3) had a signif-
icant negative relationship with SG specialization in four sites
and a significant positive relationship with OG specialization
in two sites, indicating denser wood in OG trees.
Examining the distribution of secondary forest specialist
taxa across the angiosperm phylogeny (Fig. 5), clustering of
successional habitat specialization is evident in some lineages.
Certain families have a large representation of SG specialists,
for example Melastomataceae and Myrtaceae (Fig. 5). Other
families have a greater proportion of OG specialists, for
example Sapotaceae and Primulaceae (Fig. 5). In some cases,
closely related groups of taxa differed in the prevalence of
SG vs. OG specialists; see, for instance, Salicaceae and Viola-
ceae; Clusiaceae and Hypericaceae; Moraceae and Urticaceae;
Burseraceae and Anacardiaceae (Fig. 5).
Despite overall conservatism of successional habitat spe-
cialization, there have been multiple origins of SG and OG
specialists in a wide range of lineages. Successional habitat
specialization occurs in every major clade of angiosperms
(Table 4). The Rosid I and II clades contain a large number
of SG specialists, but the flora of this region as a whole is
Rosid-dominated (Gentry 1988; Chave et al. 2006; Fig. 5;
Table 4). Many clades contain both generalist and specialist
taxa without clear patterns of clustering; for instance, Faba-
ceae, Rubiaceae and the Magnoliids (Fig. 5).
Discussion
This is the first study, to our knowledge, to demonstrate that
successional habitat specialization tends to be conserved
among angiosperms, with significantly non-random patterns
of successional habitat specialization evident in a broad sam-
ple of the angiosperm phylogeny from a diverse range of
Neotropical forest sites. In a previous study of Neotropical
trees, Norden et al. (2012) found that the SG specialists and
OG specialists at SAR are both significantly clustered on the
angiosperm phylogeny; here, we extended the scope of the
study and confirmed that there is phylogenetic clustering of
successional habitat specialists in numerous sites across a
broad geographic area.
Our data also reveal a weak association between the degree
of clustering in the secondary forest specialist habit and the
amount of rainfall at a site (Fig. 3a). Although the trend is
Table 3. (continued)
Site
Forest
age
Seed
mass (mg)
Seed length (mm)
(† = categorical) SLA
LDMC
(mg g
1)
Max. height (m)
(† = categorical)
WSG
(†† = WD)
No.
species
No. valid
contrasts
OG – N = 133
NS
N = 146
NS
N = 133
NS
N = 146
NS
N = 146
R = 0.32
P = 0.005
146 84
OSA SG – †N = 129
NS
– – †N = 130
NS
– 141 84
OG – †N = 107
NS
– – †N = 109
NS
– 118 84
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
Successional gradients shape plant evolution 9
not significant, the direction of the pattern generally agrees
with our predictions about the evolution of successional habi-
tat specialization along a gradient of dissimilarity between
early successional and late-successional habitats (Fig. 1).
There is a trend towards greater conservatism of successional
habitat specialization in wetter forests, suggesting that special-
ization may be less evolutionarily labile in these systems. In
addition, when we consider the abundance of individuals, spe-
cialists tend to make up a larger fraction of the individuals in
wet regions than in dry regions – more SG specialists in sec-
ondary forests and more OG specialists in old-growth forests
(Fig. 4). These trends are much more evident in SG special-
ists than OG specialists, perhaps due to the strong constraints
on the life-history strategies of pioneer species and the
ephemeral nature of their habitat.
Based on the differences in the abiotic environment of wet
and dry forests during succession, we predicted that func-
tional trait profiles would differ between wet and dry forest
SG specialists, with wet forest pioneers exhibiting traits for
rapid growth and light gathering while dry forest pioneers
should exhibit traits related to desiccation tolerance. Lohbeck
et al. (2013) reported contrasting successional changes in the
community-weighted means of functional traits in wet and
dry forests in Mexico, with traits indicating the importance of
light availability driving successional changes in wet forests
and water availability driving changes in dry forests. In our
broader data set, however, we identified relatively few traits
that were strongly and consistently associated with succes-
sional habitat specialization (Table 3).
The lack of strong relationships that we observed between
functional traits and successional habitat specialization is
somewhat surprising, given the directional changes in plant
functional traits that are generally observed during tropical
forest succession (Bazzaz & Pickett 1980; Finegan 1996;
Guariguata & Ostertag 2001; Chazdon et al. 2003; Lebrija-
Trejos et al. 2010; Dupuy et al. 2012; Lohbeck et al. 2013;
Chazdon 2014; Bhaskar, Dawson & Balvanera 2014). One
potential explanation is the methodological differences
between our study and previous work. Studies that use com-
munity-weighted means are strongly influenced by the most
abundant species in a community. The community-weighted
mean approach is useful for understanding the synecology of
co-occurring species (sensu Odum & Odum 1959), but less
useful for understanding the autoecology of each species. The
brunch/crunch algorithms treat each species as a data point,
rather than each individual (Purvis & Rambaut 1995). These
algorithms also explicitly correct for the lack of phylogenetic
independence among data points. For highly phylogenetically
conserved traits, comparisons that do not take phylogenetic
non-independence into account may overestimate the magni-
tude and significance of differences among groups (Felsen-
stein 1985).
1000 2000 3000 4000
–0
.5
0.
0
0.
5
1.
0
1.
5
mean annual rainfall (mm)
D
 s
ta
tis
tic
 fo
r s
ec
on
da
ry
 fo
re
st
 s
pe
ci
al
iz
at
io
n
OSA
SAR
LUQ
CHJ
PAN
BRA
CAR
GUA
KIU
CHI
NIZ
CHM
FES FOD
1000 2000 3000 4000
–0
.5
0.
0
0.
5
1.
0
1.
5
mean annual rainfall (mm)
D
 s
ta
tis
tic
 fo
r o
ld
 g
ro
w
th
 s
pe
ci
al
iz
at
io
n
OSA
SAR
LUQ
CHJ
PAN
BRA
CAR
GUA
KIU
CHI
NIZ
CHM
FES
FOD
(a) (b)
Fig. 3. D (Fritz & Purvis 2010) for second-
growth specialization (panel a) and old-
growth specialization (panel b) plotted
against annual rainfall at the 14 sites. D
values approaching 0 indicate a more
clumped distribution of the trait on the
phylogeny. Three-letter codes correspond to
the site names given in Table 1.
1000 2000 3000 4000
30
40
50
60
70
80
90
10
0
mean annual rainfall (mm)
OSA
SAR
LUQCHJ
PAN
BRA
CAR
GUA
KIU
CHI
NIZ
CHM
FES
FOD
%
 in
di
vi
du
al
s 
in
 s
ec
on
da
ry
 fo
re
st
 c
la
ss
ifi
ed
 a
s
se
co
nd
ar
y 
fo
re
st
 s
pe
ci
al
is
ts
1000 2000 3000 4000
30
40
50
60
70
80
90
10
0
mean annual rainfall (mm)
OSA
SAR
LUQ
CHJ
PAN
BRA
CAR
GUA
KIU
CHI
NIZ
CHM
FES FOD
%
 in
di
vi
du
al
s 
in
 o
ld
 g
ro
w
th
 fo
re
st
 c
la
ss
ifi
ed
 a
s
ol
d 
gr
ow
th
 fo
re
st
 s
pe
ci
al
is
ts
(a) (b)
Fig. 4. The per cent of individuals in
secondary forest classified as SG specialists
(panel a), and the per cent of individuals in
old-growth forest classified as OG specialists
(panel b) for the 14 sites.
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
10 S. G. Letcher et al.
Another explanation for the lack of strong trait-successional
habitat association that we observed is the importance of
intraspecific variation in plant traits, and particularly the onto-
genetic shifts that occur between seedlings and adults (Clark
& Clark 1992; Poorter & Rozendaal 2008). We measured
wood and leaf traits of adults, following standard protocols,
Fabaceae
Rosid I
Rosid II
Magnoliids
Melastomataceae
Euphorbiaceae
basal Asterid
Asterid I
Asterid
II
Urticaceae
Salicaceae
Moraceae
Violaceae
Anacardiaceae
Burseraceae
Sapindaceae
Meliaceae
Malvaceae
Rubiaceae
Boraginaceae
Sapotaceae
Lecythidaceae
Arecaceae
Primulaceae
Apocynaceae
Hypericaceae
Clusiaceae
Annonaceae
Lauraceae
Myrtaceae
Chrysobalanaceae
Malpighiaceae
Polygonaceae
Nyctaginaceae
Rutaceae
Fig. 5. The phylogeny for the 921 species that were common enough to be classified with confidence. Branch colours indicate second-growth
specialists (red), old-growth specialists (blue) and generalists (grey). The labels of smaller families are omitted for clarity.
Table 4. The number of species in each major angiosperm clade that was classified with confidence using the multinomial model of Chazdon
et al. (2011) at each of the 14 sites. The numbers in parentheses indicate the number of species in that clade classified as secondary forest spe-
cialists and old-growth forest specialists, respectively; for example, 8 (0, 3) indicates that eight species were classified with confidence, zero as
secondary forest specialists, three as old-growth specialists and the rest as generalists
Asterid I Asterid II Basal Asterid Eudicot Magnoliid Monocot Rosid I Rosid II
CHM 2 (0, 0) 0 6 (2, 2) 0 1 (1, 0) 0 13 (6, 2) 5 (1, 1)
NIZ 2 (0, 2) 0 6 (2, 2) 0 1 (0, 1) 0 19 (7, 10) 6 (0, 6)
KIU 6 (0, 1) 0 11 (2, 2) 0 0 0 16 (5, 4) 10 (3, 0)
CHI 4 (1, 3) 0 7 (3, 4) 0 0 0 20 (7, 12) 13 (5, 6)
GUA 9 (3, 3) 0 9 (3, 3) 0 3 (1, 0) 0 26 (11, 6) 19 (5, 10)
FES 5 (1, 3) 2 (1, 0) 9 (4, 1) 0 7 (3, 2) 2 (0, 2) 38 (14, 12) 42 (17, 13)
CAR 4 (2, 1) 1 (0, 0) 2 (0, 1) 0 1 (0, 0) 1 (0, 1) 10 (3, 3) 9 (6, 2)
FOD 13 (3, 10) 2 (1, 1) 15 (5, 5) 0 14 (5, 5) 3 (1, 2) 27 (9, 14) 34 (7, 24)
BRA 0 0 21 (2, 19) 0 9 (4, 3) 2 (0, 1) 37 (12, 19) 20 (10, 9)
PAN 27 (9, 14) 3 (2, 0) 21 (7, 12) 1 (1, 0) 24 (3, 17) 5 (0, 5) 97 (38, 46) 75 (33, 35)
CHJ 0 0 2 (1, 1) 0 1 (1, 0) 0 11 (4, 5) 10 (5, 5)
LUQ 10 (5, 2) 3 (2, 0) 7 (2, 5) 0 3 (1, 1) 2 (1, 1) 26 (8, 12) 20 (10, 7)
SAR 16 (2, 13) 2 (0, 1) 16 (4, 10) 0 20 (5, 7) 5 (1, 4) 60 (14, 25) 35 (12, 18)
OSA 12 (7, 1) 1 (0, 0) 11 (2, 6) 0 21 (6, 7) 4 (0, 2) 58 (22, 20) 42 (20, 13)
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
Successional gradients shape plant evolution 11
but the seedling may be the most relevant life stage for under-
standing how traits mediate community assembly during suc-
cession (Poorter & Markesteijn 2008; Lu et al. 2014), and
seedling traits are not always directly related to adult traits
(Cornelissen et al. 2003a; Poorter 2007).
Although few of the traits that we studied showed strong
associations with successional habitat specialization, the sig-
nificant trait relationships that we identified generally did con-
form to expectations based on the physiological ecology of
plants during succession. For seed size, our data supported
the widespread finding that secondary forest trees have smal-
ler seeds (Swaine & Whitmore 1988; Chazdon et al. 2003).
In three of the five sites where we had seed length data, we
found that SG specialists tended to be smaller seeded than
OG specialists in both wet and dry forests. Small-seeded taxa
like Cecropia and Miconia are important pioneers in both wet
and dry forest (Dalling et al. 2002). The absence of large-
seeded taxa in early successional forests may be a product of
dispersal limitation; small seeds are more easily transported,
and the large animals that disperse large-seeded taxa may not
venture into secondary forest habitats (Guariguata & Ostertag
2001; Dalling et al. 2002; Chazdon et al. 2003). Germination
of smaller seeds also tends to be sensitive to either light or
diel temperature fluctuations (V
azquez-Yanes et al. 1990;
Pearson et al. 2002), which could contribute to the high abun-
dance of small-seeded taxa in secondary forests.
For leaf traits, very few of the traits that we investigated
showed a consistent relationship with successional habitat
specialization. Recent work on leaf traits in tropical forest
species has demonstrated that leaf traits have high intraspe-
cific variation in moist forest (Rozendaal, Hurtado & Poorter
2006) and dry forest (Markesteijn, Poorter & Bongers 2007).
The plasticity of traits, rather than the magnitude of interspe-
cific trait variation, may be a key factor in understanding how
leaf traits mediate plant success in different light environ-
ments (Markesteijn, Poorter & Bongers 2007). Ontogenetic
shifts may be a particularly important factor complicating our
ability to generalize about leaf traits that mediate success in
different environments (Clark & Clark 1992; Chazdon et al.
2010). Seedling leaf traits may be more informative than adult
leaf traits for understanding community assembly (Rozendaal,
Hurtado & Poorter 2006; Poorter 2007), and the relationship
between seedling and adult leaf traits is not necessarily
straightforward (Cornelissen et al. 2003a).
Maximum height was one of the traits that showed the
strongest relationships with successional habitat specialization
(Table 3). According to our data, SG specialization is nega-
tively related to maximum height (i.e. SG specialists tend to
be shorter statured) and OG specialization is positively related
to maximum height, especially in dry forests. The only site
where we found a negative association between OG special-
ization and maximum height was in the wet forest at LUQ
(Table 3). In wet and moist forests, the shorter statured trees
are often long-lived, slow-growing understorey species that
persist in low light (Bazzaz & Pickett 1980; Poorter, Bongers
& Bongers 2006; Kitajima & Poorter 2008). Since dry forest
trees have slower growth rates (Ewel 1977; Ruiz, Fandi~no &
Chazdon 2005), light-demanding short-statured species can
persist longer before being overtopped. A light-demanding
short-statured strategy would not be advantageous in wet for-
ests, where canopy closure occurs rapidly (van der Meer &
Bongers 1996) and where canopies are generally higher (Ewel
1977; Bazzaz & Pickett 1980).
Wood density is an important functional trait related to
growth rate, structural stability and longevity of trees (Chave
et al. 2006). Tropical secondary forest trees tend to have
lower wood density, faster growth and shorter life spans
(Woodcock 2000; Guariguata & Ostertag 2001; Wiemann &
Williamson 2002). In our data set, we found significant rela-
tionships between successional habitat specialization and
wood traits in only half the sites studied. However, the stud-
ies that have found stronger and more consistent relationships
between successional stage and wood density, with low wood
density in early successional tree species (Woodcock 2000;
Wiemann & Williamson 2002), did not correct for phyloge-
netic relatedness. Since wood density is a highly conserved
trait in most tropical tree lineages (Chave et al. 2006), analy-
sis of wood traits may be particularly susceptible to overinfla-
tion of significance due to phylogenetically non-independent
data points (Felsenstein 1985).
The great diversity of lineages that have produced SG spe-
cialists and OG specialists (Fig. 5) complicates our ability to
generalize about the life-history traits and functional traits of
the species that specialize at the ends of the successional hab-
itat gradients in Neotropical forests. Trade-offs along multiple
axes govern the way that species partition resources during
succession; different lineages have different physiological and
anatomical strategies for confronting the range of stresses that
occur along successional gradients (Harms et al. 2001; Poor-
ter & Markesteijn 2008; Baraloto et al. 2010; Lebrija-Trejos
et al. 2010; Pineda-Garc
ıa, Paz & Tinoco-Ojanguren 2011;
Lu et al. 2014). The great diversity of functional traits found
in tropical tree seedlings in forests across a precipitation gra-
dient (Markesteijn & Poorter 2009; Pineda-Garc
ıa, Paz & Ti-
noco-Ojanguren 2011; Lu et al. 2014) and the relatively low
number of strong correlations between functional traits and
successional habitat specialization (Table 3) both suggest that
there are many ways of being a successful pioneer.
In this analysis, we have considered only the endpoints of
the successional gradient. Studies of resource partitioning gen-
erally find species that specialize all along the gradient, not
just at the endpoints (Grime 1977; Ricklefs 1987; Tilman &
Pacala 1993). Our conceptual model of trade-offs along a suc-
cessional gradient (Fig. 1) is necessarily an oversimplification,
and when more extensive data are available, it may be possi-
ble to identify taxa that specialize at multiple points along the
gradient rather than focusing on the endpoints. In addition,
examining multiple successional stages may provide a more
nuanced understanding of the evolution of plant strategies. At
present, we have few large-scale data sets with sufficient rep-
lication at enough forest ages that would allow us to assess
this finer-scale partitioning of the successional habitat gradient
in Neotropical trees. Several previous studies do suggest the
possibility. Letcher (2010) studied five stages of succession in
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
12 S. G. Letcher et al.
Costa Rican wet forests and identified clades of angiosperms
that were more abundant than the null model expectation in
every stage of succession. Extending this work to three wet
forest sites (Brazil (BRA), Mexico (CHJ), and an expanded
set of sites in Costa Rica (SAR)), Letcher et al. (2012) found
significantly under- and overrepresented clades for each stage
of succession examined.
The scale at which phylogenetic conservatism is measured
can have a strong impact on the detection of conservatism;
traits can be overdispersed within clades yet conserved across
the phylogeny as a whole or vice versa (Cooper, Jetz & Freckl-
eton 2010; Fritz & Purvis 2010). Focusing on particular lin-
eages, instead of the angiosperm phylogeny as a whole, may
provide more insight into the evolution of successional habitat
specialization (Richardson et al. 2004). A tantalizing finding of
Letcher (2010) was that some genera contained species that
were overrepresented in several distinct age categories; for
instance, one Miconia species (Melastomataceae) was signifi-
cantly associated with old-growth forest, and two others were
significantly associated with young forest. One Protium species
(Burseraceae) was significantly associated with old-growth for-
est, while another was indicative of older secondary forest.
This pattern suggests that taxa can radiate into specialists at
particular stages in the successional gradient, as first envisioned
by G
omez-Pompa (1971) for genera such as Piper and Acaly-
pha over four decades ago. The new availability of phyloge-
netic data provides opportunities to study these patterns.
Incorporating phylogenetic relatedness into the study of
succession has revealed new insights about community
assembly (Letcher 2010; Letcher et al. 2012; Norden et al.
2012). Incorporating the study of succession into biogeogra-
phy and evolutionary biology could provide new insights as
well. When exploring the biogeography and evolution of spe-
cies-rich taxa, it may be illuminating to include an investiga-
tion of successional habitat specialization as well as climate
and dispersal history (Chanderbali, van der Werff & Renner
2001; Richardson et al. 2004).
In sum, our results show that successional habitat special-
ization is a conserved trait for Neotropical trees; though, there
are many different pioneer lineages and a concomitant diver-
sity of functional traits associated with successional habitat
specialization. Our data further suggest that successional habi-
tat specialization may be more evolutionarily labile in dry for-
ests than in wet forests, potentially due to the lower
dissimilarity of early successional and late-successional habi-
tats in dry forests. Future studies will test the strength and
generality of this pattern and reveal the extent to which radia-
tion into successional niches has shaped plant evolution.
Acknowledgements
We thank three anonymous referees whose suggestions improved the manuscript.
The authors gratefully acknowledge the financial and logistical support of the fol-
lowing agencies and grants: at CHJ, NWO-WOTRO grant W85-326 to MvB and
FB, and SEMARNAT-CONACyT 2002-C01-0597 and SEP-CONACyT CB-
2005-01-51043 to MMR; at NIZ, SEP-CONACyT (CB-2009-01-128136) and
DGAPA-UNAM (IN 216007-3) grants to JAM; at SAR, NSF DEB-0424767,
DEB-0639114 and DEB-1110722 to RLC, Andrew W. Mellon Foundation to
DAC and DBC, NSF predoctoral fellowships to VB and S.G.L. and an OTS grad-
uate fellowship to S.G.L.; at BRA, NSF DEB-0614044 to S.G.L., Conselho Nac-
ional de Desenvolvimento Cientifico e Tecnologico (CNPq) to RCGM, and NSF
DEB-1147434 and DEB-0639114 to GBW; at CAR and LUQ, Institute of Latin
American Studies, a Cross-Cutting Initiatives Earth Institute (Columbia Univer-
sity), and NSF and NASA grants to the University of Puerto Rico; at CHI, BOL-
FOR grants to DK and TJK and DEB9201 1026, National Geographic Society
5570-95, and Tinker Foundation grants to TJK; at GUA, support from the Insti-
tute on the Environment (University of Minnesota), NASA (NS000107), NSF
DEB-1053237 and McKnight Land Grant Professorship (University of Minne-
sota) to JSP, support from the Vicerrector
ıa de Investigaci
on-Universidad de
Costa Rica and Organization for Tropical Studies to E.G.-J. and R.Q.-M.; at KIU,
FOMIX-Yucat
an grant YUC-2008-C06-108863 to J.L.H.S. and CONACYT
scholarship 46701 to L.S.V.; at OSA, the Blue Moon Fund; at PAN, data
from Barro Colorado Island were supported by NSF DEB-0640386, DEB-
0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347,
DEB-9615226, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058,
DEB-8906869, DEB-8605042, DEB-8206992 and DEB-7922197 to S.P. Hub-
bell, the Center for Tropical Forest Science, the Smithsonian Tropical Research
Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foun-
dation, the Small World Institute Fund and numerous private individuals; funding
for the Agua Salud Project (secondary forest) came from the HSBC climate part-
nership, STRI, the Panama Canal Authority, the Frank Levinson Family Founda-
tion and the Motta Family Foundation; at CHM, PAPIIT-UNAM IN208012 to H.
Paz, SEP-CONACYT 2012-179045 to A. Martinez-Yrizar, and SEP-CONACYT
2009-129740 and PAPIIT-UNAM IN229007-3 to P.B. We also gratefully
acknowledge our field assistants, technicians, taxonomists and parataxonomists,
and the landowners who provided access to their forests. This is publication #671
in the Technical Series of the Biological Dynamics of Forest Fragments Project,
BDFFP-INPA-SI.
Data accessibility
Except where otherwise noted, the phylogenetic tree used in this study and data
on plot locations, species abundances and functional traits are archived at
doi:10.5061/dryad.d87v7.
Species abundance data for:
GUA: DOI:10.1016/j.foreco.2008.10.036,
CAR and LUQ: http://luq.lternet.edu/research/luquillo-forest-dynamics-plot-
lfdp,
PAN: old-growth data at http://ctfs.arnarb.harvard.edu/webatlas/datasets/bci/;
secondary forest data at http://ctfs.arnarb.harvard.edu/Public/plotdataaccess/.
KIU: DOI 10.1111/j.1744-7429.2011.00783.x and DOI 10.1007/s10980-
010-9561-3.
Functional trait data for:
GUA: doi: 10.1111/j.1365-2435.2010.01701.x,
PAN: doi: 10.1890/09-2335.1, doi: 10.1007/s00442-015-3339-x.
CAR/LUQ: uploaded to TRY on 5/26/15 as ‘CTFS Luquillo Forest
Dynamics Plot – Swenson’; it will be available in the next data base
update.
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Received 17 October 2014; accepted 2 June 2015
Handling Editor: Shurong Zhou
Supporting Information
Additional Supporting Information may be found in the online ver-
sion of this article:
Table S1. Detailed information on the study sites, including plot
dimensions and ages. For specific methodological details, see the ref-
erences cited in Table 1 for each site.
© 2015 The Authors. Journal of Ecology © 2015 British Ecological Society, Journal of Ecology
Successional gradients shape plant evolution 15