Biodiversity recovery of Neotropical secondary forests

Abstract

Old-growth tropical forests harbor an immense diversity of tree species but are rapidly being cleared, while secondary forests that regrow on abandoned agricultural lands increase in extent. We assess how tree species richness and composition recover during secondary succession across gradients in environmental conditions and anthropogenic disturbance in an unprecedented multisite analysis for the Neotropics. Secondary forests recover remarkably fast in species richness but slowly in species composition. Secondary forests take a median time of five decades to recover the species richness of old-growth forest (80% recovery after 20 years) based on rarefaction analysis. Full recovery of species composition takes centuries (only 34% recovery after 20 years). A dual strategy that maintains both old-growth forests and species-rich secondary forests is therefore crucial for biodiversity conservation in human-modified tropical landscapes.

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Rozendaal et al., Sci. Adv. 2019; 5 : eaau3114 6 March 2019
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ECOLOGY
Biodiversity recovery of Neotropical secondary forests
Danaë M. A. Rozendaal1,2,3,4*†, Frans Bongers1, T. Mitchell Aide5, Esteban Alvarez-Dávila6,7,
Nataly Ascarrunz8, Patricia Balvanera9, Justin M. Becknell10, Tony V. Bentos11,
Pedro H. S. Brancalion12, George A. L. Cabral13, Sofia Calvo-Rodriguez14, Jerome Chave15,
Ricardo G. César12, Robin L. Chazdon3,16,17, Richard Condit18, Jorn S. Dallinga4,
Jarcilene S. de Almeida-Cortez13, Ben de Jong19, Alexandre de Oliveira20, Julie S. Denslow21,
Daisy H. Dent22,23, Saara J. DeWalt24, Juan Manuel Dupuy25, Sandra M. Durán14, Loïc P. Dutrieux4,26,
Mario M. Espírito-Santo27, María C. Fandino28, G. Wilson Fernandes29, Bryan Finegan30,
Hernando García31, Noel Gonzalez32, Vanessa Granda Moser33, Jefferson S. Hall18,
José Luis Hernández-Stefanoni25, Stephen Hubbell18, Catarina C. Jakovac11,16,34,
Alma Johanna Hernández31, André B. Junqueira16,34,35‡, Deborah Kennard36, Denis Larpin37,
Susan G. Letcher38, Juan-Carlos Licona8, Edwin Lebrija-Trejos39, Erika Marín-Spiotta40,
Miguel Martínez-Ramos9, Paulo E. S. Massoca11, Jorge A. Meave41, Rita C. G. Mesquita11,
Francisco Mora9, Sandra C. Müller42, Rodrigo Muñoz41, Silvio Nolasco de Oliveira Neto43,
Natalia Norden31, Yule R. F. Nunes27, Susana Ochoa-Gaona19, Edgar Ortiz-Malavassi44,
Rebecca Ostertag45, Marielos Peña-Claros1, Eduardo A. Pérez-García41, Daniel Piotto46,
Jennifer S. Powers47, José Aguilar-Cano31, Susana Rodriguez-Buritica31,
Jorge Rodríguez-Velázquez9, Marco Antonio Romero-Romero41, Jorge Ruíz48,49,
Arturo Sanchez-Azofeifa14, Arlete Silva de Almeida50, Whendee L. Silver51, Naomi B. Schwartz52,
William Wayt Thomas53, Marisol Toledo8, Maria Uriarte52, Everardo Valadares de Sá Sampaio54,
Michiel van Breugel18,55,56, Hans van der Wal57, Sebastião Venâncio Martins43,
Maria D. M. Veloso27, Hans F. M. Vester58, Alberto Vicentini11, Ima C. G. Vieira50, Pedro Villa59,60,
G. Bruce Williamson11,61, Kátia J. Zanini42, Jess Zimmerman62, Lourens Poorter1
Old-growth tropical forests harbor an immense diversity of tree species but are rapidly being cleared, while second-
ary forests that regrow on abandoned agricultural lands increase in extent. We assess how tree species richness
and composition recover during secondary succession across gradients in environmental conditions and anthro-
pogenic disturbance in an unprecedented multisite analysis for the Neotropics. Secondary forests recover remark-
ably fast in species richness but slowly in species composition. Secondary forests take a median time of five
decades to recover the species richness of old-growth forest (80% recovery after 20 years) based on rarefaction
analysis. Full recovery of species composition takes centuries (only 34% recovery after 20 years). A dual strategy
that maintains both old-growth forests and species-rich secondary forests is therefore crucial for biodiversity
conservation in human-modified tropical landscapes.
INTRODUCTION
Tropical forests store the majority of the world’s tree diversity, with
an estimated 53,000 tree species (1). Over the past decades, many
hyperdiverse old-growth forests and their biodiversity have disap-
peared because of the conversion of forests into agricultural lands
(2). Secondary forests regrowing after abandonment of agricultural
lands increase rapidly in extent and may constitute important bio-
diversity reservoirs (3). It is therefore critical to assess the biodiver-
sity conservation potential of secondary tropical forests (3,4) by
analyzing biodiversity recovery (i.e., the rate of recovery to a predis-
turbance state) of tropical forests during secondary succession. Bio-
diversity recovery could be fast because species richness (i.e., the
number of species) may recover rapidly to old-growth forest levels
over succession (5). Recovery of species composition (i.e., species
identity and relative abundance), in contrast, could take centuries
(6), particularly if old-growth species go locally extinct or fail to be
dispersed into regenerating forest areas.
Recovery rates of tree species richness and composition have been
evaluated for individual sites (7–9) and summarized in meta-analyses
(5, 10), but how recovery rates vary across large-scale gradients in
environmental conditions and anthropogenic disturbance remains
unknown. Community assembly during succession depends on the
size and the composition of the regional species pool and on local
effects of environmental filtering and dispersal limitation that de-
termine which species actually establish. The size and composition
of the regional species pool have been shaped by historical effects
and vary with water and soil nutrient availability (11). Locally, tree
species’ establishment during succession depends on (i) water and
nutrient availability that constrain or facilitate seedling establishment,
(ii) forest cover and quality in the surrounding landscape matrix that
indicate the availability and proximity of seed sources and dispersal
agents (12,13), and (iii) the type and intensity of previous land use.
Previous land use modifies environmental conditions, such as soil
structure and nutrient availability, and determines the presence of
forest legacies (e.g., remnant trees, a soil seed bank, and resprouting
tree stumps) that accelerate succession (14).
Here, we assess how tree species richness and composition re-
cover during secondary succession across major gradients in environ-
mental conditions and anthropogenic disturbance in the Neotropics
using original data from 56 sites, 1630 plots, and >183,000 trees
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(Fig.1 and table S1) (15). We quantify biodiversity recovery as the
absolute recovery rate at which tree species richness increases over
succession and as the relative recovery of species richness and com-
position to old-growth forest values to assess if, and when, secondary
forests attain the old-growth stage. We hypothesize that biodiversity
recovery will (i) increase with water availability and soil fertility in
absolute terms, because of larger regional species pools in wetter
forests and at high fertility soils (16,17) and enhanced tree growth
and survival under these conditions, but decrease in relative terms
because of the larger species pool that needs to be recovered; (ii)
increase with forest cover in the landscape matrix because high forest
cover tends to indicate greater availability of old-growth forests in
the landscape that ensure seed availability of old-growth species
(12,13); and (iii) be higher on abandoned shifting cultivation fields
compared to pasture because of the lower levels of disturbance asso-
ciated with shifting cultivation (18).
We assessed the biodiversity recovery of Neotropical forests us-
ing data from 56 chronosequence sites, where successional change
is inferred from plots that vary in time since abandonment (Fig.1
and table S1). For each site, we calculated absolute recovery of tree
species richness per secondary forest plot as the number of species
per 25 stems ≥5 cm diameter at breast height (dbh). For 45 sites for
which data from old-growth forest plots were available, we calculated
relative recovery of species richness as a percentage of the mean
number of species per 25 stems of old-growth plots and relative re-
covery of species composition (the mean pairwise similarity in spe-
cies composition between secondary and old-growth plots based on
the Chao-Jaccard index expressed as a percentage of the mean within-
site similarity between old-growth plots). We used linear mixed-
effects models to model absolute recovery of species richness and
relative recovery of species richness and composition as a function
of stand age, the size of the local old-growth forest species pool (for
relative recovery; calculated using the Chao 1 estimator), climatic
water availability (CWA), soil cation exchange capacity (CEC; an
indicator for soil fertility), forest cover in the landscape matrix (based
on tree cover in the year 2000 in a 5000-m radius around the plots),
previous land use (shifting cultivation, pasture, or a combination of
these), and plot size (to account for variation in plot size across
sites) as fixed effects, along with a random intercept and slope for
stand age per site.
1Forest Ecology and Forest Management Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, Netherlands. 2Department of Biology, University of Regina,
3737 Wascana Parkway, Regina, SK S4S 0A2, Canada. 3Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA. 4Laboratory of Geo-
Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, Netherlands. 5Department of Biology, University of Puerto Rico,
P.O. Box 23360, San Juan, PR 00931-3360, Puerto Rico. 6Escuela ECAPMA, UNAD, Calle 14 Sur No. 14-23, Bogotá, Colombia. 7Fundación Con Vida, Avenida del Río # 20-114,
Medellín, Colombia. 8Instituto Boliviano de Investigación Forestal (IBIF), Km 9 Carretera al Norte, El Vallecito, FCA-UAGRM, Santa Cruz de la Sierra, Bolivia. 9Instituto de
Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, CP 58190, Morelia, Michoacán, México. 10Environmental Studies Program,
Colby College, Waterville, ME 04901, USA. 11Biological Dynamics of Forest Fragments Project, Coordenação de Dinâmica Ambiental, Instituto Nacional de Pesquisas da
Amazônia, Manaus, AM CEP 69067-375, Brazil. 12Department of Forest Sciences, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Av. Pádua Dias, 11,
13418-900 Piracicaba, São Paulo, Brazil. 13Departamento de Botânica-CCB, Universidade Federal de Pernambuco, Pernambuco, CEP 50670-901, Brazil. 14Earth and Atmo-
spheric Sciences Department, University of Alberta, Edmonton, AB T6G 2EG, Canada. 15Laboratoire Evolution et Diversité Biologique, UMR5174, CNRS/Université Paul
Sabatier, Bâtiment 4R1, 118 route de Narbonne, F-31062 Toulouse cedex 9, France. 16International Institute for Sustainability, Estrada Dona Castorina 124, Horto, Rio de
Janeiro, RJ 22460-320, Brazil. 17Department of Ecology and Evolutionary Biology, Ramaley N122, University of Colorado, Boulder, CO 80309, USA. 18SI ForestGEO, Smithsonian
Tropical Research Institute, Roosevelt Ave., 401 Balboa, Ancon, Panama. 19Department of Sustainability Science, El Colegio de la Frontera Sur, Av. Rancho Polígono
2-A, Ciudad Industrial, Lerma 24500, Campeche, Mexico. 20Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, Rua do Matão, Travessa 14, no. 321,
São Paulo CEP 05508-090, Brazil. 21Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, LA 70118, USA. 22Smithsonian Tropical Research
Institute, Roosevelt Ave., 401 Balboa, Ancon, Panama. 23Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK. 24Department of Biological
Sciences, Clemson University, 132 Long Hall, Clemson, SC 29634, USA. 25Centro de Investigación Científica de Yucatán A.C. Unidad de Recursos Naturales, Calle 43 # 130
x 32 y 34, Colonia Chuburná de Hidalgo, C.P. 97200 Mérida, Yucatán, México. 26National Commission for the Knowledge and Use of Biodiversity (CONABIO), Mexico City,
C.P. 14010, México. 27Departamento de Biologia Geral, Universidade Estadual de Montes Claros, Montes Claros, Minas Gerais, CEP 39401-089, Brazil. 28Fondo Patrimonio
Natural para la Biodiversidad y Areas Protegidas, Calle 72 No. 12-65 piso 6, Bogotá, Colombia. 29Ecologia Evolutiva & Biodiversidade/DBG, ICB/Universidade Federal de
Minas Gerais, Belo Horizonte, MG 30161-901, Brazil. 30Forests, Biodiversity and Climate Change Programme, CATIE – Centro Agronómico Tropical de Investigación y
Enseñanza, Turrialba, Costa Rica. 31Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Calle 28A No. 15-09 Bogotá, Colombia. 32Departamento
de Ingenierías, Instituto Tecnológico de Chiná, Tecnológico Nacional de México, Calle 11 s/n, entre 22 y 28, Chiná, 24520 Campeche, México. 33Graduate School, Tropical
Agricultural Centre for Research and Higher Education (CATIE), Turrialba, Costa Rica. 34Centre for Conservation and Sustainability Science (CSRio), Department of Geography
and the Environment, Pontificial Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil. 35Department of Soil Quality, Wageningen University, P.O. Box 47, 6700 AA,
Wageningen, Netherlands. 36Department of Physical and Environmental Sciences, Colorado Mesa University, 1100 North Avenue, Grand Junction, CO 81501, USA. 37Direction
Générale Déléguée aux Musées et aux Jardins botaniques et zoologiques (DGD-MJZ), Direction des Jardins Botaniques, Muséum National d’Histoire Naturelle, 43 rue
Buffon, 75005 Paris, France. 38Department of Environmental Studies, Purchase College (SUNY), 735 Anderson Hill Road, Purchase, NY 10577, USA. 39Department of Biology
and the Environment, Faculty of Natural Sciences, University of Haifa-Oranim, Tivon 36006, Israel. 40Department of Geography, University of Wisconsin–Madison, 550
North Park St., Madison, WI 53706, USA. 41Departamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City,
C.P. 04510, México. 42Graduate Program in Ecology, Departamento de Ecologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
43Departamento de Engenharia Florestal, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. 44Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Forestal,
Cartago, Costa Rica. 45Department of Biology, University of Hawai’i at Hilo, Hilo, HI 96720, USA. 46Centro de Formação em Ciências Agroflorestais, Universidade Federal do
Sul da Bahia, Itabuna-BA, 45613-204, Brazil. 47Departments of Ecology, Evolution, and Behavior and Plant Biology, University of Minnesota, Saint Paul, MN 55108, USA.
48School of Social Sciences, Geography Area, Universidad Pedagogica y Tecnologica de Colombia (UPTC), Tunja, Colombia. 49Department of Geography, 4841 Ellison Hall,
University of California, Santa Barbara, Santa Barbara, CA 93106, USA. 50Museu Paraense Emilio Goeldi, C.P. 399, CEP 66040-170, Belém, Pará, Brazil. 51Ecosystem Science
Division, Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA. 52Department of Ecology, Evolution and
Environmental Biology, Columbia University, New York, NY, 10027, USA. 53Institute of Systematic Botany, The New York Botanical Garden, 2900 Southern Blvd., Bronx, NY
10458-5126, USA. 54Departamento de Energia Nuclear -CTG, Universidade Federal de Pernambuco, Av. Prof. Luis Freire 1000, Recife, Pernambuco, CEP 50740-540, Brazil.
55Yale-NUS College, 16 College Avenue West, Singapore 138610, Singapore. 56Department of Biological Sciences, National University of Singapore, 14 Science Drive 4,
Singapore 117543, Singapore. 57Departamento de Agricultura, Sociedad y Ambiente, El Colegio de la Frontera Sur, Unidad Villahermosa, 86280 Centro Tabasco, México.
58Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, 1090 GE Amsterdam, Netherlands. 59Program of Botany, Departa-
mento de Biologia Vegetal, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. 60Fundación para la Conservación de la Biodiversidad (ProBiodiversa), 5101
Mérida, Mérida, Venezuela. 61Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803-1705, USA. 62Department of Environmental Sciences,
University of Puerto Rico, Río Piedras Campus, San Juan, PR 00936, Puerto Rico.
*Corresponding author. Email: danae.rozendaal@wur.nl
†Present address: Plant Production Systems Group and Centre for Crop Systems Analysis, Wageningen University, P.O. Box 430, 6700 AK Wageningen, Netherlands.
‡Present address: Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, 08193 Bellatera, Barcelona, Spain.
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RESULTS
Recovery of species richness and species composition
Absolute recovery of tree species richness and relative recovery of
species richness and composition significantly increased with stand
age (Fig.2). After 20 years, predicted species richness was, on average,
11 species per 25 stems but varied fourfold (from 4 to 16 species;
Fig.2A) across sites. Predicted relative recovery of species richness
was, on average, 80% of the richness of old-growth forest after 20 years
in standardized samples of 25 stems and varied twofold across sites
(from 46 to 99%; Fig.2B). Predicted relative recovery of composi-
tion was, on average, 34% after 20 years, ranging from 5 to 102%
across sites (Fig.2C).
Strong effects of stand age on biodiversity recovery
Among all predictors, stand age had the strongest effect on all three
types of recovery (Fig.3). Absolute recovery of species richness also
significantly increased with CWA and with plot size but was not
influenced by CEC, landscape forest cover, and previous land use
(Fig.3A and fig. S2). Relative recovery of species richness signifi-
cantly decreased with the size of the species pool and increased with
forest cover (Fig.3B). Relative recovery of species composition
significantly increased with plot size (Fig.3C).
Time needed to recover to old-growth forest values
Across sites, the median predicted time span to full recovery of
old-growth forest values was 54 years for species richness (range,
11 to 228 years) and 780 years for species composition (range, 19 years
to no recovery at all). Recovery to 90% of old-growth values was
31 years for species richness (range, 5 to 134 years) and 487 years
for species composition (range, 14 years to no recovery). Given
the high median value and tremendous site-to-site variation in
relative recovery of species composition (Fig. 2C), it is safest to
conclude that recovery to old-growth forest composition may take
centuries.
Fig. 1. Tree species richness and recovery of Neotropical secondary forests. (A) Absolute recovery of species richness (number of species per 25 stems). (B) Relative
recovery of species richness [% old-growth (OG)] after 20 years. The 56 study sites (45 sites for relative recovery) are indicated; symbol size scales with predicted recovery
at 20 years after abandonment. Green shading indicates forest cover in the year 2000 (39). Dry forests have an annual rainfall of <1500 mm year−1, moist forests have an
annual rainfall of 1500 to 2499 mm year−1, and wet forests have an annual rainfall of ≥2500 mm year−1. (C) Forest recovery in dry tropical forests: secondary forest and
old-growth forest plot in a dry forest site in the Atlantic forest in Brazil. (D) Forest recovery in wet tropical forests: secondary forest and old-growth forest plot in the wet
forest site Sarapiquí in Costa Rica. Stand age (in years) of the secondary forests is indicated. (E) Forest legacies in an agricultural field in Márques de Comillas, Mexico.
Photo credit: M.M.E.-S., D.M.A.R., and M.M.-R.
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DISCUSSION
Quick recovery of species richness but slow recovery of
species composition
Tree species richness increased very rapidly during secondary suc-
cession with 80% recovery of old-growth values after only 20 years,
which highlights the resilience of tropical forests in terms of species
richness. In many secondary forests, tree species richness surpassed
that of old-growth forest (Fig.2B), which is in line with the inter-
mediate disturbance hypothesis (19,20): Biodiversity peaks in mid-
successional forests because of the co-occurrence of persisting pioneer
species that established just after disturbance and late-successional,
shade-tolerant species that established in the shade of pioneers
(7,9). Fast relative recovery of species richness is likely facilitated by
forest legacies (e.g., a soil seed bank, tree stumps, and roots from
which trees establish), by remnant trees that attract seed dispersal
agents (14), and by propagule availability in the landscape matrix.
Relative recovery of species composition was much slower (5,10)
because of the low dispersal capacity of rare old-growth specialists
that may delay their arrival, as well as the often centuries-long lifespan
of trees that results in slow species replacement over succession.
Secondary forests have a remarkably high tree species richness, but
much of their diversity may be accounted for by second-growth
specialists (21). Despite the presence of old-growth species in sec-
ondary forests (7,9), full recovery of old-growth forest composition
is estimated to take centuries, assuming propagule availability.
Hence, secondary forests have a high conservation value in human-
modified tropical landscapes, but in the short term, they cannot re-
place old-growth forests that harbor many old-growth specialists (22).
Effects of climate and landscape forest cover
Absolute recovery of species richness increased with water avail-
ability, which might suggest that more species are able to establish
under wetter conditions, as a result of weaker environmental filter-
ing (23). Nevertheless, the slow absolute recovery of species richness
in sites with low water availability may also result from the smaller
species pool in these forests (fig. S3) and mirrors variation in species
richness of old-growth forests in the Neotropics.
Relative recovery of species richness decreased with the size of
the local species pool and increased with landscape forest cover.
Since the size of the species pool is strongly, positively related to
CWA (fig. S3), recovery may be faster in drier forests where the
lower number of species present allows for faster recovery. High
forest cover is generally associated with greater availability of seed
trees and dispersal agents and increased landscape connectivity,
enhancing relative recovery of species richness. For 45 of our sites,
we estimated that 53% (±3.8 SE) of total forest cover around the
plots consisted of secondary forest, with the remainder consisting of
old-growth forest. Propagule availability of both secondary and old-
growth forest species therefore ensured recovery. Nevertheless, the
effect of forest cover on relative recovery of species richness was
weak and did not influence absolute recovery of species richness and
relative recovery of species composition, possibly because we quan-
tified landscape forest cover for the year 2000 only. Ideally, we would
have included the surrounding forest cover at the time of abandon-
ment for each plot, but unfortunately, historical forest cover data
were not always available. Therefore, we used a remote sensing–
based tree cover map for the year 2000 (24), which provided a stan-
dardized measure of forest cover for all sites, although local accuracy
is not completely verified. Another reason for finding weak effects
A
B
C
Fig. 2. Absolute recovery of species richness and relative recovery of species
richness and composition in relation to stand age for Neotropical secondary
forests. Each line indicates predicted recovery per site based on the site-specific
intercept and slope from the mixed-effects models. Lines span the age range of
secondary forest per site; symbols indicate the individual plots. Dry forests (annual
rainfall of <1500 mm year−1) are indicated in green, moist forests (1500 to 2499 mm
year−1) are indicated in light blue, and wet forests (≥2500 mm year−1) are indicated
in dark blue. The gray line indicates the average predicted recovery rate for a site
that is recovering after shifting cultivation, with all other predictors kept constant
at the mean. (A) Rarefied species richness (per 25 stems; n = 56 sites). (B) Relative
recovery of rarefied species richness [as a percentage of old-growth (% OG) forest;
n = 45 sites]. The black dashed line indicates 100% recovery to the species richness
of old-growth forest. (C) Relative recovery of species richness (n = 45 sites) based
on the Chao-Jaccard index. The black dashed line indicates 100% recovery to the
mean similarity in species composition (0.47 ± 0.040 SE) between old-growth plots
in the same site averaged across the 41 sites with at least two old-growth plots to
account for within-site variation in composition across old-growth plots.
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of landscape forest cover may be that most of our sites had a rela-
tively high landscape forest cover (>50%), although, overall, the range
in landscape forest cover across our sites was large (9.4 to 99.9%;
table S1). Possibly, biodiversity recovery is only hampered at very
low levels of landscape forest cover.
No effects of soil fertility and previous land use
Unexpectedly, we did not find soil fertility effects on biodiversity
recovery, possibly because (i) CEC was obtained from a global data-
base rather than locally measured for many sites, (ii) phosphorus and
nitrogen may be more important than CEC, and/or (iii) biogeo-
graphical history may be driving the observed patterns (i.e., higher
diversity in the central and western Amazon than in Central America
and Mexico). We neither detected differences in biodiversity recovery
among the broad categories of previous land use that we defined,
despite known effects of land-use history in some of our sites
(13,25), which could be due to within–land-use type variation in
land-use intensity (13). Recovery will likely depend on the number
of cycles that a fallow is cultivated or used as pasture and on the use
of fire (25), but it proved impossible to obtain detailed information
in a standardized way for all sites. Recovery will likely also depend
on the extent of additional perturbations during the recovery pro-
cess (26), but we neither had data on the occurrence of disturbances
after secondary forests started regrowing.
Conclusions
We show that species richness recovers remarkably fast in secondary
forests across the Neotropics, which highlights their potential for
biodiversity conservation in human-modified tropical landscapes.
Forest cover in the surrounding landscape should be maintained to
safeguard seed sources and dispersers. Average forest cover in our
sites was high (76%); recovery may be much slower in severely de-
forested landscapes. Fast recovery of species richness, along with fast
recovery of standing biomass (27), could also promote the provision
of other ecosystem services, such as carbon storage and sequestra-
tion (15,28,29). Secondary forests should be left to grow to ad-
vanced age to sustain species pools in the landscape and to enhance
landscape connectivity (26), particularly where old-growth forests
are nearby (12). Our results indicate that natural regeneration is an
effective, nature-based solution for maintaining tree biodiversity.
Species composition, in contrast, may take centuries to recover.
Conservation policies and restoration efforts should therefore main-
tain both secondary and old-growth forests in the landscape to
enhance the potential for biodiversity conservation of secondary
forests (3,26,30) and thereby that of the entire landscape.
MATERIALS AND METHODS
Study sites and plot characteristics
Chronosequence data were compiled for 56 Neotropical lowland
forest sites, in 10 countries, covering the entire latitudinal gradient
in the Neotropics (Fig.1 and table S1) (15). To reduce the con-
founding effect of elevation, we included sites that were generally
below 1000 m above sea level. Annual rainfall varied from 750 to
6000 mm across sites, topsoil CEC varied from 1.7 to 64.6 cmol(+) kg−1,
and percentage of forest cover in the landscape matrix ranged from
9.4 to 99.9% (table S1).
We aimed to assess the rate and extent of biodiversity recovery
after abandonment of pastures and shifting cultivation fields. Shift-
ing cultivation is typically performed at a small scale, in which
patches of 0.5 to 1 ha are slashed, burned, cultivated, or used as
pasture for some years and abandoned, after which they recover
(13,14). We were therefore interested in recovery of alpha diversity
at the scale of these local patches. To avoid edge effects of neighbor-
ing old-growth forest, secondary forest researchers typically estab-
lish small plots (0.1 ha; see below) in abandoned fields. For each
Coefficient
–2 –1 012
Coefficient
–20 –10 01020
Coefficient
–20 –10 01020
A
Rarefied richness (25)
B
Rarefied richness (% OG)
C Species composition (% OG)
ln(age)
Species pool
Climatic water availability
Soil fertility (CEC)
Forest cover
Land use (PA − SC)
Land use (P
A − SC and PA )
Plot size
Fig. 3. Effects of stand age, the size of the local old-growth forest species pool, CWA, CEC, forest cover, previous land-use type, and plot size on biodiversity
recovery in Neotropical secondary forests. The size of the local old-growth forest species pool was estimated based on the Chao 1 estimator. Standardized coefficients
with bootstrapped 95% confidence intervals are indicated. Negative coefficients indicate a negative relation, and positive coefficients indicate a positive relation. Effect
sizes of land-use type comparisons are not directly comparable with those of the other predictors. SC, shifting cultivation; SC and PA, some plots shifting cultivation and
some plots pasture; PA, pasture. Filled symbols indicate significant responses, and open symbols indicate nonsignificant responses. (A) Absolute recovery of rarefied
species richness (number of species per 25 stems; n = 56 sites). Effects of the local species pool on absolute recovery of rarefied richness were not included, as old-growth
plots were not available for all sites. (B) Relative recovery of rarefied richness [% old-growth (OG); n = 45 sites]. (C) Relative recovery of species composition [% OG; based
on the Chao-Jaccard index (31)], accounting for variation in composition among old-growth plots (n = 45 sites).
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chronosequence site, an average of 29.1 plots (range, 4 to 251) were
included, with secondary forest plots ranging in stand age from 1 to
100 years across sites (table S1). Plot ages were estimated using
landowner interviews (33 sites), satellite images or aerial photo-
graphs (6 sites), landowner interviews combined with tree-ring
counts (1 site), and satellite images and/or aerial photographs that
were combined with information from landowner interviews (16 sites).
In general, age estimates for young secondary plots were regarded
to be more precise (precise to the year or to 6 months for some sites)
than age estimates for older secondary forest plots (error of a few
years), and this is exactly what is needed given that initial recovery
goes fast (thus exact age estimates are important) and that later in
succession recovery rates slow down. Data from old-growth forests
were included as a reference for estimating biodiversity recovery for
45 of the 56 sites (table S1). Old-growth forests had no record of
previous disturbance for at least 100 years. Plot sizes ranged from
0.01 to 1 ha, with an average of 0.09 ha across all plots. To accurately
estimate biodiversity recovery, we assured that, within each chrono-
sequence site, secondary forest plots and old-growth plots had
similar sizes, but for 12 sites, old-growth plots were slightly larger
or smaller than secondary forest plots (table S1). All stems ≥5 cm
dbh of trees, palms, and shrubs were measured for dbh and identified
to species, with the exception of six sites for which the minimum
dbh was 10 cm. Across chronosequences, on average, 92.8% of
the stems were identified to species (range, 58 to 100%) and 99.6%
(range, 94 to 100%) were identified to family, genus, species, or
morphospecies (table S2).
Recovery of species richness and species composition
To account for differences in stem density among plots within sites
and across sites, we calculated rarefied species richness per 25 stems
for all secondary (i.e., absolute recovery of species richness) and
old-growth plots. Plots with less than 25 stems (only 186 of a total of
1816 plots) were excluded from analyses. Relative recovery of spe-
cies richness and composition was calculated for 45 sites for which
old-growth forest plots were included in the chronosequences. Rela-
tive recovery of species richness was expressed as a percentage of
the mean rarefied richness (based on 25 stems) of old-growth forest
plots in the same site. We used 25 stems as a reference value for
comparing absolute and relative recovery in species richness among
sites to be able to include as many plots as possible, because plots
were generally small and included few stems. Differences in species
richness among sites may be compressed in a small sample of
25 stems; therefore, we may underestimate diversity differences across
sites. Rates of relative recovery of species richness may depend on
the number of stems used for calculating rarefied richness, as rarefied
richness based on 25 stems starts to saturate if more than 25 species
are present. To assess the influence of the number of stems used for
calculation of rarefied richness on estimated rates of relative recovery,
we calculated rarefied richness per 25 and per 50 stems for a subset
of 697 secondary forest plots (in 36 sites that included old-growth
plots) that had at least 50 stems. General ranking in absolute recovery
of species richness of plots across gradients in environmental con-
ditions and in anthropogenic disturbance was similar for rarefied
richness calculated for 25 and 50 stems, as linear mixed-effects
models (as described below) indicated that, for both measures of
rarefied richness, the same predictors had an effect on absolute and
relative recovery (based on the set of plots with at least 50 stems).
Nevertheless, relative recovery of species richness was lower when
rarefied richness was calculated based on a sample of 50 stems, with,
on average, 81.3 and 77.6% of old-growth species richness recovered
after 20 years based on 25 and 50 stems, respectively. We may there-
fore obtain faster rates of relative recovery of species richness by
using the number of species per 25 stems.
Relative recovery of species composition of each secondary
forest plot was calculated as the mean pairwise similarity in species
composition between the secondary forest plot and the old-growth
plots in the same site based on the Chao-Jaccard index, which com-
pares abundances of shared and unshared species between two plots
(31). The Chao-Jaccard index reduces undersampling bias by account-
ing for unseen, shared species, making it suitable for comparing
plots of different sizes with many rare species (31). In addition, we
accounted for the large variation in species composition across old-
growth forest plots within a site that results from strong local spe-
cies turnover. For the 41 sites with at least two old-growth plots, the
overall average within-site similarity of old-growth plots, which is
the average of the per-site average similarity between pairs of old-
growth plots of the 41 sites, was 0.47 ± 0.040% (mean ± SE). We
therefore used a pairwise similarity of 0.47 as the maximum attain-
able reference value and, thus, as 100% relative recovery of species
composition. As such, we estimated recovery in species composition
toward a state comparable to old-growth forest. We used the same
reference value of 0.47 for all sites, rather than site-specific reference
values, as many sites had very few old-growth plots to accurately
estimate the within-site average pairwise similarity between old-
growth plots. We do recognize that the species composition of old-
growth forests may also change over time. For example, species
composition may slightly fluctuate in response to short-term drought
or other disturbances, but we cannot predict these changes based on
our data. By including the current (static) species richness and
species composition of old-growth forest as the reference value for
assessing secondary forest recovery, we assume that the species
richness and species composition of old-growth forests remain
stable over time.
The rate of biodiversity recovery may also be influenced by the
size of the regional species pool, as forests with more species may
take more time to recover. We used the size of the local old-growth
forest species pool as a proxy for the regional species pool. As such,
we also accounted for differences in biogeographical history across
sites, particularly since we also included chronosequence sites on
islands (i.e., Providencia Island and Puerto Rico), which may have a
totally different species richness compared to forests with the same
environmental conditions on the mainland. The local species pool
was estimated on the basis of old-growth plots only, as we regarded
old-growth forest as the reference for biodiversity recovery. For
each site, we estimated the size of the local species pool based on the
Chao 1 estimator (32), with bias correction (33), using all stems
from old-growth forest plots. Although we did not estimate the
exact size of the local old-growth species pool, this approach allows
ranking the sites based on the magnitude of their species pool and
evaluating the role of the species pool in biodiversity recovery of
secondary forests.
Data on environmental conditions and previous land use
Average annual rainfall (in mm year−1) was obtained from the
nearest weather station for each site. As seasonality in water avail-
ability is a stronger determinant of species richness and composi-
tion than annual rainfall (16), we obtained CWA (in mm year−1)
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from http://chave.ups-tlse.fr/pantropical_allometry.htm (where CWA
is referred to as “climatic water deficit”). CWA indicates the amount
of water lost by the environment during dry months, that is, the
months in which evapotranspiration is larger than rainfall. CWA is,
by definition, negative, and sites with a maximum CWA of 0 do not
experience seasonal drought stress. For one site for which CWA was
not available (Providencia Island; table S1), we estimated CWA from
a linear regression between CWA and rainfall based on the other
chronosequence sites (CWA = −822 + 0.203 × rainfall; n = 55, P <
0.0001, R2 = 0.49). Topsoil CEC [in cmol(+) kg−1] over the first 30 cm
of the soil was used as an indicator of soil nutrient availability. We
preferably included data from old-growth forest plots because soil
fertility is expected to recover over the course of succession. CEC
represents the amount of exchangeable cations [Ca2+, Mg2+, K+,
Na+, Al3+, and H+ in cmol(+) kg−1]. A high CEC can therefore also
result from high acidity or aluminum toxicity and may not only re-
flect soil fertility. For 39 sites for which no local CEC data were
available, CEC was obtained from the SoilGrids database (34). SoilGrids
did not contain data on soil nitrogen and phosphorus. Phosphorus
is thought to limit plant growth in highly weathered tropical soils and
may therefore be strongly correlated with the biodiversity recovery
of tropical forests. We obtained total exchangeable bases (TEB) from
the World Harmonized Soil Database (35), as this variable was not
included in SoilGrids, for 55 sites for which data were available
(Providencia Island was not included in the database). CEC was
significantly, positively correlated with TEB (Pearson’s r = 0.67, P <
0.0001, n = 55), which indicates that, for our dataset, CEC likely
reflected soil fertility rather than the degree of aluminum toxicity
or acidity. Therefore, we included CEC in the analyses, as for part of
the sites, locally measured values were available, while no local data
were available for TEB.
Biodiversity recovery will likely be highest when seed sources and
seed dispersal agents are nearby, thus with high forest cover and
forest quality in the landscape. For each site, percentage of forest
cover was calculated for each of the plots within circular buffers
with radii of 500, 1000, and 5000 m using a remote sensing–based
tree cover map for the year 2000 (24). For 11 chronosequence sites,
(part of) the fieldwork was conducted in the 1990s, and for the other
sites, the fieldwork was conducted from the year 2000 onward. This
does mean that landscape forest cover in the year 2000 generally
reflects the landscape matrix for the younger secondary forest plots.
Therefore, our estimate of landscape forest cover is ecologically rele-
vant, as it reflects the landscape conditions experienced by younger
secondary forests (<20 years), when most of the recovery of species
richness and species composition occurs (Fig.2).
Tree cover data were available at a resolution of 30 m by 30 m
(24) and included any type of tree cover (e.g., old-growth forest,
secondary forest, and plantations). A threshold of 30% tree cover
was applied per pixel to distinguish between forest and nonforest
land cover types, and forest cover was calculated on the basis of the
number of pixels covered by forest versus nonforest land cover types.
For eight sites without individual plot-level coordinates, we similarly
calculated percentage of forest cover in circular buffers with radii of
500, 1000, and 5000 m based on just the average coordinates of the
site. Landscape-scale forest cover was estimated as percentage of
forest cover in the total area covered by a union of circular buffers
with radii of 500, 1000, or 5000 m of all individual plots within a
chronosequence site. Thus, areas in which circular buffers over-
lapped were included only once in the calculation of percentage of
forest cover in the landscape. In addition, we estimated percentage
of old-growth forest and secondary forest cover in the landscape
matrix (i.e., in a radius of 1 km around the area that comprises all
plots of a chronosequence site) for 45 of our sites (15).
Biodiversity recovery depends on forest legacies that accelerate
secondary forest succession, such as the presence of a soil seed bank,
resprouts from tree roots or stumps, or remnant forest trees. Both
remaining legacies and environmental conditions that influence re-
generation, such as soil nutrient availability and soil structure, are
partly driven by previous land use (13,18). We distinguished three
types of land use before abandonment (shifting cultivation, pasture,
and a combination of these in the landscape) based on interviews
with local landowners. Land-use intensity is generally lowest under
shifting cultivation, resulting in faster forest recovery in abandoned
agricultural fields than abandoned pastures.
Statistical analysis
We modeled absolute recovery of species richness and relative
recovery of species richness and composition as a function of stand
age, CWA, CEC, forest cover in the landscape matrix, previous
land-use type, and plot size using linear mixed-effects models. For
relative recovery of species richness and composition, we also in-
cluded the size of the local old-growth species pool. We did not
include the size of the species pool for absolute recovery of species
richness, as data from old-growth plots were not available for all
sites. Before analysis, stand age was ln-transformed to account for
nonlinear recovery responses over time. Stand age, the size of the
species pool (for relative recovery), CWA, CEC, landscape-scale
forest cover, land-use type, and plot size were included as fixed
effects. To account for the nonindependence of plots within a site
and for site-specific recovery rates, we included a random intercept
and a random slope for stand age per site. In some sites (table S2),
plots had a nested design, where large trees were measured in the
entire plot and smaller trees were measured in a subplot only. To
account for the possibly slightly lower absolute richness of nested
sites, we also included a random intercept for nested versus non-
nested sites for absolute recovery of species richness. For relative
recovery of species richness and composition, inclusion of a random
intercept for nested versus non-nested sites did not improve model
fits based on likelihood ratio tests. Similarly, likelihood ratio tests
indicated that a random intercept for the general region (South
America versus Central America and Mexico) to account for the
overall higher soil fertility in Central America and Mexico did not
improve model fits for absolute recovery of species richness and
relative recovery of species richness and composition. We therefore
did not include a random intercept for the general region in the
models. We compared models with forest cover in circular buffers
with radii of 500, 1000, and 5000 m around the plots based on
Akaike’s information criterion. Models that included forest cover
based on a 5000-m radius were best supported; thus, we included
forest cover within a 5000-m radius in the final models. Including
an interaction between stand age and forest cover to account for
possibly stronger effects of forest cover early in succession did not
improve model fits.
Predictors were not strongly correlated (tables S3 and S4). To be
able to compare effect sizes among predictors, we standardized all
continuous predictors by subtracting the mean and dividing the dif-
ference by 1 SD. To assess the significance of the fixed effects, we
obtained 95% confidence intervals of the model coefficients using
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parametric bootstrapping. Fixed effects were considered significant
if the confidence interval of the coefficients did not overlap with
zero.
For each site, we estimated the recovery time as the time needed
to recover to old-growth species richness and composition based on
the estimated model coefficients, including a site-specific (random)
intercept and slope for stand age. Estimated recovery times may
be extrapolated beyond the maximum stand age of included sec-
ondary forest plots for sites where secondary forests have not fully
recovered to old-growth values yet. All analyses were performed
in R 3.1.2 (36). Rarefied species richness, the Chao-Jaccard index,
and the Chao 1 estimator were calculated using the “vegan” pack-
age (37). Mixed-effects models were performed using the “lme4”
package (38).
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/5/3/eaau3114/DC1
Fig. S1. Relative recovery of species composition [% old-growth (OG)] for Neotropical
secondary forests after 20 years.
Fig. S2. Absolute recovery of species richness, and relative recovery of species richness and
species composition, in relation to CWA, CEC, and forest cover in the landscape matrix for
Neotropical secondary forests.
Fig. S3. Effects of CWA and soil fertility (CEC) on the local old-growth species pool (based on
the Chao 1 estimator) for 45 Neotropical secondary forest sites.
Table S1. Characteristics of the included Neotropical secondary forest sites.
Table S2. Characteristics of the dataset for 56 Neotropical secondary forest sites.
Table S3. Correlations between predictors for 56 Neotropical secondary forest sites.
Table S4. Correlations between predictors for 45 Neotropical secondary forest sites for which
data from old-growth plots were included.
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Acknowledgments: This paper is a product of the 2ndFOR collaborative research network on
secondary forests. We are grateful to numerous field assistants for help with fieldwork, local
institutions for logistical support, and local communities for hospitality. We thank H. de Foresta,
J.-F. Molino, and D. Sabatier for the use of plot data. We thank R. B. Foster, S. Lao, and R. Perez
for the use of plot data, managed under the Center for Tropical Forest Science and the
Smithsonian Tropical Research Institute in Panama. We thank two anonymous reviewers for
insightful comments. This is publication #718 in the Technical Series of the Biological
Dynamics of Forest Fragments Project (BDFFP-INPA). This is publication #4 from 2ndFOR.
Funding: We acknowledge the following agencies for financial support: the Australian
Department of Foreign Affairs and Trade-DFAT, the Biological Dynamics of Forest Fragments
Project (BDFFP), the Blue Moon Foundation, CGIAR-FTA, CIFOR, COLCIENCIAS (grant
no. PRE00503026837, 521, 2010), COLCIENCIAS (grant no. 1243-13-16640), Consejo Nacional
de Ciencia y Tecnología (SEP-CONACYT 2009-129740 and SEP-CONACYT 2015-255544 for
ReSerBos, SEP-CONACYT CB-2005-01-51043 and CB-2009-128136, CONACYT 33851-B, and
SEMARNAT-CONACYT 2002 C01-0597), Conselho Nacional de Desenvolvimento Científico e
Tecnológico (CNPq 481576/2009-6, 563304/2010-3, 562955/2010-0, 574008/2008-0,
308778/2017-0, PQ 306375/2016-8, PQ 307422/2012-7, and PQ 309874/2015-7), FOMIX-Yucatan
(YUC-2008-C06-108863), ForestGEO, Fundação de Amparo à Pesquisa do Estado do Amazonas
(FAPEAM), Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG CRA APQ-00001-11,
PPM-00627-16), Fundación Ecológica de Cuixmala, the Global Environment Facility (GEF-grant
VEN/SGP/2010-2015), the Heising-Simons Foundation, HSBC, the IAI Nitrogen Initiative,
Investissement d’Avenir grant of the Agence Nationale de la Recherche (CEBA: ANR-10-
LABX-25-01), ICETEX, Instituto Internacional de Educação do Brasil (IEB), Instituto Nacional
de Ciência e Tecnologia dos Serviços Ambientais da Amazonia (INCT/Servamb), the
Inter-American Institute for Global Change Research (Tropi-dry network CRN3-025) via a grant
from the U.S. National Science Foundation (grant no. GEO-1128040), Intercolombia, the
International Climate Initiative (IKI) of the German Federal Ministry for the Environment, the
NASA Terrestrial Ecology Program, the National Science Foundation [NSF-CNH-RCN grant
1313788 for Tropical Reforestation Network: Building a Socioecological Understanding of
Tropical Reforestation (PARTNERS), NSF DEB-0129104, NSF DEB-9972116, NSF BCS-1349952,
NSF Career Grant DEB-1053237, NSF DEB-1050957, 0639393, 1147429, 0639114, and 1147434],
Nature Conservation, Building and Nuclear Safety (BMUB), Netherlands Organisation for
Scientific Research (NWO; grant no. NWO-ALWOP.241), the Norwegian Agency for
Development Cooperation (Norad), NUFFIC, PAPIIT-DGAPA-UNAM IN213714 and IN218416,
Science without Borders Program (CAPES/CNPq) (grant no. 88881.064976/2014-01), Stanley
Motta, the Grantham Foundation for the Protection of the Environment, the São Paulo
Research Foundation (FAPESP) (grant nos. 2011/06782-5 and 2014/14503-7), the United
Nations Development Programme (Venezuela), Instituto Nacional de Investigaciones Agrícolas
(INIA-Amazonas), the Silicon Valley Community Foundation, Stichting Het Kronendak, the
Tropenbos Foundation, the University of Connecticut Research Foundation, USAID (BOLFOR),
Wageningen University (INREF Terra Preta programme and FOREFRONT programme), and
Yale-NUS College (grant no. R-607-265-054-121). This study was partly funded by the
European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement
no. 283093 [Role Of Biodiversity In climate change mitigatioN (ROBIN)]. Author contributions:
D.M.A.R., L.P., and F.B. conceived the study and coordinated data compilation. D.M.A.R.
analyzed the data. J.S.D. and L.P.D. contributed to analytical tools used in the analysis.
D.M.A.R., L.P., and F.B. wrote the paper, and all other authors performed fieldwork, provided
suggestions for data analyses, discussed the results, and commented on the manuscript.
Competing interests: The authors declare that they have no competing interests. Data and
materials availability: Plot-level data of 54 sites are available from DNA (https://doi.
org/10.17026/dans-xh8-gh92) and data for two sites can be requested from D.M.A.R.
(danae.rozendaal@wur.nl). Additional data needed to evaluate the conclusions in the paper
are present in the paper and/or the Supplementary Materials.
Submitted 28 May 2018
Accepted 25 January 2019
Published 6 March 2019
10.1126/sciadv.aau3114
Citation: D. M. A. Rozendaal, F. Bongers, T. M. Aide, E. Alvarez-Dávila, N. Ascarrunz, P. Balvanera,
J. M. Becknell, T. V. Bentos, P. H. S. Brancalion, G. A. L. Cabral, S. Calvo-Rodriguez, J. Chave,
R. G. César, R. L. Chazdon, R. Condit, J. S. Dallinga, J. S. de Almeida-Cortez, B. de Jong, A. de Oliveira,
J. S. Denslow, D. H. Dent, S. J. DeWalt, J. M. Dupuy, S. M. Durán, L. P. Dutrieux, M. M. Espírito-Sa nto,
M. C. Fandino, G. W. Fernandes, B. Finegan, H. García, N. Gonzalez, V. G. Moser, J. S. Hall,
J. L. Hernández-Stefanoni, S. Hubbell, C. C. Jakovac, A. J. Hernández, A. B. Junqueira, D. Kennard,
D. Larpin, S. G. Letcher, J.-C. Licona, E. Lebrija-Trejos, E. Marín-Spiotta, M. Martínez-Ramos,
P. E. S. Massoca, J. A. Meave, R. C. G. Mesquita, F. Mora, S. C. Müller, R. Muñoz, S. N. de Oliveira Net o,
N. Norden, Y. R. F. Nunes, S. Ochoa-Gaona, E. Ortiz-Malavassi, R. Ostertag, M. Peña-Claros,
E. A. Pérez-García, D. Piotto, J. S. Powers, J. Aguilar-Cano, S. Rodriguez-Buritica, J. Rodríguez-Velazqu ez,
M. A. Romero-Romero, J. Ruíz, A. Sanchez-Azofeifa, A. S. de Almeida, W. L. Silver, N. B. Schwartz,
W. W. Thomas, M. Toledo, M. Uriarte, E. V. de Sá Sampaio, M. van Breugel, H. van der Wal, S. V. M artins,
M. D. M. Veloso, H. F. M. Vester, A. Vicentini, I. C. G. Vieira, P. Villa, G. B. Williamson, K. J. Zanini,
J. Zimmerman, L. Poorter, Biodiversity recovery of Neotropical secondary forests. Sci. Adv. 5,
eaau3114 (2019).
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Biodiversity recovery of Neotropical secondary forests
Zimmerman and Lourens Poorter
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Maria Uriarte, Everardo Valadares de Sá Sampaio, Michiel van Breugel, Hans van der Wal, Sebastião Venâncio Martins, Maria
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DOI: 10.1126/sciadv.aau3114
(3), eaau3114.5Sci Adv
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REFERENCES http://advances.sciencemag.org/content/5/3/eaau3114#BIBL
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