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More than 10000 pre-Columbian earthworks are still hidden throughout
Amazonia
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ANTHROPOLOGY
More than 10,000 pre-Columbian earthworks are still
hidden throughout Amazonia
Vinicius Peripato et al.
Indigenous societies are known to have occupied the Amazon basin for more than 12,000 years,
but the scale of their influence on Amazonian forests remains uncertain. We report the discovery, using
LIDAR (light detection and ranging) information from across the basin, of 24 previously undetected
pre-Columbian earthworks beneath the forest canopy. Modeled distribution and abundance of large-scale
archaeological sites across Amazonia suggest that between 10,272 and 23,648 sites remain to be
discovered and that most will be found in the southwest. We also identified 53 domesticated tree species
significantly associated with earthwork occurrence probability, likely suggesting past management
practices. Closed-canopy forests across Amazonia are likely to contain thousands of undiscovered
archaeological sites around which pre-Columbian societies actively modified forests, a discovery
that opens opportunities for better understanding the magnitude of ancient human influence on
Amazonia and its current state.
During the pre-Columbian era, Amazonia
was home to dense and complex socie-
ties throughout its vast forested area
spanning 6.7 million km
2
(1). These an-
cient Indigenous societies had profound
knowledge of earthmoving, riverine dynamics,
soil enrichment, and plant and animal ecology,
which allowed them to create domesticated
landscapes that were more productive for hu-
mans (2–4). With earthmoving techniques, In-
digenous peoples created a wide variety of
earthworks (i.e., ring ditches, geoglyphs, ponds,
and wells), mostly between 1500 and 500 years
before present, with social, ceremonial, and
defensive functions (5). Around these earth-
works, they also managed hundreds of tree
species, some of which show evidence of do-
mestication (6–9), and effected long-lasting
changes in forest composition (10–13). The
scale and intensity of that landscape trans-
formation remain unknown, in part because
there has never been a comprehensive in-
ventory of pre-Columbian sites across the
basin.
Domesticated landscapes in Amazonia have
mostly been discovered by means of evidence
from on-the-ground surveys (5,14). Earthworks
can be detected by orbital optical satellites with
very high spatial resolution (15),butthattech-
nique is mostly suitable for deforested areas (16).
Airborne light detection and ranging (LIDAR)
data—a remote sensing technique that can map
microtopography beneath the forest canopy—
has substantially changed our understanding
of the magnitude of pre-Columbian urbanism
in Mesoamerica (17,18)andSouthAmerica
(19). Over the past decade, the use of LIDAR
data has revealed the complexity of Mayan
civilization by indicating a regionally inte-
grated urban-rural community network in
Mesoamerica (17). More recently, LIDAR en-
abled the detailed mapping of two monumen-
talpre-Columbiansettlementsinanintensively
domesticated landscape hidden under forest in
southwestern Amazonia (19). Although Meso-
american archaeological sites feature very
different types of structures—stone construction
as opposed to the use of earth, as in Amazonia—
LIDAR technology has substantially improved
our spatial understanding of archaeological
sites in forested landscapes by enabling the
visualization of ancient large-scale earthworks
(18,19) beneath the forest canopy. Because de-
forestationinAmazoniahasremovedabout
17% of the natural vegetation cover to date
(20), LIDAR has the potential to reveal many
more discoveries in the remaining 83% of the
basin that is opaque to other remote sensing
approaches.
Here, we report a large number of previous-
ly undocumented pre-Columbian earthworks
with geometrically patterned enclosures in an
Amazon-wide LIDAR dataset covering 0.08%
of the basin (21).Wecombinethesenewlydis-
covered sites with a comprehensive dataset
of existing archaeological sites (ring ditches,
geoglyphs, ponds, and wells) to model areas
likely to harbor as yet undetected earthworks
hidden beneath remote forest landscapes. On
thebasisofourpredictivemodel,weestimate
the number of undocumented earthworks and
identify domesticated tree species associated
with earthwork presence.
Archaeological discoveries beneath the canopy
Scanning 5315 km
2
of LIDAR data originally
obtained for estimating aboveground biomass
throughout the Amazonian forest (22) revealed
RESEARCH
All authors and affiliations are listed at the end of this Research
Article.
Fig. 1. Geographical distribution of known and newly discovered pre-Columbian geometric earth-
works in Amazonia. (A) Map of previously reported and newly discovered earthworks (purple circles and
yellow stars, respectively) reported in this study across six Amazonian regions: central Amazonia (CA),
eastern Amazonia (EA), Guiana Shield (GS), northwestern Amazonia (NwA), southern Amazonia (SA),
and southwestern Amazonia (SwA). (B) Newly discovered earthworks in SA. (Cto F) Newly discovered
earthworks in SwA. (Gto I) Newly discovered earthworks in GS. (Jand K) Newly discovered earthworks
in CA. Scale bars, 100 m.
Peripato et al., Science 382, 103–109 (2023) 6 October 2023 1of6
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24 unreported earthworks in southern, south-
western, central, and northern (the Guiana
Shield)Amazonia(Fig.1A)(21). We detected a
fortified village in southern Amazonia (Fig. 1B),
defensive and ceremonial sites in southwestern
Amazonia(Fig.1,CtoF),crownedmountains
and megalithic structures in the Guiana Shield
(Fig. 1, G to I), and riverine sites on floodplains
in central Amazonia (Fig. 1, J and K).
In southern Amazonia, we found an ancient
plaza town located in the Upper Xingu Basin
(Fig. 1B). This region is known to have sup-
ported dense populations in the past, distrib-
uted throughout plaza villages interconnected
by road networks and surrounded by domes-
ticated landscapes with a diverse array of ter-
restrial and aquatic resources (10,23). It is also
clear that the earthworks in this region extend
beyond the sampled area of the 200-m-wide
LIDAR transect, restraining their full iden-
tification. The layout of these earthworks is
similar to that of other fortified villages doc-
umentedinthisregion,whichsupportstheidea
that these structures were built before European
contact (10,15,24).
In southwestern Amazonia, we found a com-
bination of rectangular and circular features,
known as geoglyphs, without detectable in-
terconnecting roads occurring on flat terrain
close to water bodies (Fig. 1, C to F). Docu-
mented defensive and ceremonial earthworks
in this region were built around two millennia
ago and are dispersed across the well-drained
plateaus of the tributaries of the Purus and
Madeira rivers (25).
In the Guiana Shield, we detected a combi-
nation of rectangular and circular features on
plateaus near water bodies (Fig. 1, G to I). The
region holds different types of earthworks with
different usages: permanent settlements within
crowned mountains in French Guiana (26)and
ceremonial sites featuring megalithic struc-
tures arranged in circular clusters found along
the coast of Amapá, Brazil (27).
In the floodplains of central Amazonia, a
hotspot of pre-Columbian riverine settlements
(3,23,28), we identified two other earthworks
(Fig. 1, J and K). We considered these sites to
be anthropogenic because of their straight
edges, although the geometry of these sites is
distinct from that of the earthworks found in
upland forests. Constant sedimentary deposi-
tion over the centuries, through periodic floods,
mayhaveburiedsmallerfeatures,preserving
only the observed structures, which elsewhere
have been associated with pre-Columbian fish-
eries management (29).
Modeling basin-wide distribution of earthworks
By extrapolating the density of earthworks ob-
served in our LIDAR data (0.0062 earthworks/
km
2
) to the extent of Amazonia (6.7 million km
2
),
we calculated that >41,000 earthworks may
occur throughout the forest. However, given
that our LIDAR data covered only 0.08% of the
total area of Amazonia and that earth-building
societies were not evenly distributed across the
basin (15,30), more-rigorous methods were
needed to estimate how many other as yet un-
documented pre-Columbian earthworks might
occur and where. To answer these questions,
we used newly developed Bayesian statistical
techniques and an inhomogeneous Poisson
process (IPP) model (31), with an intensity func-
tion using intensity covariates and thinned by
observability covariates (32). Recently, the use
of other machine learning techniques such as
random forests have become popular for spe-
cies distribution models (SDMs). There is still
some uncertainty about this use (33), and the
implementation of random forests to IPPs is
still not available, but it might be a welcome
addition to the toolkit of SDM analysis.
The aforementioned statistical analysis was
based on the records of 937 known earthworks
complemented by our discoveries (24 earth-
works), with three bioclimatic, three edaphic,
and three topographic variables as intensity
covariates. More than 40 variables were con-
sidered in the model (table S1), and the se-
lec te d ones (nine variables) cover gradients of
temperature, precipitation, soil structure and
fertility, topography, water-table depth, and
distance to water bodies (21). Observability
Fig. 2. Probability model of pre-Columbian earthworks across Amazonia. (A) Predicted probability of
earthwork presence for 1-km
2
cells across six Amazonian regions using an inhomogeneous Poisson process
predictive model: central Amazonia (CA), eastern Amazonia (EA), Guiana Shield (GS), northwestern Amazonia
(NwA), southern Amazonia (SA), and southwestern Amazonia (SwA). Areas not modeled (NA) are greyed
out. (B) Predictive probability function for the number of as yet undetected earthworks; the dark area
under the curve represents the credibility interval (CI) of the probabilities associated with each number.
(C) Boxplot of the estimated relative contribution of each covariate; the yellow diamond indicates the mean
value. SCC, soil cation concentration; TPI, terrain position index; HAND, height above the nearest drainage.
(D) Individual predicted probability of earthwork presence against intensity covariates. For projected areas
across each Amazonian region on different probability thresholds, see table S2, and for the IPP model
on continuous values, see fig. S1.
RESEARCH |RESEARCH ARTICLE
Peripato et al., Science 382, 103–109 (2023) 6 October 2023 2of6
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covariates were used to describe the dataset
sample preference by indicating the most fa-
vorable location for sample acquisition (32).
The effect of sample selection bias was individ-
ually weighted for each sample (21).
Our model predicts the number of as yet
undiscovered pre-Columbian structures at be-
tween 10,272 and 23,648, with 95% probabil-
ity, giving an average of 16,187 sites (Fig. 2B).
These estimates suggest that the earthworks
already documented in the Amazon to date
account for a mere 4 to 9% of the total, and
that 91 to 96% of Amazonian earthworks re-
main undiscovered.
This predictive model indicated that earth-
works are likely concentrated in southwestern
Amazonia (Fig. 2A) and corroborated previous
studies that found this region to be a hotspot
of earth-building societies (13,15,34). In addi-
tion, nearly all the highest-probability cells (≥25%
predicted probability) occur in a 94,713-km
2
rectangle that overlays a substantial portion of
the Brazilian state of Acre. Indeed, southwest-
ern Amazonia contains the earliest plant cul-
tivation and domestication (9,35), the oldest
anthropogenic soils (35), low-density urban-
ism (19), and now a much higher density of
earthworks. The underlying spatial data dis-
tribution may offer valuable information about
pre-Columbian practices before European con-
tact (36).
Our analysis also suggests that pre-Columbian
societies engaged in earthwork construction
in all other regions, covering a broader area
than previously thought. However, earthworks
are heterogeneously distributed across Ama-
zonian regions. Almost 80% of the basin has
a 0 to 1% predicted probability of earthwork
presence for 1-km
2
cells. These low-probability
areas are mostly located in northwestern, north-
ern, and central Amazonia, whereas higher-
probability areas (≥25% predicted probability,
covering 1.41% of the basin) are located in
southwestern Amazonia. Earth-building soci-
eties were very common in some parts of the
basin, but they may not have occupied all of
Amazonia (6,15,30,37). Other types of domes-
ticated landscapes, such as Amazonian dark
earths, are widespread [see maps in (37–39)]
in regions (e.g., central Amazonia) where the
earthworks analyzed in our study (ring ditches,
geoglyphs, ponds, and wells) are not commonly
found. Given the diversity of pre-Columbian
societies and their land-use practices over
12,000 years of ancient Amazonian history,
forests were likely modified at varying inten-
sities by different Indigenous populations
through time (7,38).
Forests modified by earth-building societies
aremorelikelytooccurinlocationswithhigh
temperature and low precipitation during the
wettest and driest quarters (Fig. 2, C and D).
Areaswithhighsoilcontentofclayandsilt
and high cation concentrations also show high
probabilities of earthwork presence. In ad-
dition, earthworks tend to be located on pla-
teaus with deep water tables, yet close to water
bodies. This combination of environmental
conditions probably facilitated the construc-
tion of earthworks by offering periods with
less precipitation and higher temperature,
and soils with a better texture for earthmoving.
In addition, the presence of a drier season fa-
cilitates burning, which could help remove the
vegetation for building earth structures (12),
while higher soil cation concentrations could
attract settlements for the development of di-
versified food production systems with plants
Fig. 3. Significant relationships between the occurrence and abundance of domesticated tree species
and the modeled distribution of earthworks in Amazonia. Point estimates and confidence intervals of
species significantly associated with predicted probability of earthwork presence, with an overall significance level
of 5%. Positive species are more likely to occur and be abundant where predicted probability of earthwork
presence is high, whereas negative species are less likely to occur and be abundant there.
RESEARCH |
Peripato et al., Science 382, 103–109 (2023) 6 October 2023 3of6
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managed and domesticated to different de-
grees (15,30).
As expected, observability covariates indi-
cate that previously reported earthworks are
mostly found near roads, which facilitate
field research (Fig. 2C). Tree cover, however,
has no effect on the current distribution of
earthworks. Thus, new earthworks can still be
found even in deforested areas. The use of con-
ventional very-high-resolution remote sensing
data, guided by the probability surfaces pro-
duced here (Fig. 2A), is likely to reveal more
previously undetected earthworks in both
closed-canopy and deforested areas of Ama-
zonia. In addition, the rise of machine learn-
ing techniques applied to archaeological site
detection may lead to rapid discovery of new
sites across deforested areas (40,41).
In forested areas, LIDAR surveys guided by
our discoveries (e.g., full coverage of the Fig. 1B
site) and the probability surfaces in Fig. 2A are
promising tools for discovering new sites.
However, very-high-probability areas (≥50%
predicted probability) cover 32,120 km
2
,for
which a complete LIDAR survey would require
six times more data than have been collected
to date in the Amazon. Thus, other approaches,
such as mapping the distribution and abun-
dance of domesticated species associated with
earthwork presence, may help locate new sites
within the Amazonian forest (42,43).
Relationships with domesticated species
We analyzed the relationship between the re-
sponse (occurrence and abundance) of 79 do-
mesticated tree species identified across 1676
forest plots (6) and the predicted probability
of earthwork presence using generalized linear
models to test whether forests with a higher
probability of earthwork presence have a higher
frequency and abundance of domesticated
species (21). The occurrence and/or abundance
of 35 domesticated species increased with the
predicted probability of earthwork presence,
while those of 18 species decreased. In total,
the occurrence and/or abundance of 53 of the
79 domesticated species showed significant
association with the predictive model of earth-
work distribution (Fig. 3).
The species whose responses increased the
most significantly along with the probability of
earthwork occurrence are Bertholletia excelsa
(P< 0.001, b=1.13),Hevea brasiliensis (P<
0.001, b=0.65),andBrosimum alicastrum (P<
0.001, b= 1.36), on the basis of occurrence data,
and Astrocaryum murumuru (P< 0.001, b=
0.71), Attalea phalerata (P<0.001,b=1.42),and
Theobroma cacao (P< 0.001, b=1.43),onthe
basis of abundance data (fig. S2). The species
whose responses decreased the most signifi-
cantly are Erisma japura (P< 0.001, b=–1.94),
on the basis of occurrence data, and E. japura
(P< 0.001, b=–1.7) and Oenocarpus bataua (P<
0.001, b=–0.27), on the basis of abundance data
(fig. S2). Although these highlighted species have
multiple uses (44), they have mainly been used
for their edible fruits and nuts in Amazonia,
with the exception of H. brasiliensis, which
has been used intensively for latex produc-
tion (data S1). Species that are more frequent
and abundant in forests with higher probability
of earthwork occurrence were probably fa-
vored by a combination of interacting past
Indigenous management practices and eco-
logical processes (6). These results confirm
previous archaeobotanical and ethnobotanical
data that have already shown that some spe-
cies (e.g., B. excelsa,Astrocaryum spp., and
Attalea spp.) are more abundant on and near
archaeological sites across Amazonia (8,14,36).
Species that are less frequent and abundant in
areas with a higher probability of earthwork
occurrence likely prefer habitats where earth-
works are usually not found, such as sandy soils
with lower fertility (7), or were disfavored by
past practices that might have had detrimen-
tal effects on some species (45).
Social-ecological implications
The massive extent of archaeological sites and
widespread human-modified forests across
Amazonia is critically important for establishing
an accurate understanding of interactions be-
tween human societies, Amazonian forests, and
Earth’sclimate(37). Considering the widespread
extent of locations modified by pre-Columbian
management and cultivation practices, Amazonia
can be viewed as an ancient social-ecological sys-
tem, with long-term responses to climate change
(46), more similar to old secondary forests than
pristine climax ecosystems (10).
The discovery of earthworks hidden beneath
dense forest canopies also indicates that, given
sufficient time after these sites became depop-
ulated, forests regenerated over the centuries.
It is still unknown, however, the scale of struc-
tural and floristic differences between pristine
and domesticated forests across Amazonia. The
forest reclaimed the land, but this is not the
case for the Indigenous societies that managed
these forests and waterbodies and that created
these large structures. These archaeological leg-
acies can play a role in present-day debates
around Indigenous territorial rights. They serve
astangibleproofofanancestor’s occupation,
way of life, and their relationship with the for-
est. Today, Indigenous peoples struggle to re-
cognize their right to land originally inhabited
by their ancestors, along with the protection
of their territories, languages, cultures, and heri-
tages. In addition to protecting the native peo-
ples that remain, the institution of Indigenous
lands also collaborates with forest conservation
in times of debates on climate change and the
search for solutions that minimize impacts on
the climate and promote carbon neutrality.
These human-modified landscapes harbor
an impressive archaeological heritage. Of the
24 earthworks newly reported in our study,
50% are located in areas with some degree of
legal protection. When all 937 known earth-
works are considered, however, only 9% are
located inside Indigenous lands and protected
areas. To date, most pre-Columbian earth-
works have been discovered after deforesta-
tion. The highest density of known earthworks
in Amazonia is, therefore, outside protected
areas and mostly located in the region with
the highest historical and current rates of de-
forestation, called the “Arc of Deforestation.”
Protected areas and Indigenous territories can
act as barriers against illegal activities that
promote the degradation and destruction of
Amazonia’s natural and cultural heritage, but
their implementation and expansion depend
on strong government policies and law en-
forcement (47,48).
Ironically, modern-day deforestation is re-
moving the very evidence of pre-Columbian
land-use strategies that were able to trans-
form the landscape without causing large-
scale deforestation (13). Today, Amazonia is
experiencing expansion of agriculture and
cattle ranching (49,50), especially where earth-
works are concentrated in the southern and
southwestern regions, risking the destruction
of earthworks and fracturing and hampering
the identification of pre-Columbian occupation
sites that provide direct evidence of ancient
Indigenous territories. Our data on earthwork
probability, suitable environmental conditions,
and associated domesticated species should
narrow the search for Indigenous heritage
sites, enhanced by optical and LIDAR sensing
to identify, monitor, and help conserve archae-
ological features. Amazonian forests clearly
merit protection not only for their ecological
and environmental value but also for their
high archaeological, social, and biocultural
value, which can teach modern society how
to sustainably manage its natural resources.
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Zenodo (2023); https://doi.org/10.5281/zenodo.7750985.
ACKNOWL EDGME NTS
This paper is the result of the work of hundreds of different
scientists and research institutions in the Amazon over the past
80 years. Without their hard work, this analysis would have been
impossible. We thank members of the following projects and
groups for providing data and support: Sustainable Landscapes
Brazil project; Center for Science of the Terrestrial System;
TRopical Ecosystems and Environmental Sciences; Amazon Tree
Diversity Network; Amazonian Archaeological Sites Network;
Pre-Columbian Amazon-Scale Transformations, Biodiversity Research
Program in Western Amazon Center for Integrated Studies of
Amazonian Biodiversity (PPBio-AmOc/INCT-CENBAM); and the
Brazilian Space Agency (AEB). Funding: V.P. and C.L. were
supported by the Coordination of Superior Level Staff Improvement
under Academic Excellence Program (CAPES/PROEX) research
grants (1681023, 88887.479608/2020, and 88887.474568/2020);
C.L. was supported by National Council for Scientific and
Technological Development (CNPQ) grants (159440/2018-1, and
400369/2021-4); D.Gam. was supported by a CNPQ grant
(304742/2018-0); A.B.J. was supported by the European Research
Council (ERC) under a Consolidator Grant (FP7-771056-LICCI); J.P.H.
B.O. was supported by a São Paulo Research Foundation (FAPESP)
grant (2017/22269-2) and Airborne LASER Scanning data acquisition
by the Amazon Fund grant (14.2.0929.1); H.L.G.C. was supported by a
FAPESP grant (18/14423-4); H.t.S., V.H.F.G., and R.S. were supported
by a PVE-MEC/MCTI/CAPES/CNPq/FAPs grant (407232/2013-3);
J.G.d.S., J.I., and M.Rob. were supported by Horizon 2020 grants
(ERC Cog 616179 and ERC PoC_777845); A.M.d.S. was supported by
a CAPES grant (88887.607664/2021); D.S., J.-F.M., J.E., P.P., and
J.C. were supported by an ANR grant (CEBA 10-LABX-25-01); H.L.d.Q.
and J.L.L.M. were supported by MCT/CNPq/CT-INFRA/GEOMA
grants (550373/2010-1 and 457515/2012-0); J.L.L.M. was
supported by a CAPES/PDSE grant (88881.135761/2016-01) and a
CAPES/Fapespa grant (1530801); E.M.V. was supported by a
CNPq grant (308040/2017-1); B.M.F. was supported by a FAPESP
grant (2016/25086-3); B.S.M., B.H.M.-J., and O.L.P. were
supported by a CNPq/CAPES/FAPS/BC-Newton grant (441244/
2016-5), a FAPEMAT grant (0589267/2016), and a Royal Society
GCRF International Collaboration Award (ICA\R1\180100);
T.W.H. was supported by an NSF/DEB grant (1556338); L.E.O.C.A.
was supported by a CNPQ/PQ grant (314416/2020-0). Floristic
identification in plots in the RAINFOR forest monitoring network
and plot data management by ForestPlots.net have been
supported by several Natural Environment Research Council grants
to O.L.P. and colleagues (NE/B503384/1, NE/D01025X/1,
NE/I02982X/1, NE/F005806/1, NE/D005590/1, NE/I028122/1,
and NE/S011811/1) and the Gordon and Betty Moore Foundation.
Author contributions: Conceptualization: V.P., C.L., and L.E.O.C.A.
LIDAR raw data processing: V.P. Earthworks investigation: V.P.,
J.G.d.S., J.I., M.Rob., and L.E.O.A.C. Modeling: G.A.M. and D.Gam.
Domestication investigation: V.P. and C.L. Writing –original draft:
V.P., C.L., and L.E.O.A.C. Writing –review & editing: V.P., C.L.,
G.A.M., D.Gam., N.C.A.P., and L.E.O.A.C. All of the other authors
contributed data, discussed further analyses, and commented on
various versions of the manuscript. Competing interests: The
authors declare that they have no competing interests. Data and
materials availability: Data from publicly available sources are
cited in thesupplementarymaterials. Other data and computer codes
used in the analysis are publicly available in Zenodo (51). License
information: Copyright © 2023 the authors, some rights reserved;
exclusive licensee American Association for the Advancement of
Science. No claim to original US government works. https://www.
science.org/about/science-licenses-journal-article-reuse
Authors:
Vinicius Peripato
1
*†, Carolina Levis
2
†, Guido A. Moreira
3
,
Dani Gamerman
4
, Hans ter Steege
5
, Nigel C. A. Pitman
6
,
Jonas G. de Souza
7
, José Iriarte
8
, Mark Robinson
8
,
André Braga Junqueira
9
,ThiagoB.Trindade
10
, Fernando O. de Almeida
11
,
Claide de Paula Moraes
12
, Umberto Lombardo
13
, Eduardo K. Tamanaha
14
,
Shira Y. Maezumi
15
,JeanP.H.B.Ometto
1
,JoséR.G.Braga
1
,
Wesley A. Campanharo
1
, Henrique L. G. Cassol
1
, Philipe R. Leal
1
,
Mauro L. R. de Assis
1
, Adriana M. da Silva
16
, Oliver L. Phillips
17
,
Flávia R. C. Costa
18
, Bernardo Monteiro Flores
2
, Bruce Hoffman
19
,
Terry W. Henkel
20
, Maria Natalia Umaña
21
, William E. Magnusson
18
,
Elvis H. Valderrama Sandoval
22,23
, Jos Barlow
24
, William Milliken
25
,
Maria Aparecida Lopes
26
, Marcelo Fragomeni Simon
27
,
Tinde R. van Andel
5,28
, Susan G. W. Laurance
29
, William F. Laurance
29
,
Armando Torres-Lezama
30
, Rafael L. Assis
31
, Jean-François Molino
32
,
Mickaël Mestre
33
, Michelle Hamblin
34
, Luiz de Souza Coelho
35
,
Diogenes de Andrade Lima Filho
35
, Florian Wittmann
36,37
,
Rafael P. Salomão
38,39
, Iêda Leão Amaral
35
,
Juan Ernesto Guevara
40,41
, Francisca Dionízia de Almeida Matos
35
,
Carolina V. Castilho
42
, Marcelo de Jesus Veiga Carim
43
,
Dairon Cárdenas López
44
‡,DanielSabatier
32
,
Mariana Victória Irume
35
, Maria Pires Martins
35
,
José Renan da Silva Guimarães
45
,OlafS.Bánki
5
,
Maria Teresa Fernandez Piedade
37
, José Ferreira Ramos
35
,
Bruno Garcia Luize
46
, Evlyn Márcia Moraes de Leão Novo
1
,
Percy Núñez Vargas
47
, Thiago Sanna Freire Silva
48
,
Eduardo Martins Venticinque
49
, Angelo Gilberto Manzatto
50
,
Neidiane Farias Costa Reis
51
, John Terborgh
52,29
, Katia Regina Casula
51
,
Layon O. Demarchi
37
, Euridice N. Honorio Coro nado
53,54
,
Abel Monteagudo Mendoza
47,55
,JuanCarlosMontero
56,35
,
Jochen Schöngart
37
,TedR.Feldpausch
57,17
, Adriano Costa Quaresma
36,37
,
Gerardo A. Aymard C.
58
, Chris Baraloto
59
, Nicolás Castaño Arboleda
44
,
Julien Engel
32,59
,PascalPetronelli
60
, Charles Eugene Zartman
35
,
Timothy J. Killeen
61
, Beatriz S. Marimon
62
, Ben Hur Marimon-Junior
62
,
Juliana Schietti
35
, Thaiane R. Sousa
63
,RodolfoVasquez
55
,
Lorena M. Rincón
35
,ErikaBerenguer
64,24
, Joice Ferreira
65
,
Bonifacio Mostacedo
66
, Dário Dantas do Amaral
39
, Hernán Castellanos
67
,
Marcelo Brilhante de Medeiros
27
,AnaAndrade
68
, José Luís Camargo
68
,
EmanuelledeSousaFarias
69,70
, José Leonardo Lima Magalhães
71,65
,
Henrique Eduardo Mendonça Nascimento
35
,HelderLimadeQueiroz
72
,
Roel Brienen
17
, Juan David Cardenas Revilla
35
, Pablo R. Stevenson
73
,
Alejandro Araujo-Murakami
74
, Bruno Barçante Ladvocat Cintra
75
,
Yuri Oliveira Feitosa
76
, Flávia Rodrigues Barbosa
77
,
Rainiellen de Sá Carpanedo
77
, Joost F. Duivenvoorden
78
,
Janaína da Costa de Noronha
77
, Domingos de Jesus Rodrigues
77
,
Hugo F. Mogollón
79
, Leandro Valle Ferreira
39
, John Ethan Householder
36
,
José Rafael Lozada
80
, James A. Comiskey
81,82
, Freddie C. Draper
8382
,
José Julio de Toledo
84
, Gabriel Damasco
85
, Nállarett Dávila
46
§,
Roosevelt García-Villacorta
86,87
, Aline Lopes
88
, Fernando Cornejo Valverde
89
,
Alfonso Alonso
82
, Francisco Dallmeier
82
, Vitor H. F. Gomes
90,91
,
Eliana M. Jimenez
92
,DavidNeill
93
, Maria Cristina Peñuela Mora
94
,
Daniel P. P. de Aguiar
95,96
,LuzmilaArroyo
74
, Fernanda AntunesCarvalho
18,97
,
Fernanda Coelho de Souza
18,17
, Kenneth J. Feeley
98,99
, Rogerio Gribel
35
,
Marcelo Petratti Pansonato
35,100
,MarcosRíosParedes
101
,
Izaias Brasil da Silva
102
, Maria Julia Ferreira
103
,PaulV.A.Fine
104
,
Émile Fonty
105,32
, Marcelino Carneiro Guedes
106
, Juan Carlos Licona
56
,
Toby Pennington
57,107
, Carlos A. Peres
108
, Boris Eduardo Villa Zegarra
109
,
Germaine Alexander Parada
74
, Guido Pardo Molina
110
,
Vincent Antoine Vos
110
, Carlos Cerón
111
, Paul Maas
5
,
Marcos Silveira
112
, Juliana Stropp
113
, Raquel Thomas
114
,
Tim R. Baker
17
, Doug Daly
115
, Isau Huamantupa-Chuquimaco
116
,
Ima Célia Guimarães Vieira
39
, Bianca Weiss Albuquerque
37
,
Alfredo Fuentes
117,118
, Bente Klitgaard
119
, José Luis Marcelo-Peña
120
,
Miles R. Silman
121
, J. Sebastián Tello
118
, Corine Vriesendorp
6
,
Jerome Chave
122
, Anthony Di Fiore
123,124
, Renato Richard Hilário
84
,
Juan Fernando Phillips
125
, Gonzalo Rivas-Torres
124,126
,
Patricio von Hildebrand
127
, Luciana de Oliveira Pereira
57
,
Edelcilio Marques Barbosa
35
, Luiz Carlos de Matos Bonates
35
,
Hilda Paulette Dávila Doza
101
, Ricardo Zárate Gómez
128
,
George Pepe Gallardo Gonzales
101
, Therany Gonzales
129
,
Yadvinder Malhi
130
, Ires Paula de Andrade Miranda
35
,
Linder Felipe Mozombite Pinto
101
, Adriana Prieto
131
,
Agustín Rudas
131
, Ademir R. Ruschel
65
, Natalino Silva
132
,
César I. A. Vela
133
, Egleé L. Zent
134
, Stanford Zent
134
,
Angela Cano
73,135
, Yrma Andreina Carrero Márquez
136
,
Diego F. Correa
73,137
, Janaina Barbosa Pedrosa Costa
106
,
David Galbraith
17
, Milena Holmgren
138
, Michelle Kalamandeen
139
,
Guilherme Lobo
37
, Marcelo Trindade Nascimento
140
,
Alexandre A. Oliveira
100
, Hirma Ramirez-Angulo
30
, Maira Rocha
37
,
Veridiana Vizoni Scudeller
141
, Rodrigo Sierra
142
, Milton Tirado
142
,
Geertje van der Heijden
143
, Emilio Vilanova Torre
30,144
,
Manuel Augusto Ahuite Reategui
145
, Cláudia Baider
146,100
,
Henrik Balslev
147
, Sasha Cárdenas
73
, Luisa Fernanda Casas
73
,
William Farfan-Rios
47,118,148
, Cid Ferreira
35
, Reynaldo Linares-Palomino
82
,
Casimiro Mendoza
149,150
,ItaloMesones
104
, Ligia Estela Urrego Giraldo
151
,
Daniel Villarroel
74,152
, Roderick Zagt
153
,MiguelN.Alexiades
154
,
Edmar Almeida de Oliveira
62
, Karina Garcia-Cabrera
121
,
Lionel Hernandez
67
, Walter Palacios Cuenca
155
, Susamar Pansini
51
,
Daniela Pauletto
156
, Fredy Ramirez Arevalo
23
, AdeilzaFelipe Sampaio
51
,
Luis Valenzuela Gamarra
55
,LuizE.O.C.Aragão
1,57
*
1
Division of Earth Observation and Geoinformatics, General Coordina-
tion of Earth Sciences, National Institute for Space Research (INPE),
São José dos Campos, SP, Brazil.
2
Postgraduate Program in Ecology,
Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil.
3
Centre of Molecular and Environmental Biology, Universidade do
Minho, Braga, Portugal.
4
Departamento de Métodos Estatísticos,
Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ,
Brazil.
5
Naturalis Biodiversity Center, Leiden, Netherlands.
6
Quantita-
tive Biodiversity Dynamics, Utrecht University, Utrecht, Netherlands.
7
Science and Education, The Field Museum, Chicago, IL, USA.
8
Department of Humanities, Universitat Pompeu Fabra, Barcelona,
Spain.
9
Department of Archaeology, College of Humanities, University
of Exeter, Exeter, UK.
10
Institut de Ciència i Tecnologia Ambientals,
Universitat Autònoma de Barcelona, Barcelona, Spain.
11
Instituto do
Património Histórico e Artístico Nacional (IPHAN), Centro Nacional de
Arqueologia(CNA),Brasília,DF,Brazil.
12
Departamento de Arqueologia,
Universidade Federal de Sergipe (UFS), Laranjeiras, SE, Brazil.
13
Programa de Antropologia e Arqueologia, Universidade Federal do
Oeste do Pará (UFOPA), Santarém, PA, Brazil.
14
Geographisches
Institut, University of Bern, Bern, Switzerland.
15
Instituto de
Desenvolvimento Sustentável Mamirauá, Tefé, AM, Brazil.
16
Department
of Archaeology, Max Planck Institute of Geoanthropology, Jena,
Germany.
17
Postgraduate Program in Geography, Institute of Geography,
Federal University of Uberlândia (UFU), Uberlândia, MG, Brazil.
18
School of Geography, University of Leeds, Leeds, UK.
19
Coordenação
de Pesquisas em Ecologia, Instituto Nacional de Pesquisas da
Amazônia (INPA), Manaus, AM, Brazil.
20
Amazon Conservation Team,
Arlington, VA, USA.
21
Department of Biological Sciences, Humboldt
State University, Arcata, CA, USA.
22
Department of Ecology and
Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.
23
Department of Biology, University of Missouri, St. Louis, MO, USA.
24
Facultad de Biologia, Universidad Nacional de la Amazonia Peruana,
Iquitos, Loreto, Peru.
25
Lancaster Environment Centre, Lancaster
RESEARCH |
Peripato et al., Science 382, 103–109 (2023) 6 October 2023 5of6
Downloaded from https://www.science.org on October 05, 2023
University, Lancaster, Lancashire, UK.
26
Department for Ecosystem
Stewardship, Royal Botanic Gardens, Richmond, Surrey, UK.
27
Instituto de Ciências Biológicas, Universidade Federal do Pará (UFPA),
Belém, PA, Brazi l.
28
Embrapa Recursos Genéticos e Biotecnologia,
Parque Estação Biológica, Prédio da Botânica e Ecologia, Brasilia, DF,
Brazil.
29
Biosystematics Group, Wageningen University, Wageningen,
Netherlands.
30
Centre for Tropical Environmental and Sustainability
Science and College of Science and Engineering, James Cook
University,Cairns, QLD, Australia.
31
Instituto de Investigaciones para el
Desarrollo Forestal (INDEFOR), Universidad de los Andes, Conjunto
Forestal, Mérida, Mérida, Venezuela.
32
Biodiversity and Ecosystem
Services, Instituto Tecnológico Vale, Belém, PA, Brazil.
33
AMAP, IRD,
Cirad, CNRS, INRAE, Université de Montpellier, Montpellier, France.
34
InstitutNational de Recherches Archéologiques Préventives, Bègles,
France.
35
Direction des Affaires Culturelles (DAC Guyane), Cayenne,
French Guiana.
36
Coordenação de Biodiversidade, Instituto Nacional
de Pesquisas da Amazônia (INPA), Manaus, AM, Brazil.
37
Wetland
Department, Institute of Geography and Geoecology, Karlsruhe
Institute of Technology (KIT), Rastatt, Germany.
38
Ecology, Monitoring
and Sustainable Use of Wetlands (MAUA), Instituto Nacional de
Pesquisas da Amazônia (INPA), Manaus, AM, Brazil.
39
Programa de
Pós-Graduação em Ciências Biológicas e Botânica Tropical,
Universidade Federal Rural da Amazônia (UFRA), Belém, PA, Brazil.
40
Coordenação de Botânica, Museu Paraense Emílio Goeldi, Belém,
PA, Brazil.
41
Grupo de Investigación en Biodiversidad,Medio Ambiente
y Salud (BIOMAS), Universidad de las Américas, CampusQueri, Quito,
Ecuador.
42
Centro de Pesquisa Agroflorestal de Roraima, Embrapa
Roraima, Boa Vista, RR, Brazil.
43
Departamento de Botânica, Instituto
de Pesquisas Científicas e Tecnológicas do Amapá (IEPA), Macapá,
AP, Brazil.
44
HerbarioAmazónico Colombiano, Instituto Amazónico de
Investigaciones Científicas (SINCHI), Bogotá, DC, Colombia.
45
Amcel Amapá Florestal e Celulose S.A, Santana, AP, Brazil.
46
Departamento de Biologia Vegetal, Instituto de Biologia, Universidade
Estadual de Campinas (UNICAMP), Campinas, SP, Brazil.
47
Herbario Vargas, Universidad Nacional de San Antonio Abad del
Cusco (UNSAAC), Cusco, Cusco, Peru.
48
Biological and Environmenta l
Sciences, University of Stirling, Stirling, UK.
49
Departamento de
Ecologia, Centro de Biociências, Universidade Federal do Rio Grande
do Norte (UFRN), Natal, RN, Brazil.
50
Departamento de Biologia,
Universidade Federal de Rondônia (UNIR), Porto Velho, RO, Brazil.
51
Programa de Pós-Graduação em Biodiversidade e Biotecnologia,
Universidade Federal de Rondônia (UNIR), Porto Velho, RO,
Brazil.
52
Department of Biology and Florida Museum of Natural
History, University of Florida, Gainesville, FL, USA.
53
Instituto de
Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Loreto, Peru.
54
School of Geography and Sustainable Development, University of
St Andrews, St Andrews, UK.
55
Jardín Botánico de Missouri,
Oxapampa,Pasco,Peru.
56
Instituto Boliviano de Investigacion
Forestal, Santa Cruz, Santa Cruz, Bolivia.
57
Geography, College of Life
and Environmental Sciences, University of Exeter, Exeter, UK.
58
Programa de Ciencias del Agro y el Mar, Herbario Universitario
(PORT), UNELLEZ-Guanare, Guanare, Portuguesa, Venezuela.
59
International Center for Tropical Botany (ICTB), Department of
Biological Sciences, Florida International University, Miami,
FL, USA.
60
Paracou research station, UMR EcoFoG Université de
Guyane, Campus agronomique, Kourou Cedex, French Guiana.
61
Agteca-Amazonica, Santa Cruz, Bolivia.
62
Programa de Pós-
Graduação em Ecologia e Conservação, Universidade do Estado de
Mato Grosso (UNEMAT), Nova Xavantina, MT, Brazil.
63
Programa de
Pós-Graduação em Ecologia, Instituto Nacional de Pesquisas da
Amazônia (INPA), Manaus, AM, Brazil.
64
Environmental Change
Institute, University of Oxford, Oxford, Oxfordshire, UK.
65
Empresa
Brasileira de Pesquisa Agropecuária, Embrapa Amazônia Oriental,
Belém, PA, Brazil.
66
Facultad de Ciencias Agrícolas, Universidad
Autónoma Gabriel René Moreno, Santa Cruz, Santa Cruz, Bolivia.
67
Centro de Investigaciones Ecológicas de Guayana, Universidad
Nacional Experimental de Guayana, Puerto Ordaz, Bolivar, Venezuela.
68
Projeto Dinâmica Biológica de Fragmentos Florestais, Instituto
Nacional de Pesquisas da Amazônia (INPA), Manaus, AM, Brazil.
69
Laboratório de Ecologia de Doenças Transmissíveis da Amazônia
(EDTA), Instituto Leônidas e Maria Deane (Fiocruz Amazônia),
Manaus, AM, Brazil.
70
Programa de Pós-graduação em Biodiversidade
e Saúde, Instituto Oswaldo Cruz (Fiocruz), Rio de Janeiro, RJ, Brazil.
71
Programa de Pós-Graduação em Ecologia, Universidade Federal do
Pará (UFPA), Belém,PA, Brazil.
72
Diretoria Técnico-Científica, Instituto
de Desenvolvimento Sustentável Mamirauá, Tefé, AM, Brazil.
73
Laboratorio de Ecología de Bosques Tropicales y Primatología,
Universidad de los Andes, Bogotá, DC, Colombia.
74
Museo de Historia
Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene
Moreno, Santa Cruz, Santa Cruz, Bolivia.
75
Departamento de Botânica,
Instituto de Biociências, Universidade de São Paulo (USP), São Paulo,
SP, Brazil.
76
Programa de Pós-Graduação em Botânica, Instituto
Nacional de Pesquisas da Amazônia (INPA), Manaus, AM, Brazil.
77
Institute of Natural, Human, and Social Sciences (ICNHS), Federal
University of Mato Grosso (UFMT), Sinop, MT, Brazil.
78
Institute of
Biodiversity and Ecosystem Dynamics, University of Amsterdam,
Amsterdam, Netherlands.
79
Endangered Species Coalition, Silver
Spring, MD, USA.
80
Facultad de Ciencias Forestales y Ambientales,
Instituto de Investigaciones para el DesarrolloForestal, Universidad de
los Andes, Mérida, Mérida, Venezuela.
81
Inventory and Monitoring
Program, National Park Service, Fredericksburg, VA, USA.
82
Center for
Conservation and Sustainability, Smithsonian Conservation Biology
Institute, Washington, DC, USA.
83
Department of Geography and
Planning, University of Liverpool, Liverpool, UK.
84
Departamento de
Meio Ambiente e Desenvolvimento, Universidade Federal do Amapá
(UNIFAP),Macapá,AP,Brazil.
85
Gothenburg Global Biodiversity
Centre, University of Gothenburg, Gothenburg, Sweden.
86
Programa
Restauración de Ecosistemas (PRE), Centro de Innovación Científica
Amazónica (CINCIA), Tambopata, Madre de Dios, Peru.
87
Peruvian
Center for Biodiversity and Conservation (PCBC), Iquitos, Loreto,
Peru.
88
Department of Ecology, Institute of Biological Sciences,
University of Brasilia (UNB), Brasilia, DF, Brazil.
89
Andes to Amazon
Biodiversity Program, Madre de Dios, Madre de Dios, Peru.
90
Escola
de Negócios Tecnologia e Inovação, Centro Universitário do Pará,
Belém, PA, Brazil.
91
Environmental Science Program, Geosciences
Department, Universidade Federal do Pará (UFPA), Belém, PA, Brazil.
92
Grupo de Ecología y Conservación de Fauna y Flora Silvestre,
Instituto Amazónico de InvestigacionesImani, Universidad Nacional de
Colombia sede Amazonia, Leticia, Amazonas, Colombia.
93
Universidad
Estatal Amazónica, Puyo, Pastaza, Ecuador.
94
Universidad Regional
Amazónica IKIAM, Tena, Napo, Ecuador.
95
Procuradoria-Geral de
Justiça, Ministério Público do Estado do Amazonas, Manaus, AM,
Brazil.
96
Coordenação de Dinâmica Ambiental, Instituto Nacional de
Pesquisas da Amazônia (INPA), Manaus, AM, Brazil.
97
Departamento
de Genética, Instituto de Ciências Biológicas, Ecologia e Evolução,
Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG,
Brazil.
98
Department of Biology, University of Miami, Coral Gables, FL,
USA.
99
Fairchild Tropical Botanic Garden, Coral Gables, FL, USA.
100
Departamento de Ecologia, Instituto de Biociências, Universidade
de São Paulo (USP), São Paulo, SP, Brazil.
101
Servicios de
Biodiversidad EIRL, Iquitos, Loreto, Peru.
102
Postgraduate Program in
Biodiversity and Biotechnology Bionorte, Federal University of Acre
(UFAC), Rio Branco, AC, Brazil.
103
Postgraduate Program in
Ethnobiology and Nature Conservation, Federal Rural University of
Pernambuco (UFRPE), Pernambuco, PB, Brazil.
104
Department of
Integrative Biology, University of California, Berkeley, CA, USA.
105
Direction régionale de la Guyane, Office national des forêts,
Cayenne, French Guiana.
106
EmpresaBrasileiradePesquisa
Agropecuária, Embrapa Amapá, Macapá, AP, Brazil.
107
Tropical
Diversity Section, Royal Botanic Garden Edinburgh, Edinburgh,
Scotland, UK.
108
School of Environmental Sciences, University of East
Anglia, Norwich, UK.
109
Direccíon de Evaluación Forestal y de Fauna
Silvestre, Magdalena del Mar, Lima, Peru.
110
Instituto de Investiga-
ciones Forestales de la Amazonía, Universidad Autónoma del Beni
José Ballivián, Campus Universitario Final, Riberalta, Beni, Bolivia.
111
Escuela de Biología Herbario Alfredo Paredes, Universidad Central,
Quito, Pichincha, Ecuador.
112
Centro de Ciências Biológicas e da
Natureza, Universidade Federal do Acre (UFAC), Rio Branco, AC,
Brazil.
113
Museo Nacional de Ciencias Naturales (MNCN-CSIC),
Madrid, Spain.
114
Iwokrama International Centre for Rain Forest
Conservation and Development, Georgetown, Guyana.
115
New York
Botanical Garden, Bronx, New York, NY, USA.
116
Herbario HAG,
Universidad Nacional Amazónica de Madre de Dios (UNAMAD), Puerto
Maldonado, Madre de Dios, Peru.
117
Herbario Nacional de Bolivia,
Universitario UMSA, La Paz, La Paz, Bolivia.
118
Center for Conservation
and Sustainable Development, Missouri Botanical Garden, St. Louis,
MO, USA.
119
Department for Accelerated Taxonomy, Royal Botanic
Gardens, Richmond, Surrey, UK.
120
Departamento Académico de
Ingenieria Forestal y Ambiental, Universidad Nacional de Jaén, Jaén,
Cajamarca, Peru.
121
Biology Dep artment and Center for Energy,
Environment and Sustainability, Wake Forest University, Winston
Salem, NC, USA.
122
Laboratoire Evolution et Diversité Biologique,
Université Paul Sabatier CNRS UMR 5174 EDB, Toulouse, France.
123
Department of Anthropology, University of Texas at Austin, Austin,
TX, USA.
124
Estación de Biodiversidad Tiputini, Colegio de Ciencias
Biológicas y Ambientales, Universidad San Francisco de Quito-USFQ,
Quito, Pichincha, Ecuador.
125
Fundación Puerto Rastrojo, Bogotá,
DC, Colombia.
126
Department of Wildlife Ecology and Conservation,
University of Florida, Gainesville, FL, USA.
127
Fundación Estación
de Biología, Bogotá, DC, Colombia.
128
PROTERRA, Insti tuto de
Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Loreto, Peru.
129
ACEER Foundation, Puerto Maldonado, Madre de Dios, Peru.
130
Environmental Change Institute, Oxford University Centre for the
Environment, Oxford, England, UK.
131
Instituto de Ciencias Naturales,
Universidad Nacional de Colombia, Bogotá, DC, Colombia.
132
Instituto
de Ciência Agrárias, Universidade Federal Rural da Amazônia (UFRA),
Belém, PA, Brazil.
133
Escuela Profesional de Ingeniería Forestal,
Universidad Nacional de San Antonio Abad del Cusco, Puerto
Maldonado, Madre de Dios, Peru.
134
Laboratory of Human Ecology,
Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas,
DC, Venezuela.
135
Cambridge University Botanic Garden, Cambridge
University, Cambridge, UK.
136
Programa de Maestria de Manejo de
Bosques, Universidad de los Andes, Mérida, Mérida, Venezuela.
137
Centre for Biodiversity and Conservation Science (CBCS), The
University of Queensland, Brisbane, QLD, Australia.
138
Resource
Ecology Group, Wageningen University & Research, Wageningen,
Gelderland, Netherlands.
139
School of Earth, Environment and Society,
McMaster University, Hamilton, Ontario, Canada.
140
Laboratório de
Ciências Ambientais, Universidade Estadual do Norte Fluminense
(UENF), Campos dos Goytacazes, RJ, Brazil.
141
Departamento de
Biologia, Instituto de Ciências Biológicas (ICB), Universidade Federal
do Amazonas (UFAM), Manaus, AM, Brazil.
142
GeoIS, Quito, Pichincha,
Ecuador.
143
Faculty of Social Sciences, University of Nottingham,
University Park, Nottingham, UK.
144
Wildlife Conservation Society
(WCS), New York, NY, USA.
145
Medio Ambiente, PLUSPRETOL,
Iquitos, Loreto, Peru.
146
The Mauritius Herbarium, Agricultural
Services, Ministry of Agro-Industry and Food Security, Reduit,
Mauritius.
147
Department of Biology, Aarhus University, Aarhus C,
Aarhus, Denmark.
148
Living Earth Collaborative, Washington University
in St. Louis, St. Louis, MO, USA.
149
Escuela de Ciencias Forestales
(ESFOR), Universidad Mayor de San Simon (UMSS), Sacta,
Cochabamba, Bolivia.
150
FOMABO, Manejo Forestal en las Tierras
Tropicales de Bolivia, Sacta, Cochabamba, Bolivia.
151
Departamento
de Ciencias Forestales, Universidad Nacional de Colombia, Medellín,
Antioquia, Colombia.
152
Fundación Amigos de la Naturaleza (FAN),
Santa Cru z, Santa C ruz, Bol ivia.
153
Tropenbos International, Ede,
Netherlands.
154
School of Anthropology and Conservation, University
of Kent, Canterbury, Kent, UK.
155
Herbario Nacional del Ecuador,
Universidad Técnica del Norte, Quito, Pichincha, Ecuador.
156
Instituto
de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará
(FOPROP), Campus Tapajós, Santarém, PA, Brazil.
*Corresponding author. Email: vinicius.peripato@gmail.com (V.P.);
luiz.aragao@inpe.br (L.E.O.C.A.)
†These authors contributed equally to this work.
‡Deceased.
§Deceased.
SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.ade2541
Materials and Methods
Figs. S1 to S19
Tables S1 to S5
References (52–80)
MDAR Reproducibility Checklist
Data S1 and S2
Submitted 16 September 2022; accepted 31 August 2023
10.1126/science.ade2541
RESEARCH |RESEARCH ARTICLE
Peripato et al., Science 382, 103–109 (2023) 6 October 2023 6of6
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More than 10,000 pre-Columbian earthworks are still hidden throughout Amazonia
Vinicius Peripato, Carolina Levis, Guido A. Moreira, Dani Gamerman, Hans ter Steege, Nigel C. A. Pitman, Jonas G. de
Souza, José Iriarte, Mark Robinson, André Braga Junqueira, Thiago B. Trindade, Fernando O. de Almeida, Claide de
Paula Moraes, Umberto Lombardo, Eduardo K. Tamanaha, Shira Y. Maezumi, Jean P. H. B. Ometto, José R. G. Braga,
Wesley A. Campanharo, Henrique L. G. Cassol, Philipe R. Leal, Mauro L. R. de Assis, Adriana M. da Silva, Oliver L.
Phillips, Flávia R. C. Costa, Bernardo Monteiro Flores, Bruce Hoffman, Terry W. Henkel, Maria Natalia Umaña, William
E. Magnusson, Elvis H. Valderrama Sandoval, Jos Barlow, William Milliken, Maria Aparecida Lopes, Marcelo Fragomeni
Simon, Tinde R. van Andel, Susan G. W. Laurance, William F. Laurance, Armando Torres-Lezama, Rafael L. Assis, Jean-
François Molino, Mickaël Mestre, Michelle Hamblin, Luiz de Souza Coelho, Diogenes de Andrade Lima Filho, Florian
Wittmann, Rafael P. Salomão, Iêda Leão Amaral, Juan Ernesto Guevara, Francisca Dionízia de Almeida Matos, Carolina
V. Castilho, Marcelo de Jesus Veiga Carim, Dairon Cárdenas López, Daniel Sabatier, Mariana Victória Irume, Maria Pires
Martins, José Renan da Silva Guimarães, Olaf S. Bánki, Maria Teresa Fernandez Piedade, José Ferreira Ramos, Bruno
Garcia Luize, Evlyn Márcia Moraes de Leão Novo, Percy Núñez Vargas, Thiago Sanna Freire Silva, Eduardo Martins
Venticinque, Angelo Gilberto Manzatto, Neidiane Farias Costa Reis, John Terborgh, Katia Regina Casula, Layon O.
Demarchi, Euridice N. Honorio Coronado, Abel Monteagudo Mendoza, Juan Carlos Montero, Jochen Schöngart, Ted
R. Feldpausch, Adriano Costa Quaresma, Gerardo A. Aymard C., Chris Baraloto, Nicolás Castaño Arboleda, Julien
Engel, Pascal Petronelli, Charles Eugene Zartman, Timothy J. Killeen, Beatriz S. Marimon, Ben Hur Marimon-Junior,
Juliana Schietti, Thaiane R. Sousa, Rodolfo Vasquez, Lorena M. Rincón, Erika Berenguer, Joice Ferreira, Bonifacio
Mostacedo, Dário Dantas do Amaral, Hernán Castellanos, Marcelo Brilhante de Medeiros, Ana Andrade, José Luís
Camargo, Emanuelle de Sousa Farias, José Leonardo Lima Magalhães, Henrique Eduardo Mendonça Nascimento,
Helder Lima de Queiroz, Roel Brienen, Juan David Cardenas Revilla, Pablo R. Stevenson, Alejandro Araujo-Murakami,
Bruno Barçante Ladvocat Cintra, Yuri Oliveira Feitosa, Flávia Rodrigues Barbosa, Rainiellen de Sá Carpanedo, Joost
F. Duivenvoorden, Janaína da Costa de Noronha, Domingos de Jesus Rodrigues, Hugo F. Mogollón, Leandro Valle
Ferreira, John Ethan Householder, José Rafael Lozada, James A. Comiskey, Freddie C. Draper, José Julio de Toledo,
Gabriel Damasco, Nállarett Dávila, Roosevelt García-Villacorta, Aline Lopes, Fernando Cornejo Valverde, Alfonso Alonso,
Francisco Dallmeier, Vitor H. F. Gomes, Eliana M. Jimenez, David Neill, Maria Cristina Peñuela Mora, Daniel P. P. de
Aguiar, Luzmila Arroyo, Fernanda Antunes Carvalho, Fernanda Coelho de Souza, Kenneth J. Feeley, Rogerio Gribel,
Marcelo Petratti Pansonato, Marcos Ríos Paredes, Izaias Brasil da Silva, Maria Julia Ferreira, Paul V. A. Fine, Émile
Fonty, Marcelino Carneiro Guedes, Juan Carlos Licona, Toby Pennington, Carlos A. Peres, Boris Eduardo Villa Zegarra,
Germaine Alexander Parada, Guido Pardo Molina, Vincent Antoine Vos, Carlos Cerón, Paul Maas, Marcos Silveira,
Juliana Stropp, Raquel Thomas, Tim R. Baker, Doug Daly, Isau Huamantupa-Chuquimaco, Ima Célia Guimarães Vieira,
Bianca Weiss Albuquerque, Alfredo Fuentes, Bente Klitgaard, José Luis Marcelo-Peña, Miles R. Silman, J. Sebastián
Tello, Corine Vriesendorp, Jerome Chave, Anthony Di Fiore, Renato Richard Hilário, Juan Fernando Phillips, Gonzalo
Rivas-Torres, Patricio von Hildebrand, Luciana de Oliveira Pereira, Edelcilio Marques Barbosa, Luiz Carlos de Matos
Bonates, Hilda Paulette Dávila Doza, Ricardo Zárate Gómez, George Pepe Gallardo Gonzales, Therany Gonzales,
Yadvinder Malhi, Ires Paula de Andrade Miranda, Linder Felipe Mozombite Pinto, Adriana Prieto, Agustín Rudas, Ademir
R. Ruschel, Natalino Silva, César I. A. Vela, Egleé L. Zent, Stanford Zent, Angela Cano, Yrma Andreina Carrero Márquez,
Diego F. Correa, Janaina Barbosa Pedrosa Costa, David Galbraith, Milena Holmgren, Michelle Kalamandeen, Guilherme
Lobo, Marcelo Trindade Nascimento, Alexandre A. Oliveira, Hirma Ramirez-Angulo, Maira Rocha, Veridiana Vizoni
Scudeller, Rodrigo Sierra, Milton Tirado, Geertje van der Heijden, Emilio Vilanova Torre, Manuel Augusto Ahuite Reategui,
Cláudia Baider, Henrik Balslev, Sasha Cárdenas, Luisa Fernanda Casas, William Farfan-Rios, Cid Ferreira, Reynaldo
Linares-Palomino, Casimiro Mendoza, Italo Mesones, Ligia Estela Urrego Giraldo, Daniel Villarroel, Roderick Zagt, Miguel
N. Alexiades, Edmar Almeida de Oliveira, Karina Garcia-Cabrera, Lionel Hernandez, Walter Palacios Cuenca, Susamar
Pansini, Daniela Pauletto, Fredy Ramirez Arevalo, Adeilza Felipe Sampaio, Luis Valenzuela Gamarra, and Luiz E. O. C.
Aragão
Science 382 (6666), . DOI: 10.1126/science.ade2541
Editor’s summary
Downloaded from https://www.science.org on October 05, 2023
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Science (ISSN 1095-9203) is published by the American Association for the Advancement of Science. 1200 New York Avenue NW,
Washington, DC 20005. The title Science is a registered trademark of AAAS.
Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim
to original U.S. Government Works
Indigenous societies have lived in the Amazon for at least 12,000 years. Finding evidence of these societies, however,
has been greatly hampered by the density of the forest in Amazonia. Peripato et al. used LIDAR (light detection and
ranging) surveys to identify more than 20 previously unidentified developments and then modeled the occurrence
of others across the Amazon. The authors predict that between 10,000 and 24,000 ancient earthworks are waiting
to be discovered. Sampling of some of the LIDAR transects revealed a consistent set of domesticated tree species
associated with the developments, suggesting active forestry practices among these societies. —Sacha Vignieri
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