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O R I G I N A LA R T I C L E
Ethnobotanical ground-truthing:
indigenous knowledge, floristic
inventories and satellite imagery
in the upper Rio Negro, Brazil
Marcia Barbosa Abraao1,2, Bruce W. Nelson1, Joao Claudio Baniwa3, Douglas
W. Yu4 and Glenn H. Shepard Jr4*
INT RO DUCT IO N
‘Forming partnerships and collaborative alliances
between indigenous and traditional peoples and
conservationists is no easy task, but it would seem to
be one of the most effective ways to save the
increasingly threadbare ecosystems that still exist’
(Chapin, 2004, p. 30).
‘The idea that local people can be organized
effectively to help researchers scale up their technical
data collections is well established. The additional
assertion – that local people can be formally engaged
to guide and support more effective conservation –
shows promise and deserves further evaluation.
We encourage tropical biologists to add local
collaborations to their toolbox of approaches’ (Sheil
& Lawrence, 2004, p. 637).
Though ethnobiologists and a few enlightened taxonomists
have been saying as much for decades (e.g. Conklin, 1957;
Diamond, 1966; Berlin et al., 1974; Bulmer, 1974; Berlin, 1984;
Posey et al., 1984), mainstream ecologists have only recently
woken up to the fact that indigenous and local peoples possess
a wealth of scientifically valid knowledge about species,
habitats and resource management in tropical ecosystems,
1Ecology Program (CPEC), Instituto Nacional
de Pesquisas da Amazonia, Manaus, AM, 2Instituto Socioambiental, Research Associate,
Manaus, AM, 3Escola Indı gena Baniwa-
Coripaco/Organizac ¸a o Indı gena da Bacia do
Ic ¸ana, Sa o Gabriel da Cachoeira, AM, Brazil
and 4School of Biological Sciences, University of
East Anglia, Norwich, UK
*Correspondence: Glenn H. Shepard Jr, Estrada
do Turismo 1997, Condominio Itapuranga III,
Lote o-4, Manaus, AM, Brazil.
E-mail: [email protected]
ABS T RACT
Aim To assess the utility of indigenous habitat knowledge in studies of habitat
diversity in Amazonia.
Location Baniwa indigenous communities in Rio Icana, upper Rio Negro,
Brazil.
Methods Six campinarana vegetation types, recognized and named by a
consensus of Baniwa indigenous informants according to salient indicator species,
were studied in 15 widely distributed plots. Floristic composition (using Baniwa
plant nomenclature only, after frustrated attempts to obtain botanical collection
permits), quantitative measures of forest structure and GPS waypoints of the 4-ha
composite plot contours were registered, permitting their location on Landsat
satellite images. Non-metric multidimensional scaling (NMDS) ordination was
carried out using pc-ord software.
Results The NMDS ordinations of the plot data revealed a clear gradient of
floristic composition that was highly correlated with three quantitative measures
of forest structure: basal area, canopy height and satellite reflectance.
Main conclusions Baniwa-defined forest types are excellent predictors of habitat
diversity along the structural gradient comprising distinctive white-sand
campinarana vegetation types. Indigenous ecological knowledge, as revealed by
satellite imagery and floristic analyses, proves to be a powerful and efficient shortcut
to assessing habitatdiversity,promotingdialogue between scientific andindigenous
worldviews, and promoting joint study and conservation of biodiversity.
Keywords
Amazonia, Baniwa Indians, beta-diversity, campinarana, Guiana Shield, remote
sensing, traditional ethnobiological knowledge, vegetation classification.
Journal of Biogeography ( J. Biogeogr .) (2008) 35, 2237–2248
ª 2008 The Authors www.blackwellpublishing.com/jbi 2237Journal compilation ª 2008 Blackwell Publishing Ltd doi:10.1111/j.1365-2699.2008.01975.x
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and that they represent crucial and under-appreciated partners
in the study and conservation of global biodiversity
(Phillips et al., 1994; Sheil & Lawrence, 2004). In particular,
habitat classification by indigenous and local peoples has been
the subject of increasingly sophisticated interdisciplinary
studies. Inevitably, indigenous habitat classification schemes
resolve more habitat types at the local level than do compa-
rable scientific classifications (Parker et al., 1983; Fleck &
Harder, 2000; Shepard et al., 2001, 2004b; Krohmer, 2004;
Halme & Bodmer, 2007). Indeed, scientific habitat classifica-
tion schemes for Amazonia borrow heavily from the indige-
nous-derived vocabularies of local riverine dwellers (e.g.
Jordan, 1985; Pires & Prance, 1985; Encarnacion, 1993),
although this intellectual debt is not explicitly acknowledged.
Recognizing the value of local knowledge, and building
upon it to develop scientific collaborations with local peoples,
is important for a number of reasons. In the first place, it is
only fair that scientists working in indigenous-populated areas
treat local hosts and their traditional knowledge with respect.
Scientists who suspend their often deep and unexamined
cultural prejudices sometimes discover that local people’sknowledge and astute observations can lead to new insights
into species identification, ecological processes and rational
resource management (see Conklin, 1957; Diamond, 1966;
Bulmer, 1974; Goulding, 1980; Posey, 1983; Posey et al., 1984;
Gentry, 1993, p. 4; Shepard et al., 2001, p. 2). In specific
instances, indigenous knowledge might be applied to increase
the efficiency of biodiversity assessments (Milliken, 1998;
Shepard & Chicchon, 2001; Instituto Socioambiental, 2003),
endeavours that are chronically under-funded, notoriously
time- and labour-intensive and increasingly urgent (Tuomisto,
1998). Finally, given the fact that indigenous reserves account
for more than half of all Amazonian protected areas in total
land area (Peres, 1993), dialogue and collaboration between
conservation biologists and local populations may be crucial to
the future of tropical biodiversity (Chapin, 2004; Sheil &
Lawrence, 2004; Shepard et al., 2004b).
Research context
The work presented here was carried out as part of a Master’s
thesis research project by M.B.A. (2005) under the mentorship
of B.W.N. and G.H.S., building upon prior collaborations
(Instituto Socioambiental, 2003; Shepard et al., 2004a,b; Silva,
2004). The thesis project formed part of a larger collaborative
effort carried out by Instituto Nacional de Pesquisa daAmazonia (INPA, a Brazilian federal research institute),
Instituto Sociambiental (ISA, a non-government organization)
and Organizacao Indıgena da Bacia do Icana (OIBI, an
indigenous association), which was funded by Brazil’s National
Research Council (CNPq) and the research support founda-
tion of Amazonas state (FAPEAM). Joao Claudio, a Baniwa
high school student and FAPEAM stipend recipient, was the
designated indigenous research collaborator. The thesis project
received the prior informed consent of the indigenous
communities involved, via their representative organization,
OIBI, and was governed by the terms of a signed agreement
following the protocol established in the document ‘Criteria
and procedures governing the relations between researchers
and Indians in the Upper Rio Negro’ (Ricardo, 2000, p. 292).
As required by recent legislation, the thesis project was
evaluated by a national-level council for the protection of
genetic patrimony and associated traditional knowledge
(CGEN), created to establish rules for the sustainable and
ethical study and exploitation of biodiversity in Brazil, which is
a mega-diverse country and signatory of the Convention on
Biodiversity. The project was deemed exempt from the CGEN
permit process, as it did not involve access to genetic materials
or to commercially valuable traditional knowledge. The project
also received entry permits from the regional indigenous
federation (FOIRN) and Fundacao Nacional do Indio (FU-
NAI). It was not possible to obtain community consent for
botanical collections prior to the beginning of fieldwork, but
this consent was obtained during the course of the study. At
this point, the botanical collecting permit process was initiated
at the Brazilian Institute of Environment and Natural
Resources (IBAMA), but the bureaucratic process took longerthan the time allotted by the Master’s study programme. It is
self-defeating that the federal and state agencies that grant
degrees and finance scientific research impose strict time
deadlines, while the federal agencies that issue the authoriza-
tions to carry out the same research respect no such time
constraints (see Pivetta, 2006, for commentary by other
Brazilian scientists frustrated by the slow, cumbersome
research authorization process).
Study area
Research was carried out in Baniwa indigenous communities
of the middle Icana River, the principal study sites being
Juivitera, Jandu Cachoeira and Aracu Cachoeira (Fig. 1),
chosen for the predominance of campinarana vegetation and
the willingness of the communities to participate in the
study. The Baniwa belong to the Arawakan cultural-linguistic
family and are closely related to the Coripaco of Colombia
and the Wakuenai of Venezuela. In Brazil, the Baniwa have a
population of about 4840, divided among 93 small commu-
nities along the Icana, Ayari, Cuyari, Xie and other upper Rio
Negro tributaries in the Colombia/Venezuela border area
(Cabalzar & Ricardo, 1998). Within the upper Icana study
region, the Baniwa are divided among three main exogamous
phratries (a type of patrilineal clan; see Wright, 1998), eachpredominant within a specific geographical region (Fig. 1).
Because of exogamous marriage practices and complex
patterns of inter-ethnic exchange (see also Jackson, 1983),
the study communities include speakers of several Baniwa
dialects (including Coripaco) as well as speakers of non-
Arawakan languages, especially Cubeo and Nheengatu (or
lingua geral ). The national language of Portuguese is spoken
by many people as a second or even third language.
The upper Rio Negro comprises the north-western limit of
the Guiana Shield, composed of ancient, eroded granite
M. B. Abraa ˜ o et al.
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formations covered in soil depositions of rather recent
geological origin. Annual rainfall, at 2500 to 3000 mm, is
among the highest in Amazonia, while annual temperatures
average 24C with negligible seasonal variation. As its name
implies, the Rio Negro and most of its major tributaries are
blackwater rivers with little sediment, low productivity and a
dark, tea-like coloration (Sioli, 1984). Acidic, nutrient-poor
white sand soils (podzols) are widespread, giving rise to a low-
productivity (oligotrophic) vegetation whose structure ranges
from closed-canopy forest to savanna-like. The Baniwa call this
habitat category hamaliani; it is known variably in the
scientific literature as caatinga, caatinga amazonica (Anderson,
1981; Jordan, 1985) and campinarana (Veloso et al., 1991).
White-sand campinarana contains many endemic species and
is most common in the region centred on the Rio Negro
and Rio Branco in Brazil, extending into Colombia, Venezuela
and northern Peru. The other predominant habitat categories
in the study region are flooded black water igapo forests,
known as ala pe in Baniwa, and upland terra firme, known as
eedzawa in Baniwa (Fig. 1; see also Abraao et al., in press;
Andrello, 1998). Terra firme forests in the upper Rio Negrohave variable soil colours and clay–sand proportions (see
Andrello, 1998; Instituto Socioambiental, 2003). Though the
Baniwa consider them suitable for agriculture, sand content
and acidity are sometimes apparently higher than typical
upland terra firme as described elsewhere (Pires & Prance,
1985); thus some forests the Baniwa consider to be ‘terra firme’
might in fact represent the far end of the campinarana to terra
firme transition.
Building upon Andrello’s (1998) prior study of local
knowledge about habitat diversity on the Icana River, the
aim of this study is to assess the utility of Baniwa-recognized
vegetation types in characterizing environmental gradients and
‘ground-truthing’ satellite images within the white-sand
campinarana forests of the upper Rio Negro.
M E T HO DS
Andrello’s (1998) preliminary list of Baniwa vegetation types
was used as a starting point to develop a classification of the
most important white-sand campinarana (hamaliani) vegeta-
tion types in the study area. Baniwa habitat classification, like
that of other Amazonian peoples studied to date (see Fleck &
Harder, 2000; Shepard et al., 2001), is based on the recogni-
tion of locally abundant or salient indicator species that
appear to signal significant habitat transitions. The suffix -lima
or -rima, ubiquitous in Baniwa habitat classification, indicates
local abundance or salience. Thus, the habitat term anerima
refers to a vegetation type where the Baniwa plant taxon ane
(Eperua sp.; see Table 1) is locally abundant or salient.
Although systematic botanical collection in the plots was not
possible for this project, the author G.H.S. had previously carried out ethnobotanical research and made limited botan-
ical collections in the region as part of a study of traditional
crafts (Shepard et al., 2004a), and assisted Silva (2004) in the
collection and identification of botanical material from plots
made in terra firme forest and secondary forest resulting from
Baniwa agricultural fallows. Coincidentally, a few Baniwa taxa
relevant to the current study were collected and identified
during this prior work. Reference collections made by Shepard
(code GHS) and Silva (ALS) are included in our tables and
figures, where applicable. Fertile material is deposited in the
Figure 1 Map of the study area, Rio Icana,
municipality of Sao Gabriel da Cachoeira,Amazonas, Brazil, showing study communi-
ties, important clan (phratry) groupings and
major habitat boundaries.
Ground-truthing in the upper Rio Negro
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INPA herbarium, Manaus, and the remainder (especially sterile material) is found in Shepard’s and Silva’s working
collections. Additional field identifications for this study were
made to genus using Gentry (1993), and in some instances to
species using Ribeiro et al. (1999), which is essentially a
portable herbarium containing practical taxonomic keys and
close-up photographic details of 2175 plant species. This field
guide, resulting from an exhaustive survey of the Reserva
Ducke forest reserve, covers terra firme, campinarana and
lowland forests of the lower Rio Negro, comprising similar
habitats and including many of the same species as found in
the upper Negro study area. By making repeated expeditionsto the study region between 2001 and the present, studying
past collections and field notes, using digital photographic
material and consulting the INPA herbarium frequently,
G.H.S. has arrived at confident field identifications for most
indicator species in Baniwa campinarana habitat classification
(Table 1; see also Abraao et al., in press). All plant names
(other than common palms) identified to species level in our
tables and figures were either confirmed by reference collec-
tions from prior research or else identified using the
taxonomic and photographic keys provided in Ribeiro et al.
Table 1 Summary information on 14 consensus hamaliani vegetation types.
Vegetation
type
Indicator
species
Field
identification Description
Anerima* Ane Eperua sp. (LEG) Transitional area from open, savanna-like vegetation
to more forested campinarana habitats, often bordering
on waittirima (below); conspicuous presence of epiphytic
bromelias, orchids and the white lichen, Cladonia; herbaceous
koliwaipa (below) often in understorey Anholima* Anho Micrandra spruceana (Baill.)
R.E.Schultes (EUP)
Closed canopy forest with tall trees in humid areas especially
near stream banks and stream headwaters
Dzeekalima Dzeeka Hevea guianensis Aubl. (EUP) Closed canopy forest especially near stream banks
Heridzorolima* Heridzoro Anaxagorea manausensis
Timmerman (ANN)
Closed canopy forest in humid areas near streams and stream
headwaters, often with small puddles of standing water;
lichens and moss often found at the base of trees
Itsapolima Itsapo Duguetia sp. (ANN) Dense understorey, presence of conspicuous, whitish,
small-diameter itsapo trunks, a preferred source of strong,
flexible fishing poles
Koliwaipalima* Koliwaipa Asplundia vaupesiana Harling (CYC) Forested environment but with abundant light in the
understorey; understorey dominated by dense stands of
herbaceous koliwaipa (edible fruit), as well as orchids
and bromelias
Maarolima Maaro Caraipa sp. (CLU) Closed-canopy forest with c onspicuous large-le aved maaro
saplings visible in understorey
Maporottirima* Maporotti Humiria balsamifera (Aubl.) St. Hil.
var. (HUM) [GHS 4268, 4343, 4347,
4377; ALS 239, 243]
Associated with agricultural fallows in campinarana;
depleted soils with secondary forest species and
characteristic stands of maporotti, an important edible
fruit; related to, but distinctive from, waalia (below)
Ponamalima* Ponama Oenocarpus bataua Mart. (ARE) Humid or swampy areas; important edible fruit
Poramolima Poramo Euterpe catinga Wallace (ARE) Humid or swampy areas; important edible fruit
Ttinalima* Ttina Mauritia carana Wallace (ARE) Open, swampy areas with few trees and characteristic
herbaceous vegetation; ttin a (also known as carana)
is the prime source of roof thatch in the region
Waalialima* Waalia Humiria balsamifera (Aubl.) St. Hil.
var. (HUM)
Open, savanna-like vegetation with areas of exposed white sand;
waalia trees often stand in the open, and have a characteristic
magnolia-like architecture with many low branches; edible fruitsmuch like maporotti (above)
Waapalima* Waapa Eperua purpurea Benth. Closed-canopy forest on well-drained soils with many large-diameter
trees, transitioning to terra firme uplands; the only campinarana
vegetation type which permits manioc agriculture
Waittirima* Waitti Aldina heterophylla Spruce ex
Benth. (LEG)
Open, savanna-like vegetation with explosed areas of white sand;
often transitions anerima (above); highly conspicious waitti
trunks are white, twisted and covered with epiphytes; understorey
with bromelias, orchids and Cladonia
*Vegetation types mentioned by Andrello (1998) for hamaliani.
M. B. Abraa ˜ o et al.
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(1999) after repeated encounters and consistent naming by
multiple Baniwa informants.
Based on consensus among 10 informants (Abraao, 2005;
Abraao et al., in press), 14 Baniwa-defined habitats were
chosen for detailed study, comprising the most common and
salient hamaliani vegetation types in the study communities
(Table 1). Of these, six were chosen for further study in
standardized botanical plots, according to the followingcriteria: (1) they were common, abundant vegetation types
found in all study communities; (2) together they appeared to
encompass a broad environmental gradient; and (3) they had
continuous or nearly continuous tree canopy cover. The latter
criterion was imposed because certain open, waterlogged
savanna types have too few trees with a diameter at breast
height (d.b.h.) of ‡ 5 cm to warrant establishing plots.
A total of 15 plots representing the six chosen vegetation
types were established in three Baniwa communities with
predominantly hamaliani vegetation: Juivitera, Jandu and
Aracu (see Table 2). In the process of establishing the plots,
the vegetation types anerima and waittirima were further
divided by informants into ‘high canopy’ (dzenonipe kaawa)
and ‘low canopy’ (madoape kaawa) subtypes (see Table 2,
items 5A/5B and 6A/6B). Each plot consisted of five subplots
of 20 · 20 m arranged preferentially in a cross pattern, for a
total plot area of 2000 m2 [0.2 hectares (ha)] spread through-
out a 200 · 200 m area (4 ha), or ‘composite plot’, of
consistent vegetation (Fig. 2). Sometimes the shape and
arrangement of the subplots was altered to maintain the
consistency in vegetation type throughout the composite plot
(see Fig. 3). All trees with d.b.h. ‡ 5 cm were tagged with
numbered aluminium tags, measured and their Baniwa names
noted. The aluminium tags may still permit botanical identi-
fication of material from the plots if botanical collectionpermits can be obtained for ongoing studies in the region.
Quantitative measurements of forest structure (basal area,
average canopy height, canopy density, understorey vegetation
density, leaf litter depth) were taken, and GPS waypoints
registered at each subplot. To minimize the variability in
naming, a single knowledgeable informant was chosen from
each community to name trees in all plots in that community.
Logistical constraints as well as social considerations did not
permit all informants to work in all plots. The effect of inter-
informant variation is evaluated below.
In all, 7541 individual trees with d.b.h. ‡ 5 cm were
censused in the 15 study plots, representing a total of 353
different Baniwa plant names. Baniwa plant names mentioned
only once for all plots were eliminated to avoid undue
influence of less abundant species or idiosyncratic ethnobo-
tanical knowledge. This left a total of 223 unique Baniwa plantnames, which were incorporated into a matrix. Ethnobotanical
species composition from the tree plots was ordinated with
pc-ord software (McCune & Grace, 2002) using two-axis
non-metric multidimensional scaling (NMDS) (Fig. 4) and
single-axis NMDS (Figs 5 & 6) for indirect gradient analysis
(see McCune & Grace, 2002, pp. 125–142). We quantified tree
species composition using three different metrics: total basal
area of each Baniwa plant name (Figs 4 & 5a), numerical
abundance of each Baniwa plant name (Fig. 5b) and presence/
absence of Baniwa plant names (Fig. 5c). The latter metric is
Table 2 Summary of the ethnobotanical
plots surveyed.Code Vegetation type Study communities
No. of
replicates
1 An holima Jandu + Juivitera 2
2 Heridzorolima Aracu + Jandu 2
3 Waapalima Jandu + Juivitera 2
4 Maarolima Jandu + Juivitera + Aracu 3
5A High anerima (dzenonipe kaawa) Jandu + Juivitera 2
5B Low anerima (madoape kaawa) Jandu + Juivitera 26A High waittirima (dzenonipe kaawa) Juivitera 1
6B Low waittirima (madoape kaawa) Jandu + Juivitera 1
Figure 2 Ethnobotanical inventory plot design. Small squares
represent 20 · 20 m (0.04 ha) subplots within a larger
200 · 200 m (4 ha) sample area. Circles represent points within
each 20 · 20 m subplot where forest structural measures (canopy
height, canopy openness and understorey vegetation density) were
taken.
Ground-truthing in the upper Rio Negro
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more sensitive to differences in botanical knowledge among
the three informants (one per community), as it does not give
greater weight to the more common tree species, for which
naming conventions are presumably more stable.
The GPS waypoints were collected from the subplots and
used to draw the boundaries of the composite plots, each
representing a nearly uniform vegetation type, on a geomet-
rically corrected Landsat image. The geometric reference image
was the Geocover Landsat mosaic for 1990 (https://zulu.ssc.-
nasa.gov/mrsid/), which has a reported root mean square
accuracy of 50 m. Each composite plot of 4 ha corresponds
to c . 44 pixels, with each pixel in Landsat representing an
aggregate reflectance value for a 30 · 30 m area on the Earth’s
surface. Working at this scale, effects of errors in GPS and
image registration were minimized, allowing for a good
geometric match between field and image-derived data.
Pixel brightness values for each of the three Landsat TM
bands 3, 4 and 5 were averaged over the entire composite plot
to give one brightness value per band and per plot. We first
considered the three bands separately and used principalcomponents analysis (PCA) on pc-ord (McCune & Grace,
2002) to ordinate the 15 composite plots based on their
spectral attributes (Fig. 7). We then calculated a single average
brightness value for each plot over all three bands after
normalizing each band using the standard deviation of each
band across all 15 plots. Finally, the 15 composite plots were
plotted on a two-axis quantitative NMDS floristic ordination
using dot sizes proportional to each stand’s basal area and
grey-tone fills in the dots to represent average canopy
reflectance in three Landsat bands (Fig. 8).
Figure 3 Satellite image showing the location of ethnobotanical
plots in the community of Juivitera. The shape of plots and the
distribution of subplots were modified where necessary to main-
tain consistency of vegetation type throughout the sample area.
The community of Juivitera is located at 118¢ N, 6833¢ W.
Vegetation types are described in Table 1.
Figure 4 Indirect floristic gradient derived from relative basal
area of 223 Baniwa plant names in the study plots. Numbers
correspond to the Baniwa-assigned vegetation types of each plot
(Table 2). Each plant’s importance was measured by its percent
contribution to the basal area of the plot using the quantitative
Bray–Curtis (Sørensen) index. The size of each symbol corre-
sponds to that plot’s total basal area. Black circles represent the
plots in the Juivitera community, grey squares represent those in
Aracu and white triangles represent the Jandu plots. Taken to-
gether, the two non-metric multidimensional scaling (NMDS)
axes explain 92% of the variation between pair-wise distances in
the original data set. The near-horizontal dashed lines represent
correlation of the floristic gradient with two structural measures –
total basal area and mean canopy height – where the line’s slope
indicates the NMDS axis with which it is more strongly correlated,
and its length represents the strength of that correlation.
(a)
(b)
(c)
Figure 5 Single-axis non-metric multidimensional scaling
(NMDS) of inventoried plots analysing 232 Baniwa plant names
using (a) a quantitative similarity index using relative dominance
(basal area), (b) a quantitative similarity index using relative
abundance, and (c) a qualitative similarity index using presence/
absence. The vertical axis is simply the rank order of the NMDS
scores shown on the horizontal axis. Symbol shapes, labels and
sizes as in Fig. 4.
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RE S ULT S
The ordination in Fig. 4 shows clearly that plots assigned the
same Baniwa habitat name are closer to one another in NMDS
space – despite being geographically distant – than they are to
different plot types that are geographically close (absolute
geographical distance was not calculated, but rather commu-
nity name is used as a proxy measure of distance, i.e. plots in
the same community are geographically closer than plots
10
0
10
0
10
010
D o m i n a n c e ( b a s a l a r e a p e r h e c t a r e )
010
010
010
010
010
010
0
10
0
10
0
10
0
10
0
10
0
10
0
10
010
0
20
0
20
0
Figure 6 Changes in dominance of each of
20 Baniwa plant names along the floristic
gradient, represented by 15 study plots. Bar
heights indicate the basal area (m2 per ha) of
the indicated plant name in each study plot.
The plots are placed along the horizontal
dimension according to their score in the
single-axis non-metric multidimensional
scaling (NMDS) from Fig. 5a. Baniwa
names for the 20 plants with the highest
dominance values in the entire study are
arranged vertically by the dominance-weigh-
ted average of the NMDS scores for all
study plots in which they occur. Asterisks
identify plant names that are Baniwa
indicator species for the six main vegetation
types under study.
Figure 7 Principal components analysis (PCA) of the variation
in spectral reflectance between 15 composite study plots for
Landsat TM bands 3, 4 and 5. PCA axis 1 explains 86% and
axis 2 explains an additional 9% of the spectral variation.
Dashed lines represent correlation of the two PCA axes with
two standard spectral measures: average brightness of the three
bands and normalized difference vegetation index (NDVI).
Symbol shapes, labels and sizes represent informant, Baniwa
vegetation type and plot basal area, respectively, as in Figs 4 & 5.
Figure 8 Summary of ground-truthing for Baniwa-defined hab-
itats, triangulating three independent assessments of the study
plots: floristic composition, forest structure and satellite reflec-
tance. The two-dimensional location of each dot represents theethnobotanical species composition of the corresponding plot in a
two-axis non-metric multidimensional scaling (NMDS) (as in
Fig. 4). The area of each dot is directly proportional to the total
basal area of the corresponding plot. Grey tone values for each dot
are scaled to the average of standardized brightness of the three
Landsat bands recorded for that plot. Thus forest structure (dot
size) and canopy reflectance (dot grey tone) are clearly correlated
with the gradient of ethnobotanical species composition. Plots
with higher basal area (larger dots) have taller trees and more
irregular canopy texture in Landsat images (darker dots) and have
lower scores for NMDS axis 1 (dots further to the left).
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in different communities). This is especially true if one
considers the single-axis NMDS scores (see Fig. 5a), where
replicate plots assigned the same habitat number are, for the
most part, quite close to one another along the horizontal axis.
Even the distinctive vegetation subtypes high vs. low anerima
(codes 5A, 5B) and high vs. low waittirima (codes 6A, 6B) are
also fairly close to one another, despite their geographical
(inter-community) distance. The one exception is the Baniwa
vegetation type an holima (code 1): the two an holima plots,
located in different communities, are also fairly distant from
one another along the horizontal axes (Figs 4 & 5a,b),
although they are close to one another along the vertical axis
(NMDS axis 2) in Fig. 4. [Note that analysis of satellite
reflectance shows a similar trend for the two an holima plots,
with separation along the main (horizontal) axis but proximity
along the secondary (vertical) axis; see Fig. 7 and more detailed
discussion below].
Taken together, the two NMDS axes explain 92% of the
variation, meaning that the ordination is a very robust
representation of the variation in overall floristic composi-
tion. Axis 1 of the NMDS ordination is also strongly correlated with two measures of forest structure for each
plot (Fig. 4, dashed lines): total basal area and average canopy
height (Pearson’s coefficient of correlation 0.95 and 0.79,
respectively). Other measures of forest structure were not
significantly correlated with axis 1: canopy density (P = 0.50),
understorey vegetation density (P = 0.33) and leaf litter depth
(P = 0.33). Thus, the points on the left side of the ordination
scatter plot in Fig. 4 appear to represent higher-canopy forest
with greater vegetation cover (i.e. higher basal area), while
those on the right represent low-canopy forest with less
vegetation cover (lower basal area; note that the size of data
points in Figs 4 & 5 is proportional to total basal area of each
plot). This arrangement corresponds with the distinction
between ‘true hamaliani’ (right side) as opposed to closed
canopy, terra firme transitional forest (left side and centre),
detected independently in interview data not presented here
(see also Abraao et al., in press). Note that the three plots
found at the extreme left side of this figure represent
vegetation types 1 (an holima) a n d 2 (heridzorolima), both
associated with moist soils along stream banks or stream
headwaters (see Table 1).
Ordination was also carried out using relative abundance
of Baniwa plant names registered, with much the same result
as when using basal area (compare Fig. 5a and 5b). In other
words, when Baniwa plant names are used as a proxy forbotanical identification, the resulting ordinations reveal a
floristic gradient that correlates well with structural measures,
demonstrating that the composite plots can be organized
under the distinctive vegetation types named by multiple
Baniwa informants, despite inter-informant variation
(Figs 4–6).
When qualitative measures of similarity are used, however,
the ordination changes considerably. Figure 5c shows the
result of a single-axis qualitative NMDS based on the
presence/absence of 223 Baniwa plant names. Working with
quantitative measures such as total basal area or relative
abundance places greater emphasis on a few common plant
names, whereas a presence/absence metric gives equal weight
to rare plant names. Unlike the quantitative NMDS, in which
the plots cluster strongly by Baniwa vegetation type (Figs 4 &
5a,b), presence/absence NMDS shows a clear pattern of
clustering by community (Fig. 5c). It appears that clustering
by community (and hence by informant) is due to bias or
idiosyncrasy in the way the three informants, one from each
community, assigned Baniwa names to the less common or
less distinctive plant taxa. Indeed, there is a fairly pronounced
gap between data points from the community of Juivitera
(Fig. 5c, black circles on the left side of graph), and those
from the remaining two communities, Jandu (white triangu-
lar data points) and Aracu (square grey data points), between
which there is no such pronounced gap. With only a handful
of houses, Juivitera is much smaller than the other two
communities, yet, for historical reasons, the people of
Juivitera are more thoroughly multilingual, speaking Nheng-
atu and some Cubeo in addition to Baniwa. Thus, linguistic
variation and perhaps also social factors (i.e. varying sizes of the primary social network within which knowledge is
retained and transmitted) are likely to have contributed to
the clustering of data points in the qualitative analysis. This
result highlights the importance of systematic botanical
collection and identification, even though it is not always
feasible. Nonetheless, when analysis is weighted towards the
more common species (Fig. 5a,b) for which Baniwa names
are more likely to show inter-community agreement, inter-
informant differences are no longer detected, and Baniwa
habitat classification reveals community ecological structure
that correlates with physical structure (Figs 4 & 7).
While systems of ethnobiological classification often accu-
rately reflect taxonomic affinities among organisms, especially
at the ‘folk genus’ rank, there is not necessarily a one-to-one
correspondence between folk taxa and scientific species (Berlin
et al., 1974; Berlin, 1992), especially for species-rich plant
families and genera. Ethnobotanical systems demonstrate both
over- and under-differentiation when compared with Linnaean
botanical taxonomy (Berlin, 1992, p. 118). Over-differentiation
– when a single Linnaean species is divided among multiple
ethnobotanical taxa – is especially common in cultivated plants
and others with high cultural value. For example, the
distinctive Baniwa plant names waalia and maporotti refer to
what appear to be multiple varieties of a single botanical
species, Humiria balsamifera (Aubl.) St. Hil. The Baniwarecognize the close relationship (both taxa have nearly
identical edible fruits), but consider them to be morpholog-
ically and ecologically distinctive (see Table 1). Under-differ-
entiation – when a single folk genus refers to multiple
Linnaean species – is especially common for plant genera
and families with high local species diversity (see Jernigan,
2006). Under-differentiated taxa in Baniwa ethnobotany
include some Annonaceae (e.g. Duguetia spp.), Lauraceae
(especially Ocotea and Nectandra), Leguminosae (Inga spp.),
Myrtaceae and others. Some of these same groups (e.g.
M. B. Abraa ˜ o et al.
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Lauraceae and Myrtaceae) are notoriously difficult to identify
to species or even genus by specialists (see Gentry, 1993).
Thus there is no guarantee that all examples of a given
Baniwa plant name in the data base would correspond to
identical botanical species if botanical collections in the plots
had been possible. However, by emphasizing the most common
Baniwa plant names – which presumably refer to the most
abundant or salient local species (see Berlin, 1992, p. 21; on the
role of ecological and taxonomic salience in folk biology) – the
quantitative ordinations minimize the overall impact of such
informant variation. All of the indicator species under study
(Table 2) are distinctive taxa that leave little margin for error.
Likewise, many of the other commonly mentioned plant names
(see Fig. 6) represent highly distinctive species that are not
easily confused, even by less knowledgeable Baniwa informants.
Figure 6 demonstrates how the ethnobotanical species
composition varies along the environmental gradient (as
inferred from Fig. 4) comprising the 15 study plots. Here, the
top 20 Baniwa plant names, in terms of total basal area in the
plots, are stacked vertically. The histograms indicate the basal
area for each Baniwa plant name in each plot. Plots areorganized horizontally according to the quantitative NMDS
score from single-axis ordination. There is an orderly
movement of plant associations diagonally from the bottom
right side of the graph (low, open savanna-like campinarana)
to the top left side of the graph (high, closed-canopy forest,
transitional to terra firme). Note that species towards the
middle of the figure exhibit a wider distribution. Yet the
plants that the Baniwa use as indicator species (marked with
asterisks in Fig. 6) are clustered at the top and bottom of the
figure, suggesting that they are less tolerant of environmental
variation and hence are useful indicator species of the
campinarana–terra firme transition.
PCA of the variation in pixel reflectance between the 15
composite plots on Landsat images reinforces the ecological
validity of Baniwa habitat knowledge. In Fig. 7, plots assigned
by the Baniwa to the same vegetation type have similar
patterns of reflectance in the satellite image (i.e. they are close
to one another in the scatter plot, especially along the
horizontal axis), despite geographical (inter-community) sep-
aration. The exception is an holima: the two examples of
an holima vegetation sampled are widely separated along the
horizontal axis, though they are close along the vertical axis
(Fig. 7, data points 1). The same pattern was noted above for
an holima in quantitative NMDS ordination of the floristic data
(Fig. 4, data points 1). This congruence between floristic andsatellite data suggests that some secondary environmental
gradient (detected in both NMDS and PCA axis 2), may be
deterministic for this vegetation type (note that an holima is
associated with moist forest along stream banks or stream
headwaters).
The pattern that emerges from PCA for satellite reflectance
is nearly identical to that found in the quantitative NMDS for
the floristic inventory data. Thus, traditional Baniwa habitat
classification of hamaliani forest is confirmed independently
by two different modes of scientific data collection: ground-
based floristic inventories and orbital satellite sensors. More-
over, brightness values for the plots were highly correlated with
single-axis quantitative NMDS scores of the respective floris-
tic inventories (Pearson’s correlation coefficient = 0.96;
R2 = 0.92; see also Fig. 8). Thus, familiarity with Baniwa
ecological and botanical classification should allow one to
predict the predominant floristic composition (i.e. most
abundant species) of an unvisited campinarana forest at least
within the region encompassed in Fig. 1 and possibly beyond,
based only on satellite reflectance, with an accuracy of 80%
(0.92 from the correlation · 0.87 variation explained by
single-axis NMDS). However, this predictive relationship
between satellite reflectance and floristic composition would
have to be tested through botanical voucher collection, and
may prove less applicable to upland terra firme and flooded
igapo habitat types, where variation in canopy cover and basal
area may be less dramatic than in white-sand campinarana.
DIS CUS S IO N – T RIANGULAT ING INDIGE NO US
HABIT AT K NO W LE DGE
In order to visualize the concordance between Baniwa habitat
knowledge and forest composition and structure, we combine
our three data sets into a single figure: (1) the ethnobotanical
inventories of 15 tree plots representing six Baniwa forest
types, with all trees ‡ 5 cm d.b.h. identified using Baniwa
nomenclature; (2) the quantitative measures of forest structure
in the plots (basal area, canopy height, canopy cover, etc.); and
(3) the pixel brightness on Landsat TM satellite images
averaged over the three bands in each composite plot area.
NMDS ordinations of the first data set revealed a clear gradient
of floristic composition that was correlated with measures of
forest structure (basal area, canopy height, satellite reflectance)
in the second two data sets (Fig. 8). At one end of the gradient
are low, open-canopy campinarana forests with low basal area
and high Landsat reflectance (Fig. 8, right side – smaller,
lighter dots), and at the other end are high, closed-canopy
forests with high basal area and low reflectance (Fig. 8, middle
to left side – larger, darker dots). Though the plots were not
chosen in anticipation of this result, it turns out that two of the
study types represent savanna-like campinaranas (see Fig. 4,
far right side, codes 5A-B and 6A-B), two represent closed-
canopy forests transitioning to terra firme (Fig. 4, centre, codes
3 and 4) and two represent humid habitats near stream banks
or stream headwaters, beginning the floristic transition to
igapo flooded forest (Fig. 4, left side to middle, codes 1 and 2).This pattern is largely congruent with the habitat types
described in neighbouring areas of Venezuela as caatinga baja,
caatinga alta and bana (Jordan, 1985).
Thus, indigenous habitat knowledge, in combination with
satellite and computerized data analysis technologies, provides
an efficient way of apprehending habitat variation and
assessing vegetation patterns within a local or possibly regional
landscape (see also Shepard et al., 2004b). This is especially the
case where indigenous habitat classification schemes rely on
locally dominant indicator species – notably palms and
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bamboos (see Fleck & Harder, 2000; Shepard et al., 2001) –
that affect the forest canopy at scales detectable to satellite
sensors (Shepard et al., 2004b). Indeed, the correlation
between satellite and floristic data is so strong within
Baniwa-defined vegetation types that it appears possible to
predict the predominant species composition of unvisited
campinarana forest sites within the study area and perhaps
beyond. However, pending botanical voucher collections
would be required to confirm this predictive relationship
independently of Baniwa botanical classification. Moreover,
informant variation concerning the naming of the less
common or distinctive plant species introduces clear distortion
into the results when qualitative (presence/absence), as
opposed to quantitative (relative abundance, basal area),
indices of similarity are used (see Fig. 5). This result highlights
some limitations on the use of unverified traditional knowl-
edge to study biodiversity. A certain degree of variation is
inherent in the use of local plant names and field identifica-
tions without botanical verification, especially with regard to
less common or distinctive species. However, on these sites the
effect of informant bias was not enough to seriously distort thequantitative ordinations, nor the overall success of this exercise
in ethnobotanical ground-truthing. Especially when consider-
ing the time, cost and bureaucratic complications associated
with full-scale botanical inventories across multiple habitat
types in an area as vast as the Amazon basin (see Tuomisto,
1998), the ecological and botanical insights gleaned from
knowledgeable local informants may provide a welcome and
cost-effective short-cut for assessing habitat diversity.
Such applications of local knowledge could enrich and
possibly streamline studies of biodiversity in the upper Rio
Negro and elsewhere. For example, participatory biodiversity
surveys currently under way in a larger network of indige-
nous communities on the Icana rely on Baniwa habitat
classification to attain the maximum number of habitat types
for zoological and floristic sampling (Instituto Socioambien-
tal, 2005). Full community support and participation – not to
mention the research experience provided by this prior study
– have facilitated the collection permit process for this later
study. Though not discussed here (see Andrello, 1998; Abraao
et al., in press), certain Baniwa vegetation types are associated
with key resources distributed patchily throughout the
landscape, notably land suitable for farming specific crops,
fish and game populations, and palms and other edible fruits
or useful plants. Such information has clear applications in
territorial mapping and land management (see InstitutoSocioambiental, 2003).
As Sheil & Lawrence (2004, p. 637) note, ‘involving
communities is one way to do more biology in the tropics,
but it is also an ethically defensible way to set about developing
effective conservation’. They lament, however, that ‘most
biologists remain slow to approve and implement these
approaches… [such] neglect means that opportunities are
being missed’. By paying more attention (and respect) to local
knowledge about biodiversity, tropical biologists and conser-
vationists might contribute to the advancement of their own
science while helping indigenous peoples play a greater role in
assessing and managing their lands and resources.
ACK NO W LE DGE M E NT S
The Amazonas State Foundation for Research (FAPEAM)
provided field support funds, a Master’s student stipend to
M.B.A. and a Young Amazonian Scientist stipend to J.C.B..
Brazil’s National Science Council (CNPq) also funded the
initial phases of this study. Irineu Laureano, Andre Fernando,
Mario Farias and Armindo Brazao of OIBI provided assis-
tance in many logistical issues, as did Rosilene, Sucy,
Fernando and others at the Sao Gabriel office of Instituto
Socioambiental. We are especially indebted to Alberto from
the community of Jandu Cachoeira, Roberto from Juivitera
and Custodio from Aracu Cachoeira for their invaluable
wisdom, knowledge and patience; thanks also to Nivaldo
Juliano of Tucuma for help in locating, tagging and
measuring some plots. Special thanks go to Adeilson Lopes
da Silva. We thank three anonymous referees for many
helpful suggestions on the manuscript. Finally, we thank Paulo Assuncao Apostolo for field verification and correction
of several botanical identifications.
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B I O S K E T C H E S
Marcia Abraa ˜ o received her Master’s degree in Ecology from
Instituto Nacional de Pesquisas da Amazonia in 2005, and is
now a consultant for Instituto Socioambiental in a participa-
tory assessment of biodiversity in the upper Rio Negro. Her
interests include remote sensing of tropical forests and
resource management by indigenous peoples.Bruce Nelson is a researcher in the Ecology Department at
Instituto Nacional de Pesquisas da Amazonia. His interests
include phytogeography, ethnobotany, remote sensing and
natural forest disturbance.
Glenn H. Shepard Jr is an anthropologist and ethnobotanist
who has worked with indigenous groups of Peru, Brazil and
Mexico on traditional ecological knowledge and ethnomedical
systems.
Editor: Jorge Crisci
M. B. Abraa ˜ o et al.
2248 Journal of Biogeography 35, 2237–2248ª 2008 The Authors. Journal compilation ª 2008 Blackwell Publishing Ltd