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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



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.

2238 Journal of Biogeography 35, 2237–2248ª 2008 The Authors. Journal compilation ª 2008 Blackwell Publishing Ltd



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

Journal of Biogeography 35, 2237–2248 2239ª 2008 The Authors. Journal compilation ª 2008 Blackwell Publishing Ltd



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.





(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.





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.





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).





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.





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





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



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