LINKING FLORISTIC PATTERNS WITH SOIL HETEROGENEITY AND SATELLITE IMAGERY IN ECUADORIAN AMAZONIA

352

Ecological Applications, 13(2), 2003, pp. 352–371q 2003 by the Ecological Society of America

LINKING FLORISTIC PATTERNS WITH SOIL HETEROGENEITY ANDSATELLITE IMAGERY IN ECUADORIAN AMAZONIA

HANNA TUOMISTO,1,4 AXEL DALBERG POULSEN,2 KALLE RUOKOLAINEN,1 ROBBIN C. MORAN,2,5

CATALINA QUINTANA,3 JORGE CELI,3 AND GUSTAVO CANAS3

1Department of Biology, University of Turku, FIN-20014 Turku, Finland2Department of Systematic Botany, Aarhus University, Nordlandsvej 68, DK-8240 Risskov, Denmark

3Departamento de Ciencias Biologicas, Herbario QCA, Pontificia Universidad Catolica del Ecuador,Av. 12 de Octubre # 1076 y Carrion, Apartado 17-01-2184, Quito, Ecuador

Abstract. Florisitic ground surveys in tropical rain forests are laborious and time con-suming, so we tested to what degree reflectance differences visible in Landsat ThematicMapper (TM) satellite images can be used to predict differences in florisitic compositionand species richness among rain forest sites. To gain ecological understanding of the rainforest ecosystem, we also tested to what extent variation in these vegetation characteristicscan be explained by edaphic site conditions. The study was conducted in a relativelyhomogeneous area of Amazonian rain forest in Yasunı National Park, Ecuador. We estab-lished 27 transects of 5 m 3 500 m within an area of ;20 km 3 25 km to study edaphicand floristic patterns mainly within the tierra firme (non-inundated) forest. In each transect,soil samples were collected for chemical and textural analyses, and the abundance of eachspecies belonging to two understory plant groups, pteridophytes (ferns and fern allies) andthe Melastomataceae, was assessed. Floristic similarity between transect pairs varied widelyand ranged from almost no overlap in species composition to very high overlap. The among-transect floristic similarity patterns of the two plant groups were strongly correlated witheach other no matter whether presence–absence or abundance data were used. The floristicsimilarity patterns were also strongly correlated with the similarity in pixel values of theinfrared bands in the Landsat TM satellite image and with the similarity in most of themeasured soil variables. Similarity in species richness, on the contrary, was neither cor-related with similarity in pixel values nor with similarity in most of the soil variables. Weconclude that reflectance patterns in satellite images can be efficiently used to predictlandscape-scale floristic and edaphic patterns in tierra firme rain forest. Predicting patternsin species richness, on the other hand, is not possible in the same straightforward manner.These results have important practical implications for land use and conservation planningas well as for ecological and biodiversity research.

Key words: Amazonia; floristic composition; Mantel test; Melastomataceae; principal coordinatesanalysis; pteridophytes; satellite imagery; soils; species richness; tropical rain forest.

INTRODUCTION

An important issue in Neotropical rain forest ecologyis to what extent the floristic composition of a givensite is determined by site factors such as soil fertilityand drainage. It is generally agreed that plants react tosuch obvious environmental differences as prolongedseasonal inundation vs. no inundation and extremelypoor white sand soils vs. more fertile soils. However,recent studies in the Neotropics disagree about the im-portance of more subtle edaphic differences for plantspecies composition. Many studies have emphasizedtheir role (Young and Leon 1989, van der Werff 1992,ter Steege et al. 1993, Tuomisto and Ruokolainen 1994,Clark et al. 1995, 1998, 1999, Tuomisto et al. 1995,

Manuscript received 17 August 2001; revised 13 May 2002;accepted 8 July 2002; final version received 22 August 2002.Corresponding Editor: C. A. Wessman.

4 E-mail: [email protected] Present address: New York Botanical Garden, Bronx,

New York 10458-5126 USA.

1998, Tuomisto and Poulsen 1996, 2000, Malo andOlano 1997, Ruokolainen et al. 1997, Sabatier et al.1997, Ruokolainen and Tuomisto 1998, Tuomisto1998), whereas others have found them less important(Duivenvoorden 1995, Pitman et al. 1999, 2001, Conditet al. 2002) or have argued that disturbances, seed dis-persal, herbivory, diseases, or random drift may bemore decisive (Hubbell and Foster 1986, Condit 1996,Condit et al. 1996).

While many factors certainly play a role in deter-mining species assemblages, edaphic site characteris-tics are relatively constant in time and therefore canbe expected to give rise to relatively stable and pre-dictable patterns in the vegetation (for a classic workin the boreal zone, see Cajander [1926]). Furthermore,if site factors are important, the floristic patterns ofindependent plant groups should be correlated. On thecontrary, any floristic patterns created by such factorsas random drift or biotic interactions should be ex-pected to be uncorrelated both with edaphic site factors

April 2003 353FLORISTIC PATTERNS AND SATELLITE IMAGERY

and when measured for different plant groups. Suchdifferent predictions of the two explanatory models canbe used to test which of them provides a better expla-nation for the observed floristic patterns in any partic-ular case.

General forest classification schemes for Amazoniahave distinguished only a few different vegetationtypes, especially within the vast, structurally homo-geneous-looking non-inundated areas (e.g., Prance1989; see also Kuchler [1982] and Saatchi et al.[2000]). Although this may suggest that edaphic factorsare relatively unimportant for plant species distribu-tions in these forests, it may also reflect the paucity ofstudies focused on documenting species compositionand diversity patterns in relation to the environmentalfactors potentially affecting them. Despite extensivesearches, we have found only 18 studies that documentchemical composition and/or texture of soils and relatethese to the floristic composition of the same sites intropical lowland rain forests anywhere in the world,and only five of these were conducted in Amazonia (inAmazonia: Duivenvoorden 1995, Tuomisto et al. 1995,Ruokolainen et al. 1997, Ruokolainen and Tuomisto1998, Ruokolainen and Vormisto 2000; elsewhere:Austin et al. 1972, Knight 1975, Newbery and Proctor1983, Proctor et al. 1983, Gartlan et al. 1986, Newberyet al. 1986, 1988, Baillie et al. 1987, Hart et al. 1989,Basnet 1992, Johnston 1992, Sabatier et al. 1997, Webband Peart 2000).

Edaphic differences have also been suggested to af-fect local plant species richness (alpha diversity) withintropical rain forests. Some studies indicate that speciesrichness increases with soil fertility (Gentry 1988,Tuomisto and Poulsen 1996, Tuomisto 1998), whereasothers have found the opposite (Huston 1980) or norelationship at all (Clinebell et al. 1995). Consequently,it is still unclear whether edaphic gradients are asso-ciated with species richness gradients in tropical rainforests.

Another unresolved question for Amazonia is theecological interpretation of the spectral variation vis-ible in enhanced color composites based on Landsatsatellite images (Tuomisto et al. 1994, 1995, Kalliolaet al. 1998, Tuomisto 1998). Although atmospheric ef-fects modify the actual reflectance values, the spectraldifferences in cloud-free satellite images indicate dif-ferences in the reflectance characteristics of the groundcover. Sometimes the spectral patterns can easily beidentified as different vegetation types, such as variouskinds of swamps and forests on white sand soils (Kal-liola et al. 1991, Tuomisto et al. 1994, Ruokolainenand Tuomisto 1998).

However, most of the variation visible in satelliteimages remains undocumented in the field. This is es-pecially true in the non-inundated areas (tierra firme)where no obvious structural differences exist amongforest sites. Therefore, it is not known whether thereflectance patterns indicate the presence of edaphi-

cally different site types (as suggested by Tuomisto etal. [1995]) or transient variation determined by distur-bance, seed dispersal, and species interactions in anessentially homogeneous environment (as suggested byCondit 1996).

Clarifying which view is closer to reality has im-portant practical implications. This is because the twoviews propose different answers to important questionsin scientific research (to what extent can results froma given field site be extrapolated?), biodiversity con-servation (how should conservation areas be plannedto provide efficient protection of species and habitats?),and land use planning (to what extent should local dif-ferences in soils and species composition be taken intoaccount?). If the patterns visible in Landsat imagesreliably reflect vegetation and soil characteristics, theyprovide a powerful tool for obtaining regional and land-scape-scale information from rain forest areas that arepractically inaccessible on the ground. On the otherhand, if the satellite image patterns are transient andrandom, they provide little useful information for long-term planning.

We proposed earlier (Tuomisto et al. 1995) that Ama-zonia harbors more forest types than generally be-lieved, that floristic composition of the forest correlateswith edaphic conditions, and that the reflectance pat-terns of satellite imagery indicate these forest char-acteristics. The field data on which these hypotheseswere based came from an area with a pronouncededaphic gradient ranging from nutrient-poor white sandsoils to much more fertile clayey soils. In the presentpaper, we test these hypotheses in an area that appearedhomogeneous: no significant variation in soils or veg-etation was known, and the satellite images showedlittle variation in comparison with the areas studiedearlier.

We focused the floristic studies on two plant groups:understory pteridophytes and the Melastomataceae.These were chosen instead of canopy trees becausetheir smaller size and lower number of species makeit possible to obtain more representative samples (i.e.,to study more sites and to record a higher number ofindividuals per species) in a given amount of time.Using understory plants to field-verify satellite imagepatterns provides a stringent test indeed, because thespectral characteristics of the forest are mainly deter-mined by the canopy. Therefore, satellite image re-flectance patterns can only be correlated with floristicpatterns in understory plants if the latter are also cor-related with patterns in the spectrally important canopyproperties. Earlier studies indicate that if such corre-lations exist, they could be due to patterns in the flo-ristic composition of trees (Tuomisto et al. 1995, Ruo-kolainen et al. 1997, Ruokolainen and Tuomisto 1998,Vormisto et al. 2000), although structural or physio-logical characteristics of the forest canopy may also beinvolved.

354 HANNA TUOMISTO ET AL. Ecological ApplicationsVol. 13, No. 2

FIG. 1. Location of the study area in Yasunı National Park, Amazonian Ecuador. The rectangle marked A correspondsto the area covered by the satellite image in Fig. 2.

Our study area in Yasunı National Park, Ecuador,includes both non-inundated and inundated areas. Theinundated areas can be divided into a few structurallydistinct vegetation types such as poorly drainedswamps and relatively well-drained but seasonally in-undated floodplain forests. The non-inundated areashave traditionally been considered homogeneous andare not known to harbor any edaphically differentiatedforest types. Therefore, the present study is mainly fo-cused on the tierra firme areas.

The study area includes a permanent 50-ha tree plotthat belongs to a unique worldwide study program(Condit 1995, Romoleroux et al. 1997, Valencia et al.,in press a, b). Our results on floristic and edaphic var-iation in the area may be used to evaluate how widelythe results from the 50-ha plot can be extrapolated toits surroundings.

The present paper asks: (1) Can floristic and/or spe-cies richness patterns within the study area be predictedfrom reflectance patterns in satellite imagery? (2) Towhat extent can floristic and/or species richness pat-terns within the study area be explained by patterns insoil characteristics? Which soil characteristics seem tobe most important? (3) How similar are the landscape-scale floristic patterns of the two plant groups?

METHODS

Data collection

Fieldwork was carried out in Amazonian Ecuador inthe vicinity of the Yasunı Research Station, which issituated in Yasunı National Park and operated by Pon-tificia Universidad Catolica del Ecuador. The sampledsites were in the area delimited by 08 329–08 469 S and768 219–768 329 W, an area of ;25 km 3 20 km (Figs.1 and 2). The access to the area is a road constructedfor oil drilling that is presently controlled by the oilcompany to prevent colonization within the NationalPark.

Yasunı National Park ranges from ;200 to 300 mabove sea level and has a tropical, humid, and almostaseasonal climate. Mean annual precipitation exceeds2400 mm, and no month receives ,100 mm of rain onaverage, although drier months may occur in someyears (Canadas 1983, Balslev et al. 1987).

The landscape in Amazonian Ecuador has beenformed by different sedimentation and erosion pro-cesses since the Tertiary, and fluvial deposition causedby river dynamics has been the main process shapingthe landscape in the Quaternary. A soil study at An-angu, in the northern part of the Yasunı National Park,described the well-drained soils as Udults that are clay-


FIG. 2. Part of a Landsat Thematic Mapper image covering the study area in Yasunı National Park, Amazonian Ecuador(1 September 1995; color composite obtained using the histogram equalized bands 4, 5, and 7 for red, green, and blue,respectively). The 27 inventoried transects are shown in white and identified with the numbers used in the text. The white-bordered rectangle is a permanent 50-ha plot. Black meandering lines represent the rivers Tiputini (larger of the two) andTivacuno. Blue indicates oil road and clearings. The Yasunı biological station is situated in the clearing 1 km north of the50-ha plot. For a description of the forested areas within the image, see Results.


ey, kaolinitic, and rich in aluminum (Korning et al.1994). Vegetation in the study area consists mainly ofclosed-canopy non-inundated tierra firme forests. Thetierra firme areas are interrupted by river floodplainswith seasonally inundated forests and by swamps dom-inated by the palm Mauritia flexuosa L.f.

A Landsat Thematic Mapper (TM) image that cov-ered the study area (path 9, row 60, quadrant 4) on 1September 1995 (i.e., 7 mo prior to the commencementof field work) was obtained from Centro de Levanta-mientos Integrados de Recursos Naturales por SensoresRemotos (CLIRSEN), the institute that manages theEcuadorian ground receiving station at Cotopaxi. Apreliminary interpretation of the raw image was usedto stratify the sampling in order to include topograph-ically different sites in the inventory. Subsequently, thesatellite image was rectified using ground controlpoints that had been obtained using non-differentiallycorrected Global Positioning System (GPS) in the field.The root mean square error (RMS) was 2.3 pixels. Forimage display, a color composite was produced usingthe histogram-equalized bands 4, 5, and 7 for red,green, and blue, respectively (Fig. 2). The ER-Mapper5.5 software was used for all satellite image analyses(Earth Resource Mapping 1997).

Soil and floristic studies were conducted at 27 sitesusing line transects of 500 m by 5 m. Most transectswere in non-inundated forests because our main interestwas to study floristic variation within tierra firme, butthree were placed in seasonally inundated areas forcomparison. For practical reasons, most transects wereplaced near the road. In each case, the starting pointwas selected deep enough in the forest to appear un-affected by the forest edge, and the compass bearingto be followed by the transect was selected so that thetransect would remain in undisturbed vegetation. If theterrain was hilly, the transect was started on the top ofa hill and directed down the slope in order to samplethe local variation related to topography.

The transects were georeferenced with the help oflandmarks that were visible both in the field and in thesatellite image or available maps, by referring to thekilometer poles along the oil road, and by using GPS.Given the accuracies of these sources, we estimate thatthe starting points of the transects were positioned onthe satellite image with an error of 50 m at most, butthe error at the end points is expected to be largerbecause identifiable landmarks were usually absent in-side the forest.

A narrow trail (baseline) was opened along the centerof each transect. Topographic profiles of the transectswere measured along the baseline using a clinometer(Suunto, Vantaa, Finland). Measurements were takenevery 50 m and in between wherever the slope of theterrain changed significantly.

Surface soil samples (the top 5 cm of the mineralsoil) were collected near the beginning, middle, andend of each transect. In transects in which the topog-

raphy was either flat or consisted of the slope of a singlehill, the soil samples were evenly spaced and taken atpredefined distances from the starting point of the tran-sect. In transects that covered more than one hill, soilsampling locations were adjusted so that they coincidedwith local extremes of topography; usually the first andthird soil sample represented hill tops, and the secondsoil sample represented an intervening valley bottom.Each of the soil samples consisted of five pooled sub-samples collected within an area of ;5 m 3 5 m. Sam-ples were air dried in the field and stored in plasticbags until transport to the laboratory of the AgriculturalResearch Centre of Finland. Physical and chemicalanalyses were carried out using standard procedures(van Reeuwijk 1993) and included: particle size (per-centage of clay, silt, and sand), soil pH (in 1 mol/LKCl), loss on ignition at 4208C (LOI; a proxy for or-ganic material in the soil), phosphorous (Bray method),aluminum (in 1 mol/L KCl), and exchangeable bases(calcium, potassium, magnesium, and sodium in 1 mol/L NH4OAc at pH 7). In five samples the clay mineralscoagulated to the extent that it was not possible todifferentiate between clay and fine silt, so the textureinformation from these samples was not used in thepresent analyses.

Pteridophytes (ferns and fern allies) and Melasto-mataceae (sensu stricto, i.e., excluding Memecylaceae)were identified to species and counted within an esti-mated 2.5 m on either side of the baseline of eachtransect, yielding a sample size of 0.25 ha per transect.For pteridophytes, only individuals with at least oneleaf longer than 10 cm were recorded, and epiphyticand climbing individuals were included only if theyhad green leaves at a height ,2 m above ground. Forvegetatively spreading species such as Selaginella,each rooting stem was counted as one individual. Allindividuals of Melastomataceae, except seedlingssmaller than ;5 cm long, were recorded. Voucher col-lections for all species are deposited in herbaria in Ec-uador (QCA, QCNE), Denmark (AAU), Finland(TUR), and United States (NY). Unicates are kept inQCA.

Data analyses

Most investigations that have used satellite images(optical or radar) for vegetation studies in the Neo-tropics have focused on digital classification of the im-agery (Townshend et al. 1987, Garcia and Alvarez1994, Hill and Foody 1994, Paradella et al. 1994,Foody and Hill 1996, Langford and Bell 1997, Saatchiet al. 2000, but see Kalliola et al. 1991, 1998, Tuomistoet al. 1994, 1995). Digital classification may indeed bedesirable because it can be done quickly and the resultsare repeatable. However, the need to define rigid spec-tral classes and to evaluate the digital classificationaccuracy against another, independently derived landcover classification poses several severe problems.Consequently, classification accuracy in rain forest ar-


eas is generally poor unless the vegetation types ofinterest are structurally clearly different from each oth-er. Since the forests in our study area appear structurallyhomogeneous, we have taken a different approach: wequantified to what extent the variation in pixel valuesof the image is correlated with variation in the floristiccomposition of the forest.

On top of each of the 27 transects, an area of 50 hawas delimited in the Landsat TM satellite image. Thedelimited area was ;1000 m 3 500 m in size, and itwas centered upon the transect so that it included 250m of forest to both sides from the estimated transectlocation, which ensured that each transect was entirelycontained within the corresponding delimited area. Alarge area was used also to make sure that all localvariation, e.g., that caused by topography, would bewell represented in the pixel values. Most delimitedareas contained ;550 pixels, although in some casesthe area had to be made smaller, because clouds, cloudshadows, or obviously different terrain such as riversor roads made it impossible to obtain samples of alarger area that would still represent the same kind ofterrain as the transect. An additional area was delimitedon top of the permanent 50-ha plot (Romoleroux et al.1997) to examine how the plot relates to the generalspectral variation in the region.

Only data from bands 1–5 and 7 were used; the ther-mal infrared band 6, which has a much larger pixelsize than the other bands, was excluded. The raw pixelvalues were extracted for each delimited area, and meanpixel value was calculated separately for each band andeach area. No atmospheric correction was applied tothe image prior to pixel value extraction (cf. Song etal. 2001). Correction algorithms that assume a hori-zontally homogeneous atmosphere would have no ef-fect on the results, and a horizontally heterogeneousatmosphere cannot be reliably modeled without de-tailed measurements on the atmospheric conditions atthe time of image acquisition and/or the presence ofwell-distributed dark objects with known reflectanceproperties within the image.

All the numerical analyses explained below arebased on resemblance matrices, each of which consistsof all possible pair-wise comparisons of the samplingunits using a distance or similarity measure that is ap-propriate for the data at hand. In the present analyses,when a distance measure (D) had to be converted to asimilarity measure (S ) or vice versa, the formula S 51 2 D was used.

Satellite-derived resemblance matrices based on Eu-clidean distance were calculated using the mean pixelvalues of either a single band or one of three bandcombinations (visible wavelength bands, infraredbands, and all bands). When more than one band wasused for the calculation of a distance matrix, the pixelvalues of each band were standardized to zero meanand unit variance before calculating the distances in

order to give each band equal weight (Legendre andLegendre 1998).

Edaphic resemblance matrices based on Euclideandistance were calculated using either one soil variableor several soil variables in combination. Before cal-culating the distances, all concentrations of elementswere transformed to their natural logarithms. This wasdone because a given difference in element concentra-tion is ecologically more important at low than at highconcentrations, and at the same time the transformationmade the frequency distributions more symmetric.When several soil variables were used for the samedistance matrix, each variable was standardized to zeromean and unit variance before calculating the distanc-es. A Euclidean distance matrix of elevation differenceswas computed using the vertical difference between thelowest and highest points within each transect as asimple measure of topographic variation. A geograph-ical distance matrix was computed using the transectmidpoint coordinates that were read off the satelliteimage after the transects had been drawn on the image.

Floristic resemblance matrices were calculated usingtwo similarity indices: the Sørensen index and theSteinhaus index. These indices are mathematicallyidentical, but the Sørensen index uses only presence–absence information while the Steinhaus index also in-cludes abundance information (Legendre and Legendre1998). Consequently, it is convenient to use these in-dices for examining whether abundance data give dif-ferent results than presence–absence data. Number ofindividuals was used as a measure of species abun-dance. Similarity matrices were calculated separatelyfor the two plant groups (pteridophytes and the Me-lastomataceae).

Resemblance matrices for species richness (numberof species per transect) were computed for pterido-phytes and the Melastomataceae separately. Euclideandistance was used in both cases. Species diversity ineach transect was measured using Shannon’s H9.

Principal coordinates analyses (PCoA) were per-formed to illustrate in ordination diagrams the dis-tances of the sites as measured with the pixel valuesin the satellite image, edaphic characteristics, and flo-ristic composition of pteridophytes and the Melasto-mataceae. Generally, ordination diagrams show similarsites close together and dissimilar sites farther apart.When a Euclidean distance matrix is used in PCoA,the ordination results are identical to those that wouldbe obtained if principal components analysis (PCA)were run on the same raw data (Legendre and Legendre1998). To complement the results obtained with ordi-nation, the corresponding similarity matrices were usedto obtain a hierarchical classification based on un-weighted arithmetic average clustering (UPGMA).

Mantel tests of matrix correspondence were run tofind out whether similarities in floristic compositionand species richness correlated with similarities in pix-el values and edaphic conditions and to compare dis-


tribution patterns of the two plant groups. The stan-dardized form of the Mantel test was used, which com-putes the Pearson correlation coefficient between thecell values of two resemblance matrices. The Manteltest is especially appropriate for multivariate data, be-cause it allows for several variables to be combinedbefore the correlation coefficient is calculated; linearPearson’s correlation can only be computed betweentwo variables at a time, say one species and one en-vironmental factor. When interpreting the results, it isimportant to keep in mind that the Mantel test giveslower r values than the corresponding linear Pearson’scorrelations in univariate cases where both can be cal-culated on the same data, but that the two methodsgenerally agree on whether the correlation is statisti-cally significant (Legendre 2000). The statistical sig-nificance of each correlation was determined by a Mon-te Carlo permutation test to avoid problems related toautocorrelation and nonnormal distributions of themeasured variables. In all cases, 999 permutations wereused, which allows testing of the statistical significanceat the P , 0.001 level for each individual correlation.The computations on resemblance matrices were doneusing the R-package and the linear correlations withthe program Corr Permute (available online).6

Multiple regression analyses on distance matriceswere run to select the combinations of environmentalvariables that contributed most to explaining the var-iation in each of the floristic resemblance matrices.These analyses were done with the program Permute3.4 (alpha version; Legendre et al. 1994) in a backwardelimination procedure. Initially, all 13 independent var-iables (11 soil measurements, elevation difference, andgeographical distance) were included, and a permuta-tion test with 999 permutations was conducted. Thevariable with statistically the least significant partialregression coefficient was excluded, and this procedurewas repeated until all variables that remained in themodel had partial regression coefficients with P , 0.05after Bonferroni correction (see Legendre et al. 1994).For the final models we report the standard partial re-gression coefficients (B) and the coefficients of mul-tiple determination (R2).

RESULTS

Satellite image

A visual inspection of the Landsat TM image (Fig.2) revealed several distinct features. The roads andclearings established by the oil company and the Ti-putini and Tivacuno rivers served as landmarks duringfieldwork. Although the image is almost cloud-freeover the study area, some small clouds and their shad-ows disturbed image interpretation at the detailed scale,especially in the eastern part of the image.

6 URL: ^http://www.fas.umontreal.ca/BIOL/Legendre/indexEnglish.html&

The topography of the terrain is revealed by the pat-terns formed by shadows of hills, which are absent inflat areas but contrast strongly with the well-lit sidesof the hills in rugged terrain. In the color compositepresented in Fig. 2, the river floodplains (representedby transects 6 and 22) can be distinguished as topo-graphically smooth, relatively dark surfaces withbrighter areas corresponding to levees close to the riverchannel. The areas covered by Mauritia flexuosaswamps within the floodplains appear red-brown. Afew creek valleys are also evident in the image (oneof them is represented by transect 13).

In the non-inundated areas, a rough distinction canbe made between areas with high hills and a reddishcolor in the satellite image and areas with small hillsand a darker color in the image. However, this dis-tinction is not clear-cut, and a gradient of differenttopographies and colors can be seen between the twoextremes. In general terms, the area around the researchstation (represented by transects 8–11 and 18–27, aswell as the 50-ha plot) is darker with a relief of lowhills, and in the areas to the northwest (transects 1–7)and southwest (transects 12–17), the landscape is dom-inated by large hills and appears reddish in the image.

In the southeast corner of the image, a large yellow-ish patch indicates old secondary vegetation near theTivacuno River. According to Humberto Ahua Api, alocal informant from the Waorani tribe, this area hasbeen subject to shifting cultivation and was inhabitedby several families until ;30 yr ago. The whole areawas not necessarily in use at the same time; some ofthe successional characteristics that appear in the imagemay date several decades further back in time. Thegeneral form of the secondary patch resembles that oflarge wind blowdowns, which have been documentedin both Brazil (Nelson 1994, Nelson et al. 1994) andColombia (Etter and Botero 1990, Etter et al. 1997).The area may have been naturally disturbed immedi-ately before being colonized by the Waorani, who havetraditionally taken advantage of natural gaps when es-tablishing cultivations. Since the focus of the presentpaper is in old-growth forests, these secondary areaswere not included in the analyses. However, their pres-ence is a reminder that Amazonia has had human in-habitants for a long time, so not all patterns in theforests need to be natural even in areas that are thoughtto be pristine.

Topography and soils

The topography in the transects varied from essen-tially flat to steeply hilly. In most transects, the mea-sured elevation difference gives a good estimate of theactual size of the hills at the site. A noteworthy ex-ception is transect 12, within which the recorded ele-vation difference was 61 m (Table 1). This underesti-mates the height of the corresponding hill by at least10 m, because 500 m of transect was not enough toreach from the top of the hill to the nearest valley.


TABLE 1. Soil characteristics and the difference in elevation between lowest and highest points in 27 transects in AmazonianEcuador.

Transect pH

Cation concentration (cmolc/kg)

Ca K Mg NaCa 1 K 1Mg 1 Na Al

P(mg/kg)

LOI†(%)

Clay(per-

centage,0.002

mm)

Silt(per-

centage0.002–0.063mm)

Sand(per-

centage.0.063

mm)

Eleva-tion

differ-ence(m)

123456789

1011

3.803.723.643.863.503.513.863.523.583.913.64

9.3013.0310.47

8.946.585.888.683.470.473.593.05

0.250.180.260.160.210.430.250.180.200.170.21

2.913.263.122.542.142.732.141.220.451.391.51

0.050.050.030.050.040.070.030.020.040.060.05

12.5016.5213.8911.70

8.979.11

11.104.891.165.224.81

4.276.275.102.19

10.009.173.77

10.8217.91

5.7410.38

5.226.215.626.047.889.029.053.452.385.785.13

7.48.79.87.29.5

15.18.98.2

10.48.59.7

15.34.7

28.07.3

26.067.721.725.038.7

2.013.3

40.066.042.340.750.032.037.055.752.363.763.3

44.729.329.752.024.0

0.341.319.3

9.034.323.3

3539511827

44752152812

12131415161718192021222324252627

4.203.713.803.704.013.763.693.333.673.413.553.503.563.543.533.25

9.9013.1619.87

9.8515.0710.00

2.024.119.531.768.693.202.163.492.061.84

0.260.280.360.220.310.200.180.210.210.170.250.170.170.220.190.24

3.224.526.992.604.162.880.931.794.190.922.501.131.151.300.760.57

0.050.040.070.060.030.080.020.040.040.030.130.030.100.180.030.04

13.4218.0027.2912.7319.5613.17

3.156.15

13.972.88

11.564.523.585.193.042.69

1.103.423.868.714.314.349.32

10.897.24

11.989.938.02

17.1912.25

7.1821.04

4.599.098.818.88

14.3616.36

3.387.448.144.457.425.973.924.834.665.64

10.311.611.210.2

8.09.37.6

10.99.68.6

11.57.99.5

10.88.7

10.8

25.057.044.320.020.013.312.032.026.025.052.314.728.330.020.030.5

41.737.035.338.539.050.358.347.353.064.047.356.353.347.357.543.0

33.36.0

20.341.541.036.329.720.721.011.0

0.329.018.322.722.526.5

61114014421625113925

32026302517

Mean 3.66 7.04 0.23 2.33 0.05 9.66 8.39 6.81 9.6 25.9 48.6 25.5 27

Note: Reported soil properties are means of three soil samples taken near the beginning, middle, and end of each transect.† Loss on ignition.

FIG. 3. Ternary plot indicating the relative masses of clay,silt, and sand in the soils of 27 transects in Amazonian Ec-uador. Symbols indicate different mean concentrations of ex-tractable bases in the soil (sum of Ca, K, Mg, and Na) asshown in the inset.

Soils in the 27 transects were acidic, with pH rangingbetween 3.25 and 4.20 (Table 1). The concentration ofexchangeable bases (sum of Ca, K, Mg, Na) variedfrom just above 1 cmolc/kg to more than 27 cmolc/kgand was mainly determined by calcium, which ac-counted for 40–80% (mean, 70%) of the total basecontent. Magnesium also showed much higher concen-trations than potassium and sodium. Aluminum rangedbetween 1.1 and 21 cmolc/kg; in about half of the tran-sects the aluminum content was higher than the calciumcontent, while in the other half the situation was thereverse. Phosphorous contents ranged from 2.4 to 16mg/kg and LOI from 7.2 to 15%.

In soil texture, the silt fraction was very prominent:it ranged from one-third to two-thirds of soil mass inall transects and averaged almost 50%. This contrastswith clay and sand that averaged ;25% of the soil masseach and whose contents were very low in some of thetransects (Table 1). The ternary plot (Fig. 3) shows thatmost of the transects in which the concentration ofexchangeable bases (Ca, K, Mg, Na) was ,8 cmolc/kghad silt contents close to or above 50%, whereas tran-sects with higher base concentrations had lower siltcontents. Within the latter group of transects, the high-


TABLE 2. Linear Pearson correlation coefficients among soil properties measured in 27 transects in Amazonian Ecuador(based on data shown in Table 1).

Variable

Variable

Ca K Mg Na Bases Al P LOI† Clay Silt Sand Elevation

pHCaKMgNaBasesAlPLOIClaySiltSand

0.58 0.160.52

0.500.950.60

20.060.020.110.03

0.561.000.550.970.03

20.7620.6920.2320.64

0.1720.68

0.280.610.410.550.060.60

20.43

20.270.100.700.230.390.140.220.17

20.300.140.700.270.260.180.200.100.85

20.2420.5320.7420.5320.1220.54

0.2920.4220.4520.54

0.540.24

20.280.07

20.220.20

20.460.20

20.6720.7820.11

0.510.320.000.27

20.250.31

20.3920.1120.3920.3220.02

0.40

† Loss on ignition.

TABLE 3. Community characteristics of 27 transects inven-toried in Amazonian Ecuador.

Transect

Pteridophytes

No. indi-viduals

No.species H9

Melastomataceae

No. indi-viduals

No.spe-cies H9

123456789

101112131415161718192021222324252627

105615251643

734129026781587109433381488195310301087209410621146154530781142

8642377205118321617207319802244

476163424534436555494547334147494952444337215548605234

3.13.23.03.03.02.52.93.22.92.72.23.02.62.23.03.23.12.33.02.92.41.93.13.13.43.12.1

117198

59197153135168175372210193

8976

18183

109161276172101216169291307237200248

283619263015262733373915152221242942292735

94443372829

2.93.12.62.73.02.12.72.92.83.23.22.42.32.62.52.92.93.33.13.03.11.73.23.23.12.82.8

TotalMean

45 6081689

13747 2.8

4893181

8928 2.8

Note: Shannon’s diversity index, H9, is based on naturallogarithms.

est clay contents were found in those transects that areaffected by flooding (6, 13, and 22).

Many soil properties were correlated (Table 2). Theconcentrations of calcium and magnesium were highlycorrelated, and since these were the two elements thatdominated the sum of bases, this was also highly cor-related with both. Since aluminum affects soil acidity

(pH), the two are negatively correlated. Aluminum con-tent is negatively correlated also with the sum of ex-changeable bases; during the weathering process, alu-minum accumulates in the soil while the bases areleached away. The high correlation between clay con-tent and LOI indicates that the organic matter contentis generally higher in finer-textured soils. This rela-tionship may not be as strong as it seems, however:the LOI measurement also includes some loss of crystalwater from clay minerals and therefore gives somewhatinflated results in fine-textured soils. Even though el-evation difference is not a true soil property, it cor-relates with soil characteristics such as aluminum andsand content, pH, and LOI. This is probably becausethe topography, just as the soil properties, is to a largedegree determined by the underlying geology.

Pteridophytes

A total of 45 608 individuals and 140 species of pte-ridophytes were counted in the 27 transects. Becausethree species pairs were confused in the field, they havebeen lumped for the numerical analyses, which there-fore only deal with 137 species (see Tuomisto et al. [inpress] for a complete species list indicating represen-tative voucher specimens). The total number of pteri-dophyte individuals found within one transect variedbetween 734 and 3338, the mean being 1689 individ-uals per transect (6760 individuals/ha). The number ofspecies per transect varied between 21 and 65 (mean47; Table 3).

There was no correlation between the number of in-dividuals and the number of species found within atransect (Fig. 4A). Those transects with a low concen-tration of exchangeable bases in the soil (,8 cmolc/kg) had on average more individuals and species thantransects with a higher base content (means, 2018 vs.1426 individuals, 50 vs. 44 species), but there was nodifference in species diversity (mean Shannon’s H9 inboth cases 5 2.8). These tendencies changed if onlythe 24 tierra firme transects were considered, because


FIG. 4. The number of species plotted against the number of individuals found in each of 27 transects in AmazonianEcuador. Symbols indicate different mean concentrations of extractable bases in the soil (sum of Ca, K, Mg, and Na) asshown in the inset. The three gray symbols correspond to the transects in creek and river floodplains.

FIG. 5. Species–area curves showing the increase in observed number of species as a function of the number of transectssampled in Amazonian Ecuador. In two of the curves, transects have been ranked according to the mean concentration ofextractable bases in the soil; the third curve gives the mean species accumulation in 25 random rankings of the transects.

all three transects representing seasonally inundatedforests (6, 13, and 22) had high base contents (.8cmolc/kg) and high numbers of individuals, but verylow numbers of species. Transect 22 was also the onewith the lowest diversity (Shannon’s H9 5 1.9; Table3). Consequently, the tierra firme transects with highbase content had on average even fewer individuals pertransect (1298), but more species (48) and a higherShannon’s H9 (3.0) than when the seasonally inundatedtransects were also included.

In spite of these trends, no statistically significantlinear correlation was found between species richnessand soil base content either among all 27 transects or

when the three transects representing seasonally in-undated forests were excluded.

When a mean species-accumulation curve wasformed by adding the transects to the sample in a ran-dom order (with 25 repeats), a straight line was ob-tained in a semilog plot (Fig. 5A). When the transectswere included in the order of either increasing or de-creasing concentration of exchangeable bases in thesoil, the resulting species-accumulation curves re-mained below the mean curve and had a stepwise form.

The pair-wise similarity values calculated betweentransect pairs ranged from 0.18 to 0.81 (mean 5 0.58)when calculated on the basis of presence–absence data


(Sørensen index) and from 0.02 to 0.66 (mean 5 0.31)when calculated on the basis of abundance data (Stein-haus index).

Melastomataceae

A total of 4893 individuals and 89 species of theMelastomataceae were counted in the 27 transects (seeTuomisto et al. [in press] for a complete species listindicating representative voucher specimens). Thenumber of individuals found within one transect variedbetween 59 and 372, the mean being 181 individualsper transect (724 individuals/ha). The number of spe-cies per transect varied between 9 and 44, the meanbeing 28 (Table 3).

There was a clear positive correlation between thenumber of individuals and the number of species foundwithin a transect (r 5 0.71, P , 0.001; Fig. 4B). Thosetransects with a low concentration of exchangeable ba-ses in the soil (,8 cmolc/kg) had on average clearlymore individuals and species than transects with a high-er base content (means 5 241 vs. 133 individuals, 35vs. 23 species), and the transects with poorer soils alsohad higher diversity values (mean Shannon’s H9 5 3.1vs. 2.6). As with pteridophytes, all three transects rep-resenting seasonally inundated forests (6, 13, and 22)were among the four transects with fewest species, andtransect 22 was also the one with the lowest diversity(Shannon’s H9 5 1.7; Table 3). Excluding these threetransects hardly affected the mean number of individ-uals (increase by 2, to 135) or species (increase by 2,to 25) in the transects with high base contents, but themean Shannon’s H9 increased to 2.8.

A strong linear correlation between species richnessand soil base content (Pearson’s r 5 20.56, P 5 0.004)was observed among the 27 transects, and this corre-lation became even more pronounced (r 5 20.60, P5 0.003) when the three transects representing sea-sonally inundated forests were excluded.

When a mean species-accumulation curve wasformed by adding the transects to the sample in a ran-dom order (with 25 repeats), an almost straight linewas obtained in a semilog plot (Fig. 5B). When thetransects were included in the order of either increasingor decreasing concentration of exchangeable bases inthe soil, the resulting species-accumulation curves re-mained mainly below the mean curve and had a step-wise form. The curve that started with the transectswith high base concentrations showed especially lowspecies numbers until most of the transects had beenincluded.

The pair-wise similarity values calculated betweentransect pairs showed remarkably similar trends withthose observed with pteridophytes; the main differencewas the lower Sørensen index value of the most dis-similar transects and hence a lower mean. The Sørensenindex calculated on the basis of the Melastomataceaeranged from 0.06 to 0.84 (mean 5 0.48), and the Stein-haus index ranged from 0.01 to 0.68 (mean 5 0.28).

Ordinations and classifications

In the PCoA ordination based on pixel values in thesatellite image (Fig. 6A), the second axis can be rough-ly interpreted as a gradient in the concentration of ex-changeable bases in the soils (r 5 20.45, P 5 0.014).The transects with low soil base contents appear in theupper part of the ordination, and the transects withhigher base content appear in the lower part (it is tobe noted here that the ordination itself did not includesoil data, but rather the ordination produced by thepixel values was compared with the soil data a pos-teriori). The UPGMA classification based on pixel val-ues produced two large groups, one consisting of tran-sects with low soil base concentrations (,8 cmolc/kg),the other consisting mainly of transects with higher soilbase concentrations. The transects in the river flood-plain (6 and 22) are clearly separated from most othertransects, but are not plotted close to each other. In-stead, transect 22 is grouped together with the tierrafirme transect 26, which may be an artifact caused bythe small clouds found in the vicinity of both transects(Fig. 2). The group formed by transects 4, 11, and 19seems to be characterized by a grayish tone in the sat-ellite image, but since this group is not reproduced inany of the other ordinations (see directly below) it isnot interpretable on the basis of the measured ecolog-ical characteristics.

In the ordination based on soil characteristics (Fig.6B), two main trends stand out. The first ordinationaxis is mainly defined by the concentration of ex-changeable bases in the soil (Pearson’s r 5 20.88, P, 0.001): the transects with high base concentrationshave low values for axis 1. The second axis is mainlydefined by soil texture (r 5 0.89 for clay, r 5 20.87for sand; P , 0.001 in both cases): transects with fine-grained soils are found in the upper part of the ordi-nation diagram (compare Fig. 6B with Fig. 3). In thecluster analysis, five groups were recognized; two ofthese correspond mainly to transects with low base con-centrations (,8 cmolc/kg) and three to transects withhigher base concentrations. One of the latter groupsincludes all the transects affected by inundations ofrivers (6 and 22) and creeks (13).

The ordinations and clusterings based on floristiccomposition of pteridophytes (Fig. 6C–D) and melas-tomes (Fig. 6E–F) are all similar in that (1) the riverfloodplain transects (6 and 22) are separated from theother transects, (2) the concentration of exchangeablebases in the soil is highly correlated with the first or-dination axis (Pearson’s r ranges between 20.69 and20.78, P , 0.001 in all cases), and (3) the transectswith low base contents in the soils are clustered sep-arately from the transects with higher base contents.Transect 19 has a soil base content ,8 cmolc/kg, butit is very close to this limit. Therefore it is not sur-prising that the floristic cluster analyses are not con-sistent in respect to whether transect 19 is grouped with


FIG. 6. Ordination diagrams (principal coordinates analysis) with superimposed cluster limits (unweighted arithmeticaverage clustering) of 27 transects in Amazonian Ecuador. Similarity matrices were based on: (A) pixel values in the LandsatThematic Mapper satellite image, (B) soil characteristics, (C)–(D) species composition of pteridophytes, and (E)–(F) speciescomposition of the Melastomataceae. (A, B) Similarities calculated with Euclidean distance. (C, E) Similarities calculatedwith the Sørensen index (species presence–absence data). (D, F) Similarities calculated with the Steinhaus index (speciesabundance data). Symbols indicate different mean concentrations of extractable bases in the soil (sum of Ca, K, Mg, andNa) as shown in the inset in (C). In (A), the plus sign (1) indicates the position of the permanent 50-ha plot (number 28).


TABLE 4. Correlations (measured with the Mantel test) offloristic similarities with similarities in the pixel values ina Landsat Thematic Mapper (TM) satellite image for 27transects in Amazonian Ecuador.

TMband

Correlation coefficients

Pteridophytes

Sørensen Steinhaus

Melastomataceae

Sørensen Steinhaus

123457

0.190.250.220.27*0.33*0.38*

0.100.130.120.21*0.18*0.21*

0.150.140.110.39**0.29*0.32*

0.23*0.160.160.32***0.22*0.23*

1, 2, 34, 5, 71–5, 7

0.250.39**0.43***

0.130.24*0.26**

0.160.41**0.38**

0.21*0.32**0.35***

Notes: The floristic similarity matrices were calculated sep-arately for pteridophytes and the Melastomataceae using twodifferent similarity indices (Sørensen index for presence–ab-sence data, Steinhaus index for abundance data). All simi-larity matrices for satellite image reflectances were based onEuclidean distance. Bands 1, 2, and 3 represent visible wave-lengths, and bands 4, 5, and 7 represent infrared wavelengths.The proportion of statistically significant correlations ex-pected at random at the P , 0.05 level is 1 out of 20 tests,clearly lower than found here.

* P , 0.05; ** P , 0.01; *** P , 0.001; significance levelsare based on 999 permutations.

the low or high base content transects. The floristicclassification of only two transects (8 and 20) is clearlydifferent from what would be expected on the basis ofthe base content of their soils.

The differences between floristic ordinations andclassifications that were obtained using the two simi-larity indices were minor. The main effect of includingabundance data in the analyses was a more clear-cutseparation between the transects with low as opposedto high base contents.

Correlations between matrices

Mantel tests showed that there was a strong positivecorrelation between the floristic similarity matricesbased on pteridophytes and the Melastomataceae. Thecorrelation coefficient was higher when the Sørensenindex was used to measure floristic similarity (r 5 0.82,P , 0.001) than when the Steinhaus index was used(r 5 0.73, P , 0.001). These values are very high formatrix correlations, which tend to give lower r valuesthan the corresponding linear correlations, although thetwo methods generally agree on whether the correlationis statistically significant (Legendre 2000; see alsoTuomisto and Poulsen [2000]). The matrix correlationbetween the species richness of pteridophytes and mel-astomes was r 5 0.28 (P 5 0.01), a value much lowerthan that obtained with species composition matrices.

Correlations between the floristic similarity matricesand the similarity matrices based on pixel values in thesatellite image varied from nonexistent to a highly sig-nificant 0.43 (Table 4). The visible light bands 1–3 hadno correlation with the floristic composition, with the

exception of band 1, which was correlated with thefloristic composition of Melastomataceae when theSteinhaus index was used. Combining the three visiblelight bands into one similarity matrix did not produceadditional correlations. On the contrary, all similaritymatrices that included one or more of the infrared bands4, 5, and 7 showed statistically significant correlationswith all floristic similarity matrices. In most cases,those similarity matrices that included a combinationof infrared bands showed the highest and/or statisti-cally most significant correlations with the floristicsimilarity matrices. The correlations obtained when theSørensen index was used to measure floristic similaritywere generally higher than the correlations obtainedwhen the Steinhaus index was used.

Correlations between similarity matrices based onspecies richness and the pixel values in the satelliteimage were generally close to zero, and all except onewere statistically nonsignificant. The sole exceptionwas the correlation between pteridophytes and band 4,which yielded r 5 0.19 at P 5 0.05. However, at thissignificance level 1 out of 20 tests is expected to givea significant correlation by chance; in this case, a totalof 18 tests were run so the single significant correlationmight well be spurious.

Correlations between the floristic similarity matricesand the similarity matrices based on soil characteristicsvaried from nonexistent to a highly significant 0.52(Table 5). The only soil characteristics that were notcorrelated with any of the floristic similarity matriceswere pH, sodium, and phosphorous. The soil charac-teristics that were significantly correlated with all flo-ristic similarity matrices were calcium, loss on ignition,clay, and sand. Also, the soil similarity matrices thatcombined either all exchangeable bases (Ca, K, Mg,and Na), all texture categories (clay, silt, sand), or allsoil characteristics were significantly correlated withall floristic similarity matrices. These composite soilmatrices showed usually slightly higher correlationswith the floristic matrices based on pteridophytes thanwith the matrices based on the Melastomataceae.

In the case of pteridophytes, the Steinhaus indexgave generally higher correlations with the soil char-acteristics than did the Sørensen index, except with clayand silt. In the case of the Melastomataceae, the ten-dency was the same but the differences between theresults obtained with the two similarity indices weresmaller.

Correlations between similarity matrices based onspecies richness and soil characteristics were generallyclose to zero, and almost all were statistically nonsig-nificant. The only soil characteristic that showed sta-tistically significant matrix correlation with the speciesrichness of both plant groups was clay content (pteri-dophytes, r 5 0.24, P 5 0.04; melastomes, r 5 0.33,P 5 0.003). In addition, soil texture (which takes intoaccount clay, silt, and sand content) yielded a statis-


TABLE 5. Correlations (measured with the Mantel test) of floristic similarities with similarities in soil and terrain charac-teristics for 27 transects in Amazonian Ecuador.

Variable

Correlation coefficients

Pteridophytes

Sørensen Steinhaus

Melastomataceae

Sørensen Steinhaus

pHCaKMgNaAlPLOI†ClaySiltSand

0.060.19*0.220.160.150.16

20.040.25*0.38*0.140.45***

20.010.49***0.28*0.43***0.060.180.170.24*0.35***0.31***0.32**

0.090.23*0.29*0.23*0.110.27*

20.020.26*0.38**0.120.32**

0.120.36***0.23*0.33***0.040.26*0.030.22*0.32**0.15*0.32**

Ca, K, Mg, NaClay, Silt, SandAll soil characteristicsElevation differenceGeographical distance

0.31**0.46**0.42***0.36**0.16

0.51***0.43***0.52***0.25**0.13

0.36**0.40***0.46***0.43***0.13

0.40***0.36***0.47***0.37***0.22*

Notes: The floristic similarity matrices were calculated separately for pteridophytes and the Melastomataceae using twodifferent similarity indices (Sørensen index for presence–absence data, Steinhaus index for abundance data). All similaritymatrices for soil and terrain characteristics were based on Euclidean distance. The proportion of statistically significantcorrelations expected at random at the P , 0.05 level is 1 out of 20 tests, clearly lower than found here.

* P , 0.05; ** P , 0.01; *** P , 0.001; significance levels are based on 999 permutations.† Loss on ignition.

TABLE 6. Multiple regression models constructed with an initial set of 13 independent distance matrices to explain thevariation in a dependent distance matrix based on the floristic similarities of 27 transects in Amazonian Ecuador.

Floristicdistancematrix Model R2

PteridophytesSørensenSteinhaus

0.42 3 sand 1 0.30 3 elevation difference0.51 3 Ca 1 0.33 3 clay 1 0.21 3 elevation difference

0.290.41

MelastomataceaeSørensenSteinhaus

0.35 3 clay 1 0.39 3 elevation difference0.37 3 Ca 1 0.30 3 clay 1 0.34 3 elevation difference 1 0.22 3 geographical distance

0.300.40

Notes: Twelve of the independent variables are listed in Table 1, the thirteenth is geographical distance between thetransects. All independent distance matrices are based on Euclidean distance. Separate models were constructed for floristicdistance matrices based on the Sørensen index (for presence–absence data) and the Steinhaus index (for abundance data).Backward elimination procedure was used to select a minimum set of significant independent variables for each model sothat all variables that remained in the models were significant at the P , 0.05 level after Bonferroni correlation. Significancelevels were based on 999 permutations. The significance of R2 in all models was P , 0.001.

tically significant matrix correlation with the speciesrichness of melastomes (r 5 0.28, P 5 0.01).

Similarity in topography, as measured by the ele-vational range within each transect, was significantlycorrelated with similarity in species composition ofboth plant groups (Table 5). With Melastomataceae, thecorrelations were higher than with pteridophytes, andthe Sørensen index gave higher correlations than theSteinhaus index in all cases. Geographical distance be-tween transects was only correlated with the floristicsimilarity in one case, and the correlation was relativelylow. Similarity in species richness among transects wasnot significantly correlated with either topographical

similarity or the geographical distance between tran-sects.

The multiple regression analyses indicated that ahigh proportion of the variation in floristic similaritybetween transects can be explained by 2–4 environ-mental variables (Table 6). Models constructed forpresence–absence data (Sørensen index) explained;30% and models constructed for abundance data(Steinhaus index) explained ;40% of the variation infloristic similarity. These results were practically iden-tical for both plant groups. All regression models re-tained the elevation difference and one texture class(either clay or sand), and the models constructed for


abundance data also retained Ca. In one case, geo-graphical distance also was included in the model, butnone of the other variables entered any of the models.

DISCUSSION

Vegetation differentiation

In spite of the uniform appearance of the forests inthe non-inundated part of our study area, the satelliteimage predicted the presence of two different terraintypes, and fieldwork confirmed both floristic andedaphic differences between these. The distinction be-tween the two terrain types was not absolutely clear-cut, but as seen in the ordination and classificationresults in Fig. 6, the similarity patterns among siteswere remarkably similar when calculated using the dif-ferent floristic, edaphic, and reflectance criteria.

The predictable component in the species distribu-tion patterns is further underlined by the highly sig-nificant Mantel test results (Tables 4 and 5) and by therelatively high proportion of explained variance in themultiple regression models (Table 6). The fact that theMantel tests involving geographical distance did notproduce significant correlations with most floristic sim-ilarity matrices also indicates that the patterns are notprimarily due to spatial autocorrelation, which wouldbe expected if random drift or biotic interactions werethe most decisive structuring forces in these forests.

It can also be concluded that the landscape-level pat-terns must be relatively strong in relation to local-scalepatterns, because they came out so clearly in the anal-yses in spite of the fact that the sampling at each sitewas done along a 500 m long transect instead of acompact plot. Such averaging over a large area is ex-pected to blur relationships between floristic compo-sition and environmental factors if local-scale patternsdominate (cf. Palmer and Dixon 1990).

Our results show also that floristic differences amongapparently similar sites, i.e., along rather short edaphicgradients, can to a large degree be explained by suchsoil characteristics as texture and cation content. Thisis a significant advance in understanding the rain forestecosystem, even if a correlation study like the presentone is not sufficient to establish whether the measuredsoil factors really caused the observed floristic patternsor are just themselves correlated with the actual causalagents.

The two plant groups included in the present study,pteridophytes and the Melastomataceae, are both phy-logenetically remote and have contrasting dispersalmodes (pteridophytes are wind-dispersed, while Me-lastomataceae are mainly bird-dispersed). The fact thatthey showed very high matrix correlations (r 5 0.73–0.82, P , 0.001) in their floristic variation indicatesthat their communities are subject to such general struc-turing factors that are independent of phylogeny anddispersal. This suggests that the same factors may alsoaffect the other plant groups in these forests (see also

Ruokolainen et al. [1997], Ruokolainen and Tuomisto[1998]). If the plant species composition of the forestaffects its suitability as animal habitat, which remainsto be tested, our results will have relevance to studieson animal ecology as well.

The highest floristic similarity values observed be-tween our transects exceeded 0.8 (Sørensen index; thecorresponding Jaccard index values exceed 0.7) forboth pteridophytes and melastomes. This is very high,especially when compared to the similarity values re-ported in tree surveys, which generally remain below0.3 (Jaccard index; Duivenvoorden 1995, Ruokolainenet al. 1997, Ruokolainen and Tuomisto 1998). Both ofour target plant groups include far fewer species thanthe local tree flora, so a larger proportion of the speciespresent at each site can actually be observed within thelimited area of the sampling units. As the noise causedby inadequate sampling diminishes, the chances im-prove that sites that in reality are floristically similaralso obtain high similarity values. The large spread insimilarity values among transect pairs, together withthe high mean number of individuals observed per spe-cies, also suggest that our results are robust.

The two indices we used to calculate floristic simi-larity (Sørensen and Steinhaus) gave relatively similarresults in most analyses. Therefore it appears that arelatively good comprehension of regional floristic pat-terns can be obtained with presence–absence data,which are much quicker to collect than abundance data.

In the multiple regression analysis, abundance datadid yield a clear improvement in the interpretability ofthe results: the percentage of explained variance in-creased from 30% with presence–absence data to 40%with abundance data. In some of the Mantel tests, es-pecially those involving Ca and Mg content, the cor-relations with floristic composition were also clearlyhigher when the Steinhaus index was used than whenthe Sørensen index was used. This may actually explainwhy Ca was retained in both multiple regression mod-els for abundance data, but in none of the models forpresence–absence data. However, in most of the Manteltests there was practically no difference between thetwo indices, and in others the situation was reversedso that the Sørensen index gave the higher correlations.We have found in earlier studies (Tuomisto et al. 1998,Tuomisto and Poulsen 2000) that the availability ofabundance data can enhance the interpretability of flo-ristic and species distribution patterns at more localscales, where the edaphic differences are often smallerand the mass effect presumably has a stronger impact(see also Shmida and Wilson 1985).

Soil heterogeneity

Compared with other Amazonian sites for which wehave analyzed soil samples, the total concentrations ofexchangeable bases in Yasunı are relatively high. All15 sites studied earlier in northern Peru and easternEcuador (Poulsen and Balslev 1991, Ruokolainen et al.


1997, Tuomisto et al. 1998) had cation contents lowerthan or close to the lower limit of the range found inthe present study. This agrees with the floristic com-position of the same sites: generalists apart (see Tuom-isto and Poulsen 1996), the pteridophyte species sharedbetween the earlier sites and Yasunı were in the formerrestricted to those sites with the highest soil cationcontents.

Direct comparison of chemical soil analysis resultsfrom different laboratories is complicated by differ-ences in the applied methodology. The concentrationsof Ca, Mg, and P that were reported by Lips and Duiv-envoorden (1996) for the middle Caqueta area of Co-lombia were clearly lower than the results we obtainedfor the Yasunı soils and were more or less in line withearlier results from the poor-soil Peruvian sites. Thisagrees with the observation that the floristic compo-sition of pteridophytes in the Caqueta sites resemblesthat of the poor-soil sites in Peru (Tuomisto and Poulsen1996).

However, contents of exchangeable bases at Anangu,15 km north of the Yasunı Research Station, were re-ported by Korning et al. (1994) to be even lower thanthose in Caqueta (Lips and Duivenvoorden 1996), butthe pteridophyte flora of Anangu is that of a rich-soilsite (Tuomisto and Poulsen 1996). Further studies areneeded to establish whether this discrepancy is due todifferences in soil analysis methods or to inconsistentrelationships of plant species with soil conditions. Ourguess is that the first alternative is closer to the truth.

Soil pH is often thought of as a reasonably goodmeasure of tropical soil fertility, and several studieshave focused on whether soil pH is correlated withplant species distributions (reviewed in Sollins [1998]).In the present study, soil pH was positively correlatedwith soil cation content and negatively correlated withAl content, indicating that less acidic soils are generallymore fertile. However, no correlation was found be-tween similarity in soil pH and floristic similarity ofeither pteridophytes or the Melastomataceae. Thisagrees with earlier findings from Peruvian Amazonia(Ruokolainen and Tuomisto 1998), so soil pH seemsuseless in explaining floristic patterns in these forests,unlike many of the chemical and texture measurements.Especially useful in this respect were the contents ofCa, clay, and sand, which not only had high correlationswith floristic similarity, but were also retained in themultiple regression models (Tables 5 and 6).

Lips and Duivenvoorden (1996) found that the non-podzolized tierra firme soils in the middle Caqueta areaof Colombian Amazonia can be divided into two maingroups and suggested that these may be the prevalentsoils in most of northwestern Amazonia and even someadjacent Andean footslope areas. In the present study,we also found that two soil groups can convenientlybe separated on the basis of exchangeable base content[below vs. above 8 cmolc/kg]. Although this divisionwas not marked by an abrupt break in the base content

gradient, it was reflected in all ordinations and clas-sifications of the floristic data (Fig. 6B–F). However,the Yasunı soils seem chemically too different from themiddle Caqueta soils to represent the same soil types,at least if soil classification is to reflect such soil prop-erties that are of relevance to natural vegetation.

Species richness

Results of the Mantel tests indicated that differencesamong transects in species richness are much less pre-dictable than differences in species composition. Spe-cies richness of pteridophytes did yield statistically sig-nificant matrix correlations with species richness of theMelastomataceae, but the correlation coefficient wasmuch lower than those obtained for species composi-tion. Neither pixel values of the satellite image normost of the soil characteristics yielded significant ma-trix correlations with species richness, which contrastsstrongly with the results obtained with species com-position.

In a continent-wide survey in the Neotropics (Cli-nebell et al. 1995), it was found that woody plant spe-cies richness in 0.1-ha samples increased asymptoti-cally with rainfall. Soil fertility contributed only littleto explaining species richness after the rainfall effecthad been taken into account, but when present, thecorrelation was negative. The overall conclusion of Cli-nebell et al. was that the species richness of matureNeotropical forests is almost independent of the sub-strate, except in extreme cases such as ultra-impov-erished podsols. They also mentioned examples whereseasonally inundated forests had lower species richnessthan nearby tierra firme sites.

In the present study, the area covered is so small thatclimate can be considered uniform, so differences inprecipitation cannot explain the considerable variationin species richness among the transects (see Table 3).With pteridophytes, our results supported those of Cli-nebell et al. (1995): there was no linear correlationbetween soil fertility (exchangeable base content) andspecies richness. The same was not true of the Melas-tomataceae, which showed a clear trend of decreasingspecies richness with increasing content of exchange-able bases in the soil.

When compared to other Amazonian areas inven-toried so far, the soils in all of our Yasunı transects canbe considered to be relatively rich in exchangeable bas-es, as discussed above. Therefore, the species richnesstrends observed here represent only a small part of thesoil fertility gradient represented in Amazonia, and thepatterns observed may not be generalizable. Indeed, inearlier studies we have noticed that in the poorer endof the soil gradient, both pteridophytes and the Me-lastomataceae show an increasing species richness withincreasing soil base content (Tuomisto and Ruokolai-nen 1994, Tuomisto and Poulsen 1996, Tuomisto 1998;K. Ruokolainen, unpublished data).


The observed species richness at a site certainly de-pends on several factors (e.g., Shmida and Wilson1985, Ricklefs 1987, Diamond 1988, Huston 1994,1999, Karlson and Cornell 1998, Loreau and Mouquet1999), and it is likely that different factors act as themain limiting or promoting factor at different sites.Such biogeographical aspects as proximity to the An-des or other diversity centers may be important in de-termining the size of the species pool that is regionallyavailable to colonize the site. Species richness is there-fore a complex phenomenon that may not be adequatelydescribed by simple correlations (cf. Thomson et al.1996).

Condit et al. (1996) reported that when the meannumber of species in subsamples of different sizeswithin 50-ha tree plots are plotted against the logarithmof sample size, an almost linear relationship is found.Our data also yielded linear species–log(area) curveswhen the transects were added in the sample in a ran-dom order, and the accumulation of species was av-eraged over several random runs (Fig. 5). However, ifour species–area curves were constructed so that tran-sects were included in the sample in the order of eitherincreasing or decreasing soil base concentration, thecurve became stepwise and remained below the meancurve. This indicates that species accumulation per unitincrease in area is slower when each new transect isrestricted to be edaphically similar to the previous tran-sect than when transects are added in a random orderand that some transects along the edaphic gradient con-tribute more new species than other transects. A similarobservation has been made when considering the ac-cumulation of pteridophyte species within continuoussample plots (Tuomisto and Poulsen 1996, 2000).

Condit et al. (1996) claimed that a linear form of anaveraged species–log(area) curve implies that plantspecies are distributed randomly in the forest. Our re-sults do not support this conclusion, because the spe-cies in our data set are clearly not randomly distributed,but the averaged species–log(area) curve was never-theless linear. Furthermore, He and Legendre (1996)concluded in a review of species–area relationships thatin the Pasoh forest (one of the sites analyzed by Conditet al.), the data did not conform to the random place-ment model as well as they conformed with the logisticspecies–area model, the latter being consistent withhabitat heterogeneity.

Use of satellite images in biodiversity assessment

Interest in the mapping of biodiversity and speciesdistributions at wide scales has been increasing in re-cent years. Some attempts have been made to identifyindividual plant species from remote sensing imagery,but success has been limited (Nagendra 2001), and intropical rain forests this approach is unlikely to be suc-cessful because of the high number of species present.In a study that used satellite imagery to predict localspecies richness, it was found that structural charac-

teristics of the forest were modeled better than speciesrichness (Jakubauskas and Price 1997). More promis-ing results have been obtained by predicting speciesrichness from landscape heterogeneity, which in turnis obtained from interpretation of satellite imagery(Gould 2000). A similar conclusion was reached in ourstudy: differences in satellite image reflectance valueswere not good predictors of differences in local speciesrichness, but spectral variation over the satellite imagedid correctly predict floristic differences and hence anincrease in species richness at the landscape scale.

The most common, and most successful, use of sat-ellite images to date has been in vegetation mapping,usually with the aim of identifying structurally differ-ent vegetation types (e.g., Townshend et al. 1987,Garcia and Alvarez 1994, Hill and Foody 1994, Par-adella et al. 1994, Foody and Hill 1996, Langford andBell 1997, Nagendra and Gadgil 1999, Saatchi et al.2000, Trisurat et al. 2000, Harvey and Hill 2001, Na-gendra 2001). However, predicting and mapping flo-ristic differences within structurally uniform tropicalforests, such as Amazonian tierra firme, has rarely beenattempted (Tuomisto et al. 1994, 1995, Kalliola et al.1998). Our results show that Landsat TM images canbe used for this purpose and that the infrared bands areespecially useful in detecting vegetation differenceswithin the forest (see also Harvey and Hill [2001]).This is an encouraging result, because it shows thateven nonobvious habitat diversity is recognizablethrough remote sensing, which improves the prospectsof biodiversity mapping in Amazonia (and other trop-ical areas) significantly.

However, our results also indicate that the rain forestis spatially more heterogeneous than has been believedand that the floristic heterogeneity is linked with soilheterogeneity. Therefore, it will be important that theplanning of land use, forest resource management, andbiodiversity conservation take the possibility of spatialheterogeneity into account and do not automaticallyassume homogeneity over areas where the forests lookstructurally uniform.

CONCLUSIONS

1) Similarity in floristic composition of pterido-phytes and the Melastomataceae correlated with sim-ilarity in pixel values in the satellite image, but simi-larity in species richness did not. Hence, satellite im-ages can be used to predict similarity in species com-position but not in species richness within the rainforest.

2) Floristic similarity correlated with similarity inmost of the measured soil characteristics, most notablyCa, K, Mg, clay, sand, and LOI. Multiple regressionmodels explained 30–40% of the variation in floristicsimilarity with 2–4 environmental variables. Similarityin species richness showed no correlation with simi-larity in most of the soil characteristics.


3) The landscape-scale floristic patterns were highlycorrelated (Mantel’s r 5 0.73–0.82) when measuredusing pteridophytes and Melastomataceae. The speciesrichness patterns were less strongly correlated (Man-tel’s r 5 0.28).

ACKNOWLEDGMENTS

We thank Pontificia Universidad Catolica del Ecuador, itsYasunı Research Station and Herbarium QCA for logisticsupport, and Centro de Levantamientos Integrados de Re-cursos Naturales por Sensores Remotos (CLIRSEN) for pro-viding the satellite image. Renato Valencia is acknowledgedfor help in making the practical arrangements, Oscar Mendozaand Ermindo Zambrano for assistance in the field work, AlanR. Smith and John T. Mickel for help with species identifi-cation, Flemming Nørgaard for drawing the map in Fig. 1,Sirpa Rajaniemi and Kai Makisara for analytical discussionson satellite images, Pierre Legendre and Philippe Casgrainfor advice on data analysis, and David B. Clark for construc-tive comments on the manuscript. The soil samples were an-alyzed by the Agricultural Research Centre of Finland. Thestudy was funded by the Danish Center for Tropical Biodi-versity, the Academy of Finland, and the Turku UniversityFoundation.

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LINKING FLORISTIC PATTERNS WITH SOIL HETEROGENEITY AND SATELLITE IMAGERY IN ECUADORIAN AMAZONIA

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