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Floristic patterns along a 43‐km long transect in an Amazonian rain forest

2003; Wiley; Volume: 91; Issue: 5 Linguagem: Inglês

10.1046/j.1365-2745.2003.00802.x

1365-2745

Hanna Tuomisto, Kalle Ruokolainen, M. J. Del-Arco Aguilar, Abel Sarmiento,

Plant and animal studies

Journal of EcologyVolume 91, Issue 5 p. 743-756 Free Access Floristic patterns along a 43-km long transect in an Amazonian rain forest Hanna Tuomisto, Corresponding Author Hanna Tuomisto Department of Biology, University of Turku, FIN-20014 Turku, Finland, and *Correspondence: Hanna Tuomisto (e-mail: hanna.tuomisto@ utu.fi).Search for more papers by this authorKalle Ruokolainen, Kalle Ruokolainen Department of Biology, University of Turku, FIN-20014 Turku, Finland, andSearch for more papers by this authorMelchor Aguilar, Melchor Aguilar Universidad Nacional de la Amazonía Peruana, Iquitos, Peru Present address: Consejo Transitorio de Administracion Regional – Loreto, Av. A. Quiñones Km. 2, Iquitos, Peru Search for more papers by this authorAbel Sarmiento, Abel Sarmiento Universidad Nacional de la Amazonía Peruana, Iquitos, Peru Present address: Instituto Nacional de Recursos Naturales, Carr. Federico Basadre Km. 4.200, Pucallpa, PeruSearch for more papers by this author Hanna Tuomisto, Corresponding Author Hanna Tuomisto Department of Biology, University of Turku, FIN-20014 Turku, Finland, and *Correspondence: Hanna Tuomisto (e-mail: hanna.tuomisto@ utu.fi).Search for more papers by this authorKalle Ruokolainen, Kalle Ruokolainen Department of Biology, University of Turku, FIN-20014 Turku, Finland, andSearch for more papers by this authorMelchor Aguilar, Melchor Aguilar Universidad Nacional de la Amazonía Peruana, Iquitos, Peru Present address: Consejo Transitorio de Administracion Regional – Loreto, Av. A. Quiñones Km. 2, Iquitos, Peru Search for more papers by this authorAbel Sarmiento, Abel Sarmiento Universidad Nacional de la Amazonía Peruana, Iquitos, Peru Present address: Instituto Nacional de Recursos Naturales, Carr. Federico Basadre Km. 4.200, Pucallpa, PeruSearch for more papers by this author First published: 19 September 2003 https://doi.org/10.1046/j.1365-2745.2003.00802.xCitations: 174 AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary 1 The floristic variation in Amazonian lowland forests is poorly understood, especially in the large areas of non-inundated (tierra firme) rain forest. Species composition may be either unpredictable as abundances fluctuate in a random walk, more-or-less uniform, or it may correspond to environmental heterogeneity. 2 We tested the three hypotheses by studying the floristic variation of two phylogenetically distant plant groups along a continuous 43-km long line transect that crossed tierra firme rain forest in northern Peru. 3 The observed floristic patterns were compared to patterns in the spectral reflectance characteristics of the forest as recorded in a Landsat TM satellite image. The topography of the transect was measured in the field, and surface soil samples were collected to document edaphic conditions. The two plant groups, pteridophytes and the Melastomataceae, were assessed in 2-m wide and 500-m long sampling units. 4 Floristic similarity (Jaccard index) between sampling units ranged from 0.01 to 0.71 (mean = 0.27), showing that some units were almost completely dissimilar while others were very alike. 5 Spatially constrained clustering produced very similar subdivisions of the transect when based separately on satellite image data, pteriophytes, and Melastomataceae, and the subdivisions were also related to topography and soil characteristics. Mantel tests showed that floristic similarity patterns of the two plant groups were highly correlated with each other and with similarities in reflectance patterns of the satellite image, and somewhat less correlated with geographical distance. 6 Our results lend no support to the uniformity hypothesis, but they partially support the random walk model, and are consistent with the hypothesis that species segregate edaphically at the landscape scale within the uniform-looking forest. Introduction hypotheses on floristic patterns In the past few decades, researchers have become increasingly interested in documenting and understanding the spatial structure and species composition of Amazonian lowland rain forests. There are a range of different views concerning the main factors that control plant species distributions in these forests, and the kind of general distribution patterns that follow. The ecological and floristic differences between such contrasting habitats as inundated vs. non-inundated forests, or forests on podzolized white sand soils vs. non-podzolized soils, are universally recognized. However, researchers differ fundamentally in their views on how species are distributed within these broad forest categories, especially within the non-inundated forests on non-podzolized soils, i.e. the 'typical' tierra firme rain forest. Much of the current discussion is centred around the relative importance of three alternative views: (i) plant species are competitively equal, and the local species composition is in a state of 'random walk' as a result of local immigration and extinction; (ii) the forest is essentially homogeneous, and a small proportion of the species are competitively superior and dominate the forest over wide areas; and (iii) differences in soils within the forest are distinct enough to favour different species at different locations, and thus create numerous floristically differentiated forest patches. If the plant species in a community come and go at random, as in the first view (Hubbell & Foster 1986; Condit 1996; Hubbell 1997, 2001; Chave et al. 2002; Condit et al. 2002), the variation in abundance of a given species is expected to show strong spatial autocorrelation due to dispersal limitation, but not to be systematically correlated with the abundances (or even presence) of other species. As a result, a species that is abundant at one site is likely to be both present and abundant at nearby sites but not at faraway sites, and the overall floristic similarity among sites will decrease monotonically with increasing inter-site distance. This decrease in floristic similarity is expected to be approximately linearly related to the logarithm of geographical distance (Hubbell 2001; Condit et al. 2002). The species are more or less equivalent ecologically, so any one of them can become abundant, rare, or locally extinct by chance, and floristic patterns are therefore not expected to correlate with patterns in local site conditions. Furthermore, one would not expect to find sharp floristic boundaries where several species appear and/or disappear simultaneously. Instead, species turnover would be gradual in space. The second view maintains that species composition and abundance patterns are relatively constant over wide areas, although only a few species may be shared between sampling plots if plot size is small (Duivenvoorden 1995; Pitman et al. 1999, 2001; Terborgh et al. 2002), and that, following disturbance, species composition and abundance revert towards their prior state (Terborgh et al. 1996). Most species are expected to be widespread, and the species that become abundant at a given site are not a random subset of all the species present, but are likely to belong to a limited group of species that possess biological characteristics that enable them to compete successfully and dominate over large tracts of forest (Pitman et al. 1999, 2001). These dominant species are expected to be omnipresent in the forest, at least at the landscape scale, so their abundance patterns are not expected to show either spatial autocorrelation or correlation with patterns in local site conditions. Because the forest is considered homogeneous, no abrupt turnover zones in species composition are expected. The third view maintains that the environmental variation in western Amazonia is pronounced enough to create floristically differentiated communities within the tierra firme forest. Spatial variation in species composition is expected, both in response to landscape-scale soil differences and in relation to local factors such as topography and associated soil catenas (Poulsen & Balslev 1991; Tuomisto et al. 1995; Ruokolainen et al. 1997; Svenning 1999; see also Lieberman et al. 1985 and Clark et al. 1995, 1998, 1999 for Central America, and Sabatier et al. 1997 for Guiana). Species abundances and floristic composition are expected to reflect spatial patterns in the environmental conditions, so that if there is spatial turnover in these patterns, corresponding and predictable turnover is also expected in the vegetation (Tuomisto & Poulsen 1996, 2000). In a patchy environment, spatial autocorrelation is expected to be a poor predictor of similarity in species composition because environmentally, and hence floristically, dissimilar sites can occur in close proximity (Poulsen & Tuomisto 1996; Ruokolainen et al. 1997). Because each species is expected to be most abundant where the environmental conditions are most favourable for it, the same dominant species are expected at sites with similar environmental conditions, while different dominants are expected at sites with differing environmental conditions (Tuomisto et al. 1998). Similar discussion about the detailed variation in species composition within broadly defined vegetation types has been conducted elsewhere in the tropics, especially in south-east Asia, where field results have also been variously interpreted (Baillie et al. 1987). Poore (1968) concluded that, while rare species may be habitat specialists, the distribution of common species is determined by biotic interactions and chance. Others have found evidence for mainly edaphically determined distribution patterns (Austin et al. 1972; Ashton 1976; Baillie et al. 1987), or evidence of such patterns in some but not all of the forest types studied (Newbery & Proctor 1984). landscape-scale sampling To test the three hypotheses described above, it is necessary that field inventories are both extensive enough and detailed enough to reveal landscape-scale floristic and edaphic patterns. Extensive tree sampling has been carried out in western Amazonia (Gentry 1988; Duivenvoorden 1995; Ruokolainen et al. 1997; Ruokolainen & Tuomisto 1998; Pitman et al. 1999, 2001; Duque et al. 2002), but both sampling and species identification of trees are very laborious and time-consuming, and the number of species involved is very high, so it is difficult to obtain tree samples that are both spatially and floristically representative enough to give a detailed and reliable picture of landscape-scale species distribution patterns. To be able to cover larger spatial extents in more detail, we have concentrated our sampling effort on two plant groups that are more easily observable and less species-rich than canopy trees: pteridophytes (ferns and fern allies) and the Melastomataceae (which are mainly shrubs and small trees). In earlier studies, both groups have been found to show roughly the same floristic patterns as trees (Ruokolainen et al. 1997; Ruokolainen & Tuomisto 1998; Vormisto et al. 2000), and hence we call them here indicator groups. Because our field sampling did not cover trees, we wanted to preclude the possibility that the indicator groups chosen conform unduly with each other because of phylogenetic relatedness or similarities in life histories. We therefore studied groups that are both phylogenetically remote (pteridophytes vs. angiosperms) and have contrasting life histories and dispersal modes (pteridophytes have wind-dispersed spores and a sessile self-supporting gametophyte generation, while Melastomataceae are predominantly bee-pollinated and bird-dispersed). Any congruence in species composition patterns between such dissimilar groups is likely to reflect external factors that would also affect other plant groups. Because Amazonian rain forests are spatially extensive and difficult to access, remotely sensed information has been used to help in recognizing plant communities and spatial patterns within them. Early successional forest, inundated forest, different kinds of swamp and forest on podzolized white sand soil, which are all clearly structurally different, can be readily recognized in satellite images (Kalliola et al. 1991, 1998; Tuomisto et al. 1994; Foody & Hill 1996; Novo & Shimabukuro 1997; Tuomisto 1998; Hill 1999; Saatchi et al. 2000). Even non-inundated tierra firme forests on non-podzolized soil, which mostly look homogeneous in aerial photographs, show considerable spectral patchiness in Landsat TM (Thematic Mapper) satellite images with variation at scales from hundreds of metres to kilometres (Tuomisto et al. 1995). It has been debated whether or not these satellite image patterns indicate differences in the vegetation that are related to soil differences (Condit 1996; Duivenvoorden & Lips 1998), but recent results have indicated that this indeed is the case (Ruokolainen & Tuomisto 1998; Tuomisto & Ruokolainen 2001; Tuomisto et al. 2003a). We used a Landsat TM satellite image as a source of spatially continuous information for landscape-scale variation in the rain forest. The satellite measures reflectance of the ground cover, which in this case is mainly determined by the canopy trees, lianas and epiphytes. On the basis of earlier studies (Tuomisto et al. 1995; Ruokolainen & Tuomisto 1998; Vormisto et al. 2000; Tuomisto & Ruokolainen 2001; Tuomisto et al. 2003a) we propose that canopy patterns follow underlying edaphic conditions, and hence we use the reflectance values from the satellite image as a proxy for environmental variation. Soil samples were analysed to verify this relationship, but their spatial resolution was not sufficient to include them in the formal analyses. Note that our indicator groups are understorey plants, and therefore have hardly any direct influence on the reflectance characteristics of the forest. Consequently, a correlation between understorey species distribution patterns and canopy reflectance patterns can only be found if the factors that determine the reflectance characteristics of the canopy are strongly correlated with those factors that determine the floristic composition of the understorey. This puts the hypothesis on environmental control of floristic patterns under a stringent test, while favouring the acceptance of the random walk hypothesis and the uniformity hypothesis. objectives We have recently tested the three hypotheses using widely spaced field sampling that ranged from southern Peru to Ecuador and Colombia (Tuomisto et al. 2003b). At such a wide spatial scale, the forests were clearly not homogeneous, and the data indicated that both random walk with dispersal limitation, and environmental factors are needed to explain floristic patterns. Condit et al. (2002) found that the dispersal limitation model was sufficient to explain their field data at distances between 0.2 km and 50 km. In the present study, our aim is to concentrate on this landscape scale, and to test the three hypotheses using data from a continuous 43-km long transect. A single transect was used instead of discrete plots because continuous sampling allows observations of spatial change to be made and compared between data sets, and assessment of whether turnover is spatially continuous or occurs more rapidly at certain points. A transect can also be georeferenced more readily than separate plots, because it crosses tree-fall gaps where GPS (Global Positioning System) coordinates can readily be obtained. Our field survey extended over 43 km of forest, and the analyses of both field data and satellite image data were based on 500-m long sampling units. This geographical scale is such that it is able to detect patches of the size recognized by Tuomisto et al. (1995), who assessed spectral patchiness along 30-km long transects that were drawn on satellite images but not field-verified. The relatively coarse resolution further makes it unlikely that any correlation between patterns in satellite imagery and plant species composition is caused by ordinary forest succession in tree fall gaps, because these are typically much less than 500 m across. We also asked how many of the observed plant species are actually distributed in a way that correlates with the reflectance patterns in satellite imagery. This question was answered by first classifying the sampling units of the transect on the basis of information from the satellite image, and then testing, for each species, whether or not its distribution was biased towards any of the recognized classes. Materials and methods data collection Fieldwork was carried out in Amazonian Peru in the forest reserve of the Amazon Center for Environmental Education and Research (ACEER) close to the confluence of the Sucusari and Napo rivers (Fig. 1). The climate in the area is tropical, humid and almost aseasonal. Mean monthly temperature in the nearby city of Iquitos is 25–27 °C throughout the year, and annual precipitation is about 3100 mm. No month receives less than 180 mm of rain on average, but about half of the years for which records exist experienced one or two months with less than 100 mm of rain (Marengo 1998). Figure 1Open in figure viewerPowerPoint Location of the study area in Peruvian Amazonia, and of the 43-km long transect near the confluence of the Napo and Sucusari rivers. The transect first runs 30 km to the east, and then 13 km to the north. The area is about 100–200 m above sea level, and the landscape ranges from flat to hilly. The surface soil is formed of unconsolidated sediments of various origins and ages, including the mid-Miocene Pebas formation and more recent fluvial deposits. The geology of the area has not been studied in detail, but accounts of the geological history of the general region do exist (Hoorn 1993; Räsänen et al. 1998). The vegetation in the study area consists mainly of closed-canopy non-inundated forests, although seasonally or sporadically inundated zones are found along all rivers and major creeks. No special edaphically defined vegetation types (such as forests on white sand soils) are known from the non-inundated area. Soil and floristic studies were conducted in a continuous 43.38-km long transect. The transect followed the border of the ACEER reserve, which had been marked in the field a few months earlier by a crew of men from nearby villages, some of whom also joined us on this field expedition. The transect was georeferenced using GPS technology. A visible mark was fixed every 50 m along the length of the entire transect. Sections of 100 m were used as sampling subunits for the floristic inventory. Pteridophytes and members of the family Melastomataceae (excluding Memecylaceae) were censused within an estimated 2.0 m to the left side of the transect. Presence-absence data were collected for each species. Collecting abundance data would have been too slow to be practicable; as the length of our field expedition was mainly limited by the quantity of provisions we were able to carry, we had to compromise between local detail and spatial extent. For the purposes of the present paper, five consecutive subunits were fused to obtain 87 sampling units with an effective size of 0.1 ha (500 × 2 m). The last unit ended at the shore of the Apayacu River, and was shorter than the other units. The vegetation in the first sampling unit (the one closest to the Sucusari River) showed, in parts, characteristics typical of secondary forests. Because our purpose was not to study differences between secondary and old-growth forests, but rather variation within old-growth forests, this unit was excluded from the analyses presented. The final sample size was therefore 86 sampling units that covered almost 8.6 ha. To facilitate pteridophyte observation and sampling, only individuals with at least one leaf longer than 10 cm were considered, and epiphytic and climbing individuals whose lowermost green leaves were higher than 2 m above ground were ignored. Voucher collections for all species of both pteridophytes and the Melastomataceae are deposited in herbaria in Peru (AMAZ), Finland (TUR) and USA (KSP; herbarium acronyms follow Holmgren et al. 1990). The topographic profile of the transect was measured using a clinometer (Suunto, Vantaa, Finland). Measurements were taken every 50 m, and in between if the slope of the terrain changed significantly. Surface soil samples (the top 5 cm of the mineral soil) were collected at roughly 2.5-km intervals, such that two samples were taken from each location, one from the top of a hill and one from the bottom of the nearest valley. Each of the soil samples consisted of five pooled subsamples collected within an area of c. 5 × 5 m. Physical and chemical analyses were carried out using standard procedures (van Reeuwijk 1993). We report soil texture (percentage of coarse sand with particle size 0.25–2 mm) and the concentration of exchangeable bases (calcium, potassium, magnesium and sodium measured in 1 m NH4OAc at pH 7). A Landsat TM image (path 6, row 62, 1 November 1987) covering the study area was obtained from the Landsat Pathfinder HTF project of the University of Maryland and NASA, USA. After the field work, the satellite image was rectified using ground control points that had either been obtained using a GPS in the field, or could be identified on a base map derived from Landsat MSS satellite images (IFG 1984). The transect was drawn on the rectified satellite image with the help of GPS coordinates and landmarks. For each of the 500-m long sampling units, an area extending 200 m to either side was delimited on the satellite image. Such a large area was used for two reasons. First, it reduced the effect of hilliness on the results, as each unit was large enough to average out the differences in reflectance values between the sunlit and shaded sides of hills. Second, the error in GPS coordinates at the time of sampling may have exceeded 100 m, so a 200-m buffer in pixel sampling was deemed necessary to ensure that the transect was actually contained within the sampled area. The original values for the pixels included within each of the delimited areas were extracted for analysis. Most of the areas had 211–218 pixels, but four were made smaller (135–180 pixels) to avoid including pixels with clouds or cloud shadows. Only data from bands 1–5 and 7 were used, the thermal infrared band 6 being excluded. ER-Mapper 5.5 software (Earth Resource Mapping, Egham, UK) was used for all satellite image analyses. data analyses Our numerical analyses aimed to reveal areas of major changes in pteridophyte and Melastomataceae species composition, and to clarify the extent to which differences in environmental factors (as inferred from pixel values in the satellite image) and geographical distance can be used to predict differences in floristic composition of the forest. Almost all analyses are based on resemblance matrices, each of which consists of pairwise comparisons among all 86 sampling units using one or more descriptor variables. When a resemblance measure that had originally been calculated as distance (D) needed to be converted to similarity (S) or vice versa, the formula S = 1 − D was used. Floristic similarity matrices were calculated using the Jaccard index [S = a/(a + b + c), where a is the number of species shared between the two sampling units, b is the number of species only found in the first unit, and c is the number of species only found in the second unit]. Three similarity matrices were calculated: one using pteridophytes, one using Melastomataceae and one using both plant groups combined. Differences in pixel values between sampling units were expressed in Euclidean distance using mean pixel values calculated separately for each band for each unit. A total of 11 Euclidean distance matrices were calculated using one or more of these satellite-derived variables. Seven matrices were based on a single variable: either the mean pixel values of one of the six bands, or the green vegetation index [NDVI = (band 4 − band 3)/(band 4 + band 3)]. For the remaining matrices, two different combinations of satellite-derived variables were used [all six bands (1–5 and 7), or band 2, band 7 and NDVI]. The purpose of including just three variables in the latter case was to build a model with reduced collinearity. The visible wavelength bands 1–3 are highly intercorrelated, as are the infrared bands 4, 5 and 7, so only one band from each group was used in the reduced model. Bands 2 and 7 were chosen because they showed highest correlations with the floristic data in the Mantel test (see below). NDVI was chosen because it provides information from bands 3 and 4 but is less correlated with bands 2 and 7 than either band alone. Both combinations of satellite-derived variables were first used to produce a distance matrix in which each of the included variables was given equal weight by standardizing to zero mean and unit variance. However, there is no reason to believe that equal weights would give an optimal relation to floristic variation, so for both variable combinations, a second distance matrix was constructed where each of the standardized satellite-derived variables were weighted individually. The weights were obtained from an equation of multiple regression on distance matrices that was obtained for each variable combination. The independent distance matrices were in both cases based on the satellite-derived variables, and the dependent distance matrix was the floristic distance matrix that included both pteridophytes and Melastomataceae. For both models we report the standard partial regression coefficients (B) and the coefficients of multiple determination (R2). The partial regression coefficients for each satellite-derived variable were used as weights in calculating the corresponding Euclidean distance matrix. Backward elimination was applied in the multiple regression analysis that initially included only band 2, band 7 and NDVI to make sure that each of the variables in the final model would have a statistically significant (P < 0.05 after Bonferroni correction) contribution to the amount of variance explained (see Legendre et al. 1994; Legendre & Legendre 1998). Mantel tests of matrix correspondence were run to analyse the degree of predictability in the floristic patterns of the sampling units. First, a Mantel test was run to quantify the correlation between the floristic distances as measured separately with the two plant groups (pteridophytes and the Melastomataceae). Then, Mantel tests were run to find out to what degree the floristic distances correlated with distances in pixel values in the satellite image and with geographical distance. All possible pairwise tests involving one of the three floristic distance matrices and one of the nine unweighted satellite-derived distance matrices were run. The weighted satellite-based distance matrices were only used in one Mantel test, i.e. the one using the combined pteridophyte and Melastomataceae distance matrix that had been used in the multiple regression analysis that provided the weights. All three floristic distance matrices were also used in two Mantel tests involving geographical distance: one test used original geographical distances, and the other used ln-transformed geographical distances. Partial Mantel tests were run to find out how much of the correlation between two distance matrices (such as floristic and satellite-derived) remained after taking into account the correlation with a third distance matrix (such as geographical). For each Mantel test and partial Mantel test, we report the standardized form of the Mantel statistic, which corresponds to a Pearson correlation coefficient calculated between the two distance matrices in question. The statistical significance of each correlation was determined by a Monte Carlo permutation test using 999 permutations, which allows testing of the statistical significance at the P < 0.001 level for each individual correlation. When interpreting the results, it is important to keep in mind that Mantel's matrix correlation coefficient rM is not comparable with the linear Pearson's correlation coefficient rP, which is based on the original variables rather than distances. When both are calculated on the same univariate data, rM obtains clearly lower values than rP, although they generally show the same degree of statistical significance (Legendre 2000). Cluster analyses were carried out to classify the sampling units on the basis of their floristic similarity (on the basis of pteridophytes, Melastomataceae, and the two combined) and on the basis of their similarity in satellite image pixel values (using the matrix that gave the highest Mantel correlation with the floristic matrix). Two different agglomerative clustering methods were applied, both of which use a proportional-link linkage algorithm. The connectedness level was always set to 0.5 (i.e. mid-way between single and complete linkage). One of the clustering methods, chronological clustering, applies a unidimensional constraint and only allows the fusion of sampling units or groups of units if they are contiguous along the transect (Legendre et al. 1985; Legendre & Legendre 1998). This method gives non-hierarchical results, because the clustering procedure performs at each step a permutational test of significance and decides accordi