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Classifying spatial patterns of floristic variation into relatively homogenous units is especially attractive to facilitate decisions in management, zoning and conservation planning. Forest classification schemes, however, are poorly developed in the tropics (Salovaara et al. 2004). Vegetation classifications of Amazonian forest are mainly based on broad-scale physiognomic classes (Veloso et al. 1991), while more fine-scale schemes that incorporate changes in species composition are rare. Local schemes have been proposed (Kahn & Castro 1985; Ribeiro et al. 1999), but field data are lacking for regional extrapolation. Amazonia's vastness, difficult access and mega-diverse flora constrain the rapid collection of reliable field data. In the absence of such a complete inventory, subsets of taxa (Higgins & Ruokolainen 2004) and abiotic environmental layers may be reliable surrogates for general biodiversity patterns (Margules et al. 2002). Validation and improvement of existing classification schemes are therefore urgent tasks for Amazonia.
Non-inundated forest is the predominant vegetation class in the Amazon Basin but this class is not floristically homogeneous. In central Amazonia edaphic and topographic features play a central role in shaping species composition. North of the Amazon River, soils are derived mostly from Cretaceous sediments and are nutrient-poor (Irion 1978; Chauvel et al. 1987; Quesada et al. 2010). The soil catena, i.e. the sequence of changes in soil properties along a local topographic profile, is the main source of edaphic heterogeneity over much of the landscape. A single catena can contain large variation in texture and hydrological properties, but not fertility, going from well-drained clayey soil on the plateau to more water-saturated sandy podzol on the lower slope near water courses (Chauvel et al. 1987; Rennó et al. 2008). Environmental conditions along the catena are mirrored by floristic changes (Kahn & Castro 1985; Costa et al. 2005, 2009; Bohlman et al. 2008). Vegetation along the catena is locally classified into forest classes, such as well-drained upland and seasonal swamp forests (Kahn & Castro 1985) or plateau, slope and valley forests (Ribeiro et al. 1999). Similar habitats at different sites, however, can be floristically distinct. This can be due to historical processes, such as disturbance and dispersal limitation (Zobel 1997; Phillips et al. 2003; Ozinga et al. 2005), or due to underlying site-specific environmental differences (Vormisto et al. 2004b). To date, the floristic consistency of these local topography-based vegetation types in central Amazonia has not been examined across more extensive geographic regions.
About 100 km northeast of Manaus, Zuquim et al. (2012) identified slightly more cation-rich soils than those overlying Cretaceous deposits. The composition of fern species and legume trees (Pansonato et al. 2013) was also distinct from sites on the Cretaceous deposits. This slightly more fertile site straddles the geological boundary between sedimentary Palaeozoic (ca. 425–390 MY) and igneous Precambrian (ca. 1990–1800 MY) formations. Physical and chemical conditions of soils in Amazonia are often strongly associated with geological history and landform evolution (Irion 1978; Rossetti et al. 2010; Higgins et al. 2011). For this reason, the same topographic habitat (i.e. catena compartment) compared between different geological landscapes may be floristically distinct. If this is true, the commonly used catena-based vegetation types will be poor proxies for floristic variation, unless modified to take into account geological and landform features.
Geological and landform features can be easily derived from well-mapped layers, such as geological maps and radar data. This offers an opportunity to predict and map floristic patterns across the extensive inaccessible areas of Amazonia. Geological maps and Shuttle Radar Topography Mission (SRTM) digital elevation data have already been used in western (Salovaara et al. 2004; Higgins et al. 2011, 2012) and eastern (Rossetti et al. 2010) Amazonia to understand and map forest types. Western Amazonia is a more dynamic landscape with more recent evolution than the central sedimentary basin and the Guiana Shield (Hoorn et al. 2010), and it encompasses a broader range of soil fertilities (Tuomisto et al. 2003a; Higgins et al. 2011, 2012). In eastern Amazonia Rossetti et al. (2010) examined the relationship between geology, landform and vegetation types but these types were physiognomically very distinct (e.g. savanna, forest). Additional field data from other geological contexts must be collected to validate the use of landform-based surrogates for classifying floristic patterns across the entire basin or within landscapes completely covered by forest.
Here, we used Zingiberales species, an order of understorey monocots herbs, as a subset of taxa to derive a floristic classification of inventories across ancient geological formations in central Amazonia. Landeiro et al. (2012) showed that high congruence of spatial patterns in central Amazonia between many terrestrial plant groups (including monocot herbs) is related to their common response to edaphic features. This suggests that there is a general environmental driver of floristic patterns and that a subset of taxa can potentially reveal them. Based on field data of 123 floristic inventories concentrated at three sites, we ask: (1) are topography-based vegetation types floristically consistent in central Amazonia, and (2) do broad landform and geological features control site-specific edaphic and floristic variation and therefore obfuscate the floristic classification based purely on local topographical classes?
We bring to bear a powerful but rarely used analytical approach in ecological studies, model-based clustering coupled with geodesic floristic distances. The fuzzy, noisy and non-linear nature of species composition data have led to unsatisfactory outcomes for most clustering algorithms. Data reduced using geodesic distances may circumvent these problems (Schmidtlein et al. 2010). Selecting the best classification solution is a major challenge in numerical classification. Unlike conventional clustering methods, model-based clustering selects the most parsimonious solution, allows overlapping clusters and provides a measure of class membership uncertainty (Fraley & Raftery 1998). To justify the use of these tools, we used geometric and non-geometric internal evaluators to compare the performance of our classification with that of conventional clustering methods and non-geodesic dimensionality reduction.