2016-12-23T18:37:48

No abstract

Aim  It is a central issue in ecology and biogeography to understand what governs community assembly and the maintenance of biodiversity in tropical rain forest ecosystems. A key question is the relative importance of environmental species sorting (niche assembly) and dispersal limitation (dispersal assembly), which we investigate using a large dataset from diverse palm communities. Location  Lowland rain forest, western Amazon River Basin, Peru. Methods  We inventoried palm communities, registering all palm individuals and recording environmental conditions in 149 transects of 5 m × 500 m. We used ordination, Mantel tests and indicator species analysis (ISA) to assess compositional patterns, species responses to geographical location and environmental factors. Mantel tests were used to assess the relative importance of geographical distance (as a proxy for dispersal limitation) and environmental differences as possible drivers of dissimilarity in palm species composition. We repeated the Mantel tests for subsets of species that differ in traits of likely importance for habitat specialization and dispersal (height and range size). Results  We found a strong relationship between compositional dissimilarity and environmental distance and a weaker but also significant relationship between compositional dissimilarity and geographical distance. Consistent with expectations, relationships with environmental and geographical distance were stronger for understorey species than for canopy species. Geographical distance had a higher correlation with compositional dissimilarity for small‐ranged species compared with large‐ranged species, whereas the opposite was true for environmental distance. The main environmental correlates were inundation and soil nutrient levels. Main conclusions  The assembly of palm communities in the western Amazon appears to be driven primarily by species sorting according to hydrology and soil, but with dispersal limitation also playing an important role. The importance of environmental characteristics and geographical distance varies depending on plant height and geographical range size in agreement with functional predictions, increasing our confidence in the inferred assembly mechanisms.

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).