Aim To map and interpret floristic and geoecological patterns across the Amazon basin by combining extensive field data with basin‐wide Landsat imagery and climatic data. Location Amazonia. Taxon Ground truth data on ferns and lycophytes; remote sensing results reflect forest canopy properties. Methods We used field plot data to assess main ecological gradients across Amazonia and to relate floristic ordination axes to soil base cation concentration, Climatologies at High Resolution for the Earth's Land Surface Areas (CHELSA) climatic variables and reflectance values from a basin‐wide Landsat image composite with generalized linear models. Ordination axes were then predicted across all Amazonia using Landsat and CHELSA, and a regional subdivision was obtained using k‐medoid classification. Results The primary floristic gradient was strongly related to base cation concentration in the soil, and the secondary gradient to climatic variables. The Landsat image composite revealed a tapestry of broad‐scale variation in canopy reflectance characteristics across Amazonia. Ordination axis scores predicted using Landsat and CHELSA variables produced spatial patterns consistent with existing knowledge on soils, geology and vegetation, but also suggested new floristic patterns. The clearest dichotomy was between central Amazonia and the peripheral areas, and the available data supported a classification into at least eight subregions. Main conclusions Landsat data are capable of predicting soil‐related species compositional patterns of understorey ferns and lycophytes across the Amazon basin with surprisingly high accuracy. Although the exact floristic relationships may differ among plant groups, the observed ecological gradients must be relevant for other plants as well, since surface reflectance recorded by satellites is mostly influenced by the tree canopy. This opens exciting prospects for species distribution modelling, conservation planning, and biogeographical and ecological studies on Amazonian biota. Our maps provide a preliminary geoecological subdivision of Amazonia that can now be tested and refined using field data of other plant groups and from hitherto unsampled areas.
Aim: To evaluate the relative importance of climatic versus soil data when predicting species distributions for Amazonian plants and to gain understanding of potential range shifts under climate change.Location: Amazon rain forest. Methods:We produced species distribution models (SDM) at 5-km spatial resolution for 42 plant species (trees, palms, lianas, monocot herbs and ferns) using species occurrence data from herbarium records and plot-based inventories. We modelled species distribution with Bayesian logistic regression using either climate data only, soil data only or climate and soil data together to estimate their relative predictive powers. For areas defined as unsuitable to species occurrence, we mapped the difference between the suitability predictions obtained with climateonly versus soil-only models to identify regions where climate and soil might restrict species ranges independently or jointly.Results: For 40 out of the 42 species, the best models included both climate and soil predictors. The models including only soil predictors performed better than the models including only climate predictors, but we still detected a drought-sensitive response for most of the species. Edaphic conditions were predicted to restrict species occurrence in the centre, the north-west and in the north-east of Amazonia, while the climatic conditions were identified as the restricting factor in the eastern Amazonia, at the border of Roraima and Venezuela and in the Andean foothills.Main conclusions: Our results revealed that soil data are a more important predictor than climate of plant species range in Amazonia. The strong control of species ranges by edaphic features might reduce species' abilities to track suitable climate conditions under a drought-increase scenario. Future challenges are to improve the quality of soil data and couple them with process-based models to better predict species range dynamics under climate change. K E Y W O R D SAmazon rain forest, Bayesian logistic regression, cation exchange capacity, climate change, ecological niche models, soil factors, SoilGrids, species distribution models, species range, tropical soils Fittkau, Junk, Klinge, & Sioli, 1975;Higgins et al., 2011;Sombroek, 2000;Tuomisto & Poulsen, 1996). However, studies focused on the Amazonian rain forests and others also found in other biomes). Species occurrence records were obtained from two sources: plot-based inventories and herbarium records. To ensure data consistency, we targeted species that are easy to identify in the field. We included only species that had more than 20 presence records (see further details: Table S1, Appendix S1 in Supporting Information). 28 km at the equator) into two climatic variables: annual precipitation and dry season length, defined as the maximum consecutive number of months with <100 mm of precipitation. | Environmental dataWe used four remote sensing variables that describe terrain and forest structure properties: elevation, percentage tree cover, per- | Modelling frameworkTo evalu...
The number of species is known to decrease from the humid tropics towards drier and colder climates, but how species richness varies along environmental and spatial gradients within the tropical rain forests is not clear. We inventoried 214 transects of 0.25 ha to document species diversity patterns in an example plant group (ferns and lycophytes) across noninundated rain forests of western and central Amazonia, and assessed how well these conformed with proposed hypotheses about species richness. The observed number of species varied between 6 and 71 per transect. The effective number of species (emphasising the degree of unevenness in species abundances) varied between 1.02 and 8.60, and diversity profiles revealed considerable differences among transects in community structure. Although the density of individuals varied over almost two orders of magnitude, species diversity was better explained by other variables. In particular, within-transect species diversity increased substantially with increasing soil cation concentration. It also increased with soil aluminium concentration, heterogeneity in soil chemistry, annual rainfall and dry season rainfall, and was higher in western than in central Amazonia. Multiple regression models explained up to 70% of the variance in species diversity, but the relationships between species diversity and the environmental gradients became progressively weaker as species abundances were given more weight in the calculation of diversity. Our results conformed to the proposal that site productivity promotes species diversity. This seemed to arise from larger species pools on more fertile soils and in wetter climates, even when it could be expected that the older and more widespread infertile soils would have provided more opportunities for speciation.
Establishing which factors determine species distributions is of major relevance for practical applications such as conservation planning. The Amazonian lowlands exhibit considerable internal heterogeneity that is not apparent in existing vegetation maps. We used ferns as a model group to study patterns in plant species distributions and community composition at regional and landscape scales. Fern species composition and environmental data were collected in 109 plots of 250 × 2 m distributed among four sites in Brazilian Amazonia. Interplot distances varied from 1 to ca 670 km. When floristically heterogeneous datasets were analyzed, the use of an extended Sørensen dissimilarity index rather than the traditional Sørensen index improved model fit and made interpretation of the results easier. Major factors associated with species composition varied among sites, difference in cation concentration was a strong predictor of floristic dissimilarity in those sites with pronounced heterogeneity in cation concentration. Difference in clay content was the most relevant variable in sites with uniform cation concentrations. In every case, environmental differences were invariably better than geographic distances in predicting species compositional differences. Our results are consistent with the ideas that: (1) the relative predictive capacity of the explanatory variables depend on the relative lengths of the observed gradients; and (2) environmental gradients can be hierarchically structured such that gradients occur inside gradients. Therefore, site‐specific relationships among variables can mask the bigger picture and make it more difficult to unravel the factors structuring plant communities in Amazonia.
Aim A major problem for conservation in Amazonia is that species distribution maps are inaccurate. Consequently, conservation planning needs to be based on other information sources such as vegetation and soil maps, which are also inaccurate. We propose and test the use of biotic data on a common and relatively easily inventoried group of plants to infer environmental conditions that can be used to improve maps of floristic patterns for plants in general. Location Brazilian Amazonia. Methods We sampled 326 plots of 250 m × 2 m separated by distances of 1–1800 km. Terrestrial fern individuals were identified and counted. Edaphic data were obtained from soil samples and analysed for cation concentration and texture. Climatic data were obtained from Worldclim. We used a multivariate regression tree to evaluate the hierarchical importance of soils and climate for fern communities and identified significant indicator species for the resultant classification. We then tested how well the edaphic properties of the plots could be predicted on the basis of their floristic composition using two calibration methods, weighted averaging and k‐nearest neighbour estimation. Results Soil cation concentration emerged as the most important variable in the regression tree, whereas soil textural and climatic variation played secondary roles. Almost all the plot classes had several fern species with high indicator values for that class. Soil cation concentration was also the variable most accurately predicted on the basis of fern community composition (R2 = 0.65–0.75 for log‐transformed data). Predictive accuracy varied little among the calibration methods, and was not improved by the use of abundance data instead of presence–absence data. Conclusions Fern species composition can be used as an indicator of soil cation concentration, which can be expected to be relevant also for other components of rain forests. Presence–absence data are adequate for this purpose, which makes the collecting of additional data potentially very rapid. Comparison with earlier studies suggests that edaphic preferences of fern species have good transferability across geographical regions within lowland Amazonia. Therefore, species and environmental data sets already available in the Amazon region represent a good starting point for generating better environmental and floristic maps for conservation planning.
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