The importance of soils in predicting the future of plant habitat suitability in a tropical forest

AimsAssessment of the future of biodiversity under climate change has been based on climate-only models. We investigated the effects of including soil information when predicting future suitable areas for selected plant species in Amazonia.MethodsWe modelled current and future suitable habitats for 35 plant species and compared results of climate-only models with those obtained when climatic and edaphic variables were included. We considered six climatic scenarios for 2050 using different algorithms and projections of atmospheric CO2 concentration.ResultsTwenty-five species distribution models had an AUC > 0.69. Out of those, edaphic variables had the greatest contribution in 11 species models, while climatic variables were more important for 14 species. The inclusion of soil variables affected the size and shape of predicted suitable areas, especially in future models. For nearly half of the species, the size of future suitable areas were smaller in climate+soil models than predicted by climate-only models. Area reduction was more extreme in future scenarios with the higher level of CO2 concentration.ConclusionsOur results highlight the importance of moving beyond climatic scenarios when modelling biodiversity responses to climate change. Failure to include soils in the models can overestimate future habitat suitability for many plant species.</div

The Brazilian Program for Biodiversity Research (PPBio) Information System.

The database of the Brazilian Program for Biodiversity Research (PPBio; GIVD ID SA-BR-001) includes data on the environment and biological groups such as plants. It is organized by site, which is usually a grid with 10 to 72 uniformly-distributed plots, and has already surveyed 1,638 relevés across different Brazilian ecosystems. The sampling design is based on the RAPELD system to allow integration of data from diverse taxa and ecosystem processes. RAPELD is a spatially-explicit sampling scheme to monitor biodiversity in long-term ecological research sites and during rapid appraisals of biodiversity that has attracted support from many management agencies, which are using it as their long-term monitoring system. Vegetation surveys include measurements of cover, biomass and number of individuals from woody and herbaceous vascular plants, along with environmental data. We have recently migrated to a metadata catalog and data repository which allows searching for specific groups across all sites. All RAPELD data have been collected since 2001, though the site also allows data from other long-term plots to be archived as associated projects

The Brazilian Program for Biodiversity Research (PPBio) Information System.

The database of the Brazilian Program for Biodiversity Research (PPBio; GIVD ID SA-BR-001) includes data on the environment and biological groups such as plants. It is organized by site, which is usually a grid with 10 to 72 uniformly-distributed plots, and has already surveyed 1,638 relevés across different Brazilian ecosystems. The sampling design is based on the RAPELD system to allow integration of data from diverse taxa and ecosystem processes. RAPELD is a spatially-explicit sampling scheme to monitor biodiversity in long-term ecological research sites and during rapid appraisals of biodiversity that has attracted support from many management agencies, which are using it as their long-term monitoring system. Vegetation surveys include measurements of cover, biomass and number of individuals from woody and herbaceous vascular plants, along with environmental data. We have recently migrated to a metadata catalog and data repository which allows searching for specific groups across all sites. All RAPELD data have been collected since 2001, though the site also allows data from other long-term plots to be archived as associated projects

HERBase: a collection of understorey herb vegetation plots from Amazonia.

In this article, we describe the database HERBase, an exhaustive compilation of published and unpublished data on herb inventories in Amazonia

The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia

To determine the effect of rivers, environmental conditions, and isolation by distance on the distribution of species in Amazonia. Location: Brazilian Amazonia. Time period: Current. Major taxa studied: Birds, fishes, bats, ants, termites, butterflies, ferns + lycophytes, gingers and palms. We compiled a unique dataset of biotic and abiotic information from 822 plots spread over the Brazilian Amazon. We evaluated the effects of environment, geographic distance and dispersal barriers (rivers) on assemblage composition of animal and plant taxa using multivariate techniques and distance- and raw-data-based regression approaches. Environmental variables (soil/water), geographic distance, and rivers were associated with the distribution of most taxa. The wide and relatively old Amazon River tended to determine differences in community composition for most biological groups. Despite this association, environment and geographic distance were generally more important than rivers in explaining the changes in species composition. The results from multi-taxa comparisons suggest that variation in community composition in Amazonia reflects both dispersal limitation (isolation by distance or by large rivers) and the adaptation of species to local environmental conditions. Larger and older river barriers influenced the distribution of species. However, in general this effect is weaker than the effects of environmental gradients or geographical distance at broad scales in Amazonia, but the relative importance of each of these processes varies among biological groups

Local hydrological conditions influence tree diversity and composition across the Amazon basin.

Na Publicação: Carolina V. Castilho; Joice Ferreira

Mapping hydrological environments in central Amazonia: ground validation and surface model based on SRTM DEM data corrected for deforestation

Made available in DSpace on 2016-02-17T21:42:48Z (GMT). No. of bitstreams: 5 mapping.pdf: 3956658 bytes, checksum: 6aaf5de6dee06102ad16bfbcf02d5c07 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) license.txt: 120 bytes, checksum: c5ec8a89f6203da160ca192812b3f657 (MD5) Previous issue date: 2015One of the most important freely available digital elevation models (DEMs) for Amazonia is the one obtained by the Shuttle Radar Topography Mission (SRTM). However, since SRTM tends to represent the vegetation surface instead of the ground surface, the broad use of SRTM DEM as a framework for terrain description in Amazonia is hampered by the presence of deforested areas. We present here two data sets: (1) a deforestation-corrected SRTM DEM for the interfluve between the Purus and Madeira rivers, in central Amazonia, which passed through a careful identification of different environments and has deforestation features corrected by a new method of increasing pixel values of the DEM (Renno, 2009); and (2) a set of 18 hydrological-topographic descriptors based on the corrected SRTM DEM. Deforestation features are related with the opening of an 800 km road in the central part of the interfluve and occupancy of its vicinity. We used topographic profiles from the pristine forest to the deforested feature to evaluate the recovery of the original canopy coverage by minimizing canopy height variation (corrections ranged from 1 to 38 m). The hydrological-topographic description was obtained by the Height Above the Nearest Drainage (HAND) algorithm, which normalizes the terrain elevation (above sea level) by the elevation of the nearest hydrologically connected drainage. The validation of the HAND data set was done by in situ hydrological description of 110 km of walking trails also available in this data set. The new SRTM DEM expands the applicability of SRTM data for landscape modelling; the data sets of hydrological features based on topographic modelling are undoubtedly appropriate for ecological modelling and an important contribution to environmental mapping of Amazonia. The deforestation-corrected SRTM DEM is available at http://ppbio.inpa.gov.br/knb/metacat/naman.318.3/ppbio; the polygons selected for deforestation correction are available at http://ppbio.inpa.gov.br/knb/metacat/naman.317.3/ppbio; the set of hydrological-topographic descriptors is available at http://ppbio.inpa.gov.br/knb/metacat/naman.544.2/ppbio; the environmental description of access trails is available at http://ppbio.inpa.gov.br/knb/metacat/naman.541.2/ppbio; and the limits of deforestation corrections and drainage validation are available at http://ppbio.inpa.gov.br/knb/metacat/liliandias.38.1/ppbio.

Discovering floristic and geoecological gradients across Amazonia

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. © 2019 John Wiley & Sons Lt

The Brazilian Program for Biodiversity Research (PPBio) Information System.

The database of the Brazilian Program for Biodiversity Research (PPBio; GIVD ID SA-BR-001) includes data on the environment and biological groups such as plants. It is organized by site, which is usually a grid with 10 to 72 uniformly-distributed plots, and has already surveyed 1,638 relevés across different Brazilian ecosystems. The sampling design is based on the RAPELD system to allow integration of data from diverse taxa and ecosystem processes. RAPELD is a spatially-explicit sampling scheme to monitor biodiversity in long-term ecological research sites and during rapid appraisals of biodiversity that has attracted support from many management agencies, which are using it as their long-term monitoring system. Vegetation surveys include measurements of cover, biomass and number of individuals from woody and herbaceous vascular plants, along with environmental data. We have recently migrated to a metadata catalog and data repository which allows searching for specific groups across all sites. All RAPELD data have been collected since 2001, though the site also allows data from other long-term plots to be archived as associated projects.201