Validating forest types based on geological and land‐form features in central <scp>A</scp>mazonia

Figueiredo, Fernando O. G.; Costa, Flávia R. C.; Nelson, Bruce; Pimentel, Tânia P.

doi:10.1111/jvs.12078

Cited by 26 publications

(22 citation statements)

References 67 publications

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“…We also measured soil properties on 450 soil profiles to directly assess nutrient availability, niche diversity and long-term ecosystem stability. Geospatial data were also used to account for environmental drivers of regional diversity such as rainfall 13 or geomorphology 17 , which have already shown to be explanatory of broad scale floristic patterns in French Guiana 18 , 19 .…”

Section: Introductionmentioning

confidence: 99%

Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes

Guitet

Sabatier

Brunaux

et al. 2018

Sci Rep

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Disturbances control rainforest dynamics, and, according to the intermediate disturbance hypothesis (IDH), disturbance regime is a key driver of local diversity. Variations in disturbance regimes and their consequences on regional diversity at broad spatiotemporal scales are still poorly understood. Using multidisciplinary large-scale inventories and LiDAR acquisitions, we developed a robust indicator of disturbance regimes based on the frequency of a few early successional and widely distributed pioneer species. We demonstrate at the landscape scale that tree-species diversity and disturbance regimes vary with climate and relief. Significant relationships between the disturbance indicator, tree-species diversity and soil phosphorus content agree with the hypothesis that rainforest diversity is controlled both by disturbance regimes and long-term ecosystem stability. These effects explain the broad-scale patterns of floristic diversity observed between landscapes. In fact, species-rich forests in highlands, which have benefited from long-term stability combined with a moderate and regular regime of local disturbances, contrast with less diversified forests on recently shaped lowlands, which have undergone more recent changes and irregular dynamics. These results suggest that taking the current disturbance regime into account and including geomorphological stratifications in climate-vegetation models may be an effective way to improve the prediction of changes in species diversity under climate change.

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Section: Introductionmentioning

confidence: 99%

Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes

Guitet

Sabatier

Brunaux

et al. 2018

Sci Rep

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“…Although the quality of the SoilGrids data layers has not yet been properly assessed, inaccuracies must be expected simply because the density of field sampling in Amazonia is very low and other ecophysiological relevant soil variables, such as concentration of base cations (Ca, Mg, K and Na) and phosphorus are missing in the available soil data. These macronutrients have been shown to be important correlates of floristic patterns and forests structure (Cámara‐Leret, Tuomisto, Ruokolainen, Balslev, & Kristiansen, ; Figueiredo, Costa, Nelson, & Pimentel, ; Higgins et al., ; Quesada et al., ; Tuomisto, Ruokolainen, & Yli‐Halla, , Tuomisto et al., ; Zuquim et al., ). CEC is available, but it did not come out as an important variable in our SDMs.…”

Section: Discussionmentioning

confidence: 99%

Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species

Figueiredo

Zuquim

Tuomisto

et al. 2017

Journal of Biogeography

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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...

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“…) and gingers (Figueiredo et al. ). Our results demonstrate that the species and environmental data sets already available in the Amazon region are a good starting point towards better tools and maps for conservation planning.…”

Section: Discussionmentioning

confidence: 99%

Predicting environmental gradients with fern species composition in Brazilian Amazonia

Zuquim

Tuomisto

Jones

et al. 2014

J Vegetation Science

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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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Validating forest types based on geological and land‐form features in central Amazonia

Cited by 26 publications

References 67 publications

Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes

Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes

Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species

Predicting environmental gradients with fern species composition in Brazilian Amazonia

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