Contribution of Topography and Incident Solar Radiation to Variation of Soil and Plant Litter at an Area with Heterogeneous Terrain

Nettesheim, Felipe Cito; Conto, Tiago de; Pereira, Marcos Gervásio; Machado, Deivid Lopes

doi:10.1590/01000683rbcs20140459

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…The L_MAX model had NDVI (18%) and terrain elevation (13%) as its main inputs. Although the elevation is a topographic variable, it often modulates climate conditions as it is related to physical [This is a non-peer reviewed preprint submitted to EarthArXiv] features that may create 'climate islands' (Badía et al, 2016), either by the processes of rain shadows or via changes on atmospheric lapse rates (Nettesheim et al, 2015). Thus, it is well related to meteorological conditions (Badía et al, 2016), which impact the speed at which parent materials weather, and hence the rate of soil development.…”

Section: Environmental Modulatorsmentioning

confidence: 99%

Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil Mapping to Advance Next-Generation Earth System Models

Miranda¹,

Nóbrega²,

Silva³

et al. 2023

Preprint

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Environmental models often require soil maps to represent the spatial variability of soil properties. However, mapping soils using conventional in situ survey protocols is time-consuming and costly. As an alternative, Digital Soil Mapping (DSM) offers a fast-mapping approach that has the potential to estimate soil properties and their interrelationships over large areas. In this study, we address the currently outdated spatial information on soil properties across a tropical region (approx. 98,000 km2) with a ~700-km longitudinal gradient of contrasting topography, climate, and vegetation in Brazil by developing and applying statistical soil models for this region using a novel hybrid machine learning (HML) framework. This framework reduces prediction redundancies due to high multicollinearity by implementing a recursive feature selector algorithm for input selection. The hybrid framework’s core is composed of the Soil-Landscape Estimation and Evaluation Program (SLEEP) and a calibrated Gradient Boosting Model (GBM) capable of modeling the spatial distribution of soil properties at multiple soil depths. The use of SLEEP and GBM allowed us to explain the spatial distribution of various basic physical and chemical soil properties and their environmental modulators. The model training and testing approach used six topographical, ten meteorological and two vegetation properties, and data from 223 soil profiles across the study area. Our models demonstrated a consistent performance with spatial extrapolations exhibiting r2 values ranging from 0.79 to 0.98, and percent bias (PBIAS) from -1.39 to 1.14%. The properties related to topographic and climatic conditions were dominating when estimating the number of soil layers, percentage of silt and the sum of bases. Our framework features high flexibility, while reducing capital investments and increasing accuracy when compared to traditional mapping protocols that require extensive surveys.

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Section: Environmental Modulatorsmentioning

confidence: 99%

Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil Mapping to Advance Next-Generation Earth System Models

Miranda¹,

Nóbrega²,

Silva³

et al. 2023

Preprint

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“…The origin of Marambaia is associated with geological evolution of the Serra do Mar mountain range; soils here cover the same crystalline gneiss parent material that underlies the Atlantic Forest across this mountain range (Almeida & Carneiro 1998;Menezes et al 2005). Although Marambaia ranges over less altitude (0 to 641 m) than continental sections of Serra do Mar, it is marked by the typical heterogeneous terrain displayed by this mountain range (Nettesheim et al 2015). The heterogeneous topography of Marambaia is characterized by a main ridge stretching across its mountainous terrain ( Fig.…”

Section: Study Areamentioning

confidence: 99%

“…We sampled and determined a total of 18 soil and litter (environmental) predictors (Tab. 1) within each plot (see detailed description of methodology in Nettesheim et al 2015).…”

Section: Data Collectionmentioning

confidence: 99%

“…This suggests that even small terrain variation such as a slight surface change in slope orientation is sufficient to determine different environmental conditions (e.g. distinct amounts of sunlight incidence; see Nettesheim et al 2015) which increase the array of habitats for the tree community. Understanding how heterogeneous terrain is related to the behavior of local-scale community assembly may provide valuable insight into the mechanisms behind the high number of species and high levels of endemism reported for the Atlantic Forest.…”

Section: Introductionmentioning

confidence: 99%

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Local environmental controls of Atlantic Forest tree community assembly on a coastal continental island in southeastern Brazil

et al. 2019

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Tropical forests with high species diversity are commonly found in rugged montane areas. We investigated causes of local tropical forest tree community assembly on a continental island with heterogeneous terrain. We recorded tree community (absolute species abundance), topography, soil, litter and location in 40 sampling units on two opposite sides of the island with similar heterogeneous terrain. We used transformation based Redundancy Analyses and variation partitioning to determine the contribution of environment (topography, soil and litter), spatial structure (geographic location and Moran Eigenvector Maps) and the shared effects of these to explain community assembly. The environment made a significant contribution to explain tree community patterns (species composition and abundance) across all models. Conversely, spatial structure showed minor impact. Contribution of strictly environmental effects and spatially structured environmental effects varied when evaluating each site independently as well as when evaluating the combined data. Evidence suggests that local tropical tree community assembly on heterogeneous terrain may be located much closer to the niche end of the hypothetic niche-neutral continuum. Findings indicate additionally that heterogeneity of environmental factors present in dissected mountainous terrain can affect the way tropical community assembly processes are perceived.

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“…Most of the studies have focused mainly on the effects of soil properties on tree community diversity and ecosystem functioning Rodrigues et al 2019a;2019b). Soil properties change along environmental gradients at different spatiotemporal scales (Nettesheim et al 2015). However, environmental conditions (i.e., climate, topography) and forest attributes (i.e., tree species composition, richness, abundance and biomass) can simultaneously explain chemical soil properties and fertility variability (Baker et al 2009;Malhi et al 2009;Laughlin et al 2015).…”

Section: Introductionmentioning

confidence: 99%

On multiple drivers predicting ecosystem functioning in a tropical forest

Rodrigues¹

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Understand the relative contribution of different factors that can determine the structure and diversity of forest communities along environmental gradients and secondary succession time has been a relevant theme in contemporary ecology. Recently the impact of these environmental factors (e.g., soils, topography, and climate) on biodiversity-ecosystem function relationship has gained importance in understanding tropical forests. Topographic and edaphic gradients together with climate can influence the distribution of functional traits, and consequently, ecosystem functioning. The study of the secondary succession of tropical forests with an approach based on the relation of functional traits and environmental factors has allowed elucidating emerging patterns of the ecosystem processes, mainly of the production and storage of biomass. Thus, the community weighted mean (CWM) are the diversity metrics to test effects on aboveground biomass. This research will assess how abiotic factors and biotic factors affect the assembly of tree communities and the ecosystem functioning of the forest Atlantic in southeastern Brazil. For this, we will analyze how species richness, composition and dominance can change through edaphic-topographic-climatic gradients and time of succession in permanent plots. For this, the following hypotheses will be tested: (1) The edaphic-topographic factors and the time of succession affect the richness, composition and dominance of the species in terms of their contribution to an ecosystem process (2) the variability climate over time of succession has implications for the classification of leaf phenology groups of tree communities. Finally, (3) we propose that AGB increases with time of succession, but changes depending on environmental variability (soil and topography), and that the variation in biomass can be explained by the functional traits of the species through functional dominance (CWM) in secondary forest communities. To answer these questions, we selected three areas with different topographic conditions located in a fragment in the secondary regeneration stage in Viçosa, MG, Brazil. Each area has permanent plots of 1 ha covering a topographic gradient from the valleys to the plateau. Each permanent plot has 100 subplots of 10m x 10m. Totaling two hectares of forest and 200 subplots. In each subplot, all individuals of the living arboreal species with stem circumference showed a height of 10 cm or greater and a height of 130 cm. For each subplot, three topographic variables (elevation, slope and convexity) were measured and calculated using a total station with the aid of an engineer and surface soil samples with a depth of 0 to 10 cm to determine physical and chemical parameters. The aboveground biomass was calculated for each tree individual sampled in the subplots by an allometric equation. We selected different types of functional traits such as leaf phenology, wood density and tree diameter. Multivariate regression analyzes were performed to classify habitat types according to topographic variables and species composition. In addition, we constructed a series of models to explain the effect of potential predictor variables on the response of species richness, species composition, and ecosystem functioning. We also used machine learning to classify leaf phenology groups under the effect of environmental variables. Our study demonstrated that topographic variability, mainly elevation and convexity, determine soil fertility. These results advance our understanding that context-dependent conditions based on topography and soil properties have a high variability at a fine-scale. Furthermore, we found that different topographic conditions and successional timing affect community composition, richness, abundance and proportion of carbon-dominant species over time. We found that different topographic conditions and stand age change community composition, richness, abundance, and carbon dominant species along the late-secondary stage. This study advances our understanding of the mechanisms that drive carbon stock in tropical forests and supports the ‘mass ratio’ hypothesis. We observe that evergreen species show higher richness; meanwhile the deciduous species has a greater contribution to aboveground carbon stock. Thus, the leaf phenology groups can affect the relationships between species richness and aboveground carbon stock. For example, deciduous species are key to maintaining higher carbon stock with smaller numbers of species; meanwhile evergreen species are important to maintain a higher species richness. Thus, we presumed that the leaf phenology group's distribution could be responsible for the cobenefits (positive aboveground carbon stock and species richness relationship) in tropical forests. Using random forest, it was observed that the most influential predictor in the classification of functional groups was topography and soil properties. We emphasize that the information generated in this research can be important for the planning of forest restoration activities (passive and active) based on the high variability of environmental variables on a local scale. We also emphasize the relevance of the functional traits approach to understanding the functioning, conservation and management of tropical forests. Keywords: Biotic and abiotic factors. Cobenefits. Functional diversity. Topographical heterogeneity. Assembly community. Machine learning.

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Contribution of Topography and Incident Solar Radiation to Variation of Soil and Plant Litter at an Area with Heterogeneous Terrain

Cited by 8 publications

References 32 publications

Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil Mapping to Advance Next-Generation Earth System Models

Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil Mapping to Advance Next-Generation Earth System Models

Local environmental controls of Atlantic Forest tree community assembly on a coastal continental island in southeastern Brazil

On multiple drivers predicting ecosystem functioning in a tropical forest

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