Dataset Information

Hydrological networks and associated topographic variation as templates for the spatial organization of tropical forest vegetation.

ABSTRACT: An understanding of the spatial variability in tropical forest structure and biomass, and the mechanisms that underpin this variability, is critical for designing, interpreting, and upscaling field studies for regional carbon inventories. We investigated the spatial structure of tropical forest vegetation and its relationship to the hydrological network and associated topographic structure across spatial scales of 10-1000 m using high-resolution maps of LiDAR-derived mean canopy profile height (MCH) and elevation for 4930 ha of tropical forest in central Panama. MCH was strongly associated with the hydrological network: canopy height was highest in areas of positive convexity (valleys, depressions) close to channels draining 1 ha or more. Average MCH declined strongly with decreasing convexity (transition to ridges, hilltops) and increasing distance from the nearest channel. Spectral analysis, performed with wavelet decomposition, showed that the variance in MCH had fractal similarity at scales of ?30-600 m, and was strongly associated with variation in elevation, with peak correlations at scales of ?250 m. Whereas previous studies of topographic correlates of tropical forest structure conducted analyses at just one or a few spatial grains, our study found that correlations were strongly scale-dependent. Multi-scale analyses of correlations of MCH with slope, aspect, curvature, and Laplacian convexity found that MCH was most strongly related to convexity measured at scales of 20-300 m, a topographic variable that is a good proxy for position with respect to the hydrological network. Overall, our results support the idea that, even in these mesic forests, hydrological networks and associated topographical variation serve as templates upon which vegetation is organized over specific ranges of scales. These findings constitute an important step towards a mechanistic understanding of these patterns, and can guide upscaling and downscaling.

SUBMITTER: Detto M

PROVIDER: S-EPMC3799763 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Hydrological networks and associated topographic variation as templates for the spatial organization of tropical forest vegetation.

Detto Matteo M Muller-Landau Helene C HC Mascaro Joseph J Asner Gregory P GP

PloS one 20131018 10

An understanding of the spatial variability in tropical forest structure and biomass, and the mechanisms that underpin this variability, is critical for designing, interpreting, and upscaling field studies for regional carbon inventories. We investigated the spatial structure of tropical forest vegetation and its relationship to the hydrological network and associated topographic structure across spatial scales of 10-1000 m using high-resolution maps of LiDAR-derived mean canopy profile height ( ...[more]

PMID: 24204610

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Project description:The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000 m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300 m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

Project description:BackgroundMapping the aboveground biomass of tropical forests is essential both for implementing conservation policy and reducing uncertainties in the global carbon cycle. Two medium resolution (500 m - 1000 m) pantropical maps of vegetation biomass have been recently published, and have been widely used by sub-national and national-level activities in relation to Reducing Emissions from Deforestation and forest Degradation (REDD+). Both maps use similar input data layers, and are driven by the same spaceborne LiDAR dataset providing systematic forest height and canopy structure estimates, but use different ground datasets for calibration and different spatial modelling methodologies. Here, we compare these two maps to each other, to the FAO's Forest Resource Assessment (FRA) 2010 country-level data, and to a high resolution (100 m) biomass map generated for a portion of the Colombian Amazon.ResultsWe find substantial differences between the two maps, in particular in central Amazonia, the Congo basin, the south of Papua New Guinea, the Miombo woodlands of Africa, and the dry forests and savannas of South America. There is little consistency in the direction of the difference. However, when the maps are aggregated to the country or biome scale there is greater agreement, with differences cancelling out to a certain extent. When comparing country level biomass stocks, the two maps agree with each other to a much greater extent than to the FRA 2010 estimates. In the Colombian Amazon, both pantropical maps estimate higher biomass than the independent high resolution map, but show a similar spatial distribution of this biomass.ConclusionsBiomass mapping has progressed enormously over the past decade, to the stage where we can produce globally consistent maps of aboveground biomass. We show that there are still large uncertainties in these maps, in particular in areas with little field data. However, when used at a regional scale, different maps appear to converge, suggesting we can provide reasonable stock estimates when aggregated over large regions. Therefore we believe the largest uncertainties for REDD+ activities relate to the spatial distribution of biomass and to the spatial pattern of forest cover change, rather than to total globally or nationally summed carbon density.

Dataset Information

Hydrological networks and associated topographic variation as templates for the spatial organization of tropical forest vegetation.

Publications

Hydrological networks and associated topographic variation as templates for the spatial organization of tropical forest vegetation.

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