Publications by authors named "Daniel Magnabosco Marra"

Article Synopsis
  • - The study examines how disturbances (like wildfires and storms) impact biodiversity, highlighting that effects vary based on disturbance severity, landscape, and species community characteristics.
  • - Results showed mixed outcomes: while some species groups thrived in disturbed environments (e.g., those preferring open canopies), others (like ground-dwelling organisms) faced declines, indicating that disturbances can have both positive and negative impacts on different taxa.
  • - The highest overall biodiversity (α-diversity) was observed in areas with moderate disturbance severity, specifically when about 55% of trees were affected, suggesting that not all disturbances are detrimental to biodiversity.
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Amazon forests account for ~25% of global land biomass and tropical tree species. In these forests, windthrows (i.e.

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Canopy gaps created by wind-throw events, or blowdowns, create a complex mosaic of forest patches varying in disturbance intensity and recovery in the Central Amazon. Using field and remote sensing data, we investigated the short-term (four-year) effects of large (>2000 m(2)) blowdown gaps created during a single storm event in January 2005 near Manaus, Brazil, to study (i) how forest structure and composition vary with disturbance gradients and (ii) whether tree diversity is promoted by niche differentiation related to wind-throw events at the landscape scale. In the forest area affected by the blowdown, tree mortality ranged from 0 to 70%, and was highest on plateaus and slopes.

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Old-growth forest ecosystems comprise a mosaic of patches in different successional stages, with the fraction of the landscape in any particular state relatively constant over large temporal and spatial scales. The size distribution and return frequency of disturbance events, and subsequent recovery processes, determine to a large extent the spatial scale over which this old-growth steady state develops. Here, we characterize this mosaic for a Central Amazon forest by integrating field plot data, remote sensing disturbance probability distribution functions, and individual-based simulation modeling.

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