LONG-TERM IMPACTS OF AMAZON FOREST DEGRADATION ON CARBON STOCKS AND ANIMAL COMMUNITIES: COMBINING SOUND, STRUCTURE, AND SATELLITE DATA

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dc.contributor.advisorDubayah, Ralphen_US
dc.contributor.advisorMorton, Douglasen_US
dc.contributor.authorRappaport, Danielle Ien_US
dc.contributor.departmentGeographyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-07-01T05:30:45Z
dc.date.available2021-07-01T05:30:45Z
dc.date.issued2020en_US
dc.description.abstractThe Amazon forest plays a vital role in the Earth system, yet forest degradation from logging and fire jeopardizes carbon storage and biodiversity conservation along the deforestation frontier. Polices to reduce forest carbon emissions (REDD+) will fall short of their intended goals unless carbon and biodiversity losses from forest degradation can be monitored over time. Emerging remote sensing tools, lidar and ecoacoustics, provide a means to monitor carbon and biodiversity across spatial, temporal, and taxonomic scales to address data gaps on species distributions and time-scales for recovery. This dissertation draws from a novel multi-sensor perspective to characterize the long-term ecological legacy of Amazon forest degradation across a 20,000 km2 landscape in Mato Grosso, Brazil. It combines high-density airborne lidar, 1100 hours of acoustic surveys, and annual time series of Landsat data to pursue three complementary studies. Chapter 2 establishes the bedrock of the investigation by using fine-scale measurements of structure sampled across a large diversity of degraded forests to model the initial loss and time-dependent recovery of carbon stocks and habitat structure following fire and logging. Chapter 3 models the interactions between sound and structure to predict acoustic community variation, and to account for attenuation in dense tropical forests. Lastly, Chapter 4 uses sound to go beyond structure to identify the specific degradation sequences and pseudo-taxa that give rise to variation in the ‘acoustic guild’ over time. Soundscapes reveal strong and sustained shifts in insect assemblages following fire, and a decoupling of biotic and biomass recovery following logging that defy theoretical predictions (Acoustic Niche Hypothesis). The synergies between lidar and acoustic data confirm the long-term legacy of forest degradation on both forest structure and animal communities in frontier Amazon forests. After multiple fires, forests become carbon-poor, habitats become simplified, and animal communication networks became quieter, less connected, and more homogenous. The combined results quantify large potential benefits to protecting already-burned Amazon forests from recurrent fires. This dissertation paves the way for greater integration of remote sensing and analysis tools to enhance capabilities for bringing biomass and biodiversity monitoring to scale. Building on this research with species-level and multi-temporal measurements will reduce uncertainty around the breakpoints that drive carbon and biodiversity loss following degradation.en_US
dc.identifierhttps://doi.org/10.13016/9ng1-g9ck
dc.identifier.urihttp://hdl.handle.net/1903/27188
dc.language.isoenen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pqcontrolledEcologyen_US
dc.subject.pqcontrolledEnvironmental scienceen_US
dc.subject.pquncontrolledBiodiversityen_US
dc.subject.pquncontrolledCarbon stocksen_US
dc.subject.pquncontrolledConservationen_US
dc.subject.pquncontrolledFireen_US
dc.subject.pquncontrolledLoggingen_US
dc.subject.pquncontrolledTropical forest degradationen_US
dc.titleLONG-TERM IMPACTS OF AMAZON FOREST DEGRADATION ON CARBON STOCKS AND ANIMAL COMMUNITIES: COMBINING SOUND, STRUCTURE, AND SATELLITE DATAen_US
dc.typeDissertationen_US

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