Spatial drivers of composition and connectivity across endangered tropical dry forests

Christopher S. Balzotti, Gregory P. Asner, Edith D. Adkins, Elliott W. Parsons

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Tropical dry forests are among the most threatened ecosystems in the world. Rapid loss, degradation and fragmentation of these native ecosystems in a changing climate have driven a time-sensitive need to improve our understanding and management of remaining dry forests. We used advanced remote sensing technologies, combined with extensive field data and machine learning, to better understand how spatial drivers (e.g. climate, fire, human) of canopy species composition vary in importance and correlate with forest cover (total, native and non-native), within an endangered Hawaiian tropical dry forest. Past introductions of non-native, drought-tolerant tree species into this Hawaiian dry forest have created a new forest canopy composition and a loss of native forest biodiversity and connectivity at the landscape scale. Synthesis and applications. Our findings help to spatially visualize the loss and transition of native Hawaiian forests and provide a new conservation planning tool. Conservation and restoration efforts can now be informed by spatial maps of canopy composition, connectivity and determinants of forest cover for the region. For example, our models identified a climatic transition zone between 800 and 1,000 m where native forests exist in high densities, and non-native forests are not yet dominant. This area may be optimal for cost-effective conservation and targeted management. Ecosystems are changing globally at unprecedented rates. The methods presented in this study provide a framework that can be adapted to monitor these changes around the world.

Original languageEnglish (US)
Pages (from-to)1593-1604
Number of pages12
JournalJournal of Applied Ecology
Issue number8
StatePublished - Aug 1 2020


  • Global Airborne Observatory
  • conservation
  • forest cover
  • machine learning
  • remote sensing
  • restoration
  • tropical forest

ASJC Scopus subject areas

  • Ecology


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