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1.
Proc Natl Acad Sci U S A ; 117(52): 33711-33718, 2020 12 29.
Artigo em Inglês | MEDLINE | ID: mdl-33318215

RESUMO

Coral is the life-form that underpins the habitat of most tropical reef ecosystems, thereby supporting biological diversity throughout the marine realm. Coral reefs are undergoing rapid change from ocean warming and nearshore human activities, compromising a myriad of services provided to societies including coastal protection, fishing, and cultural practices. In the face of these challenges, large-scale operational mapping of live coral cover within and across reef ecosystems could provide more opportunities to address reef protection, resilience, and restoration at broad management- and policy-relevant scales. We developed an airborne mapping approach combining laser-guided imaging spectroscopy and deep learning models to quantify, at a large archipelago scale, the geographic distribution of live corals to 16-m water depth throughout the main Hawaiian islands. Airborne estimates of live coral cover were highly correlated with field-based estimates of live coral cover (R2 = 0.94). Our maps were used to assess the relative condition of reefs based on live coral, and to identify potential coral refugia in the face of human-driven stressors, including marine heat waves. Geospatial modeling revealed that water depth, wave power, and nearshore development accounted for the majority (>60%) of live coral cover variation, but other human-driven factors were also important. Mapped interisland and intraisland variation in live coral location improves our understanding of reef geography and its human impacts, thereby guiding environmental management for reef resiliency.


Assuntos
Antozoários/fisiologia , Conservação dos Recursos Naturais , Recifes de Corais , Animais , Ilhas , Modelos Biológicos , Reprodutibilidade dos Testes
2.
Ecol Appl ; 32(2): e2519, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-34918400

RESUMO

Native forests of Hawai'i Island are experiencing an ecological crisis in the form of Rapid 'Ohi'a Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawai'i's keystone endemic tree, known as 'ohi'a (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser-guided imaging spectroscopy (LGIS) of forests on Hawai'i Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD-infected and healthy 'ohi'a canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green 'ohi'a, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non-linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non-structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near-infrared (700-1300 nm) and shortwave-infrared regions (1300-2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M. polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms.


Assuntos
Myrtaceae , Árvores , Florestas , Folhas de Planta/fisiologia , Tecnologia de Sensoriamento Remoto
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