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1.
Am J Bot ; 103(9): 1607-17, 2016 09.
Artículo en Inglés | MEDLINE | ID: mdl-27638918

RESUMEN

PREMISE OF THE STUDY: California experienced severe drought between 2012 and 2016. During this period, we compared seasonal changes in tissue-water relations among eight fern species in the Santa Monica Mountains of southern California to elucidate differential mechanisms of drought survival and physiological performance during extreme water deficits. METHODS: We monitored seasonal changes in water potential (Ψmd) and dark-adapted chlorophyll fluorescence (Fv/Fm), assessed tissue-water relations including osmotic potential at saturation and the turgor loss point (Ψπ, sat and Ψπ, tlp), and measured, for two evergreen species, xylem-specific and leaf-specific hydraulic conductivity (Ks and Kl) and vulnerability of stem xylem to water stress-induced embolism (water potential at 50% loss hydraulic conductivity, Ψ50). KEY RESULTS: Species grew in either riparian or chaparral understory. The five chaparral species had a wider range of seasonal water potentials, root depths, and frond phenological traits, including one evergreen, two summer-deciduous, and two desiccation-tolerant (resurrection) species. Evergreen species were especially diverse, with an evergreen riparian species maintaining seasonal water potentials above -1.3 MPa, while an evergreen chaparral species had seasonal water potentials below -8 MPa. In those two species the Ψ50 values were -2.5 MPa and -4.3 MPa, respectively. CONCLUSIONS: Observed differences in physiological performance among eight fern species reflected niche partitioning in water utilization and habitat preference associated with distinct phenological traits. We predict differential survival among fern species as future drought events in California intensify, with desiccation-tolerant resurrection ferns being the most resistant.


Asunto(s)
Cambio Climático , Sequías , Helechos/metabolismo , California , Desecación , Ecosistema , Estaciones del Año , Especificidad de la Especie , Agua/metabolismo
2.
Remote Sens Ecol Conserv ; 8(1): 57-71, 2022 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-35873085

RESUMEN

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha-1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

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