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- W4200132364 endingPage "108741" @default.
- W4200132364 startingPage "108741" @default.
- W4200132364 abstract "High-severity wildfire in arid regions has caused ecological state change, transforming previously forested areas into shrublands. This dramatically alters the microclimatic conditions, which can exceed the climatic tolerance of tree seedlings, rendering the likelihood of returning post-wildlife landscapes to their previous state relatively low. Characterizing microclimatic variability across severely burned landscapes could allow for identifying locations where seedling survival is more likely. We used a combination of small unmanned aircraft system imagery, satellite data and in-situ microclimate data recordings, together with a machine learning approach, to model monthly near-ground minimum, mean and max temperature as well as relative humidity and vapor pressure deficit in a previously forested area, which is now dominated by two different shrub species. Spatially explicit models predicted recorded microclimate well (r = 0.73 to 0.97), and model projections highlight that at any given location in the hottest month, the solar buffering capacity of existing vegetation can alter the maximum temperature by ∼12 °C, increase relative humidity by ∼20% and reduced vapor pressure deficit by 0.3mbar relative to open areas. By harnessing these microclimate refugia, the success rate of reforestation efforts in post-wildfire landscapes could be substantially increased and mitigate seedlings from climate warming at local scales." @default.
- W4200132364 created "2021-12-31" @default.
- W4200132364 creator A5026162922 @default.
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- W4200132364 date "2022-02-01" @default.
- W4200132364 modified "2023-10-16" @default.
- W4200132364 title "Identifying microclimate tree seedling refugia in post-wildfire landscapes" @default.
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- W4200132364 doi "https://doi.org/10.1016/j.agrformet.2021.108741" @default.
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