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Lynn C. Sweet

Researcher at University of California, Riverside

Publications -  15
Citations -  351

Lynn C. Sweet is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Climate change & Habitat. The author has an hindex of 7, co-authored 12 publications receiving 287 citations. Previous affiliations of Lynn C. Sweet include Lynn University & University of California, Santa Barbara.

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Shrinking windows of opportunity for oak seedling establishment in southern California mountains

TL;DR: In this paper, the authors integrate field seedling establishment trials conducted in the southern Sierra Nevada and western Tehachapi Mountains of southern California with spatially downscaled grids of modeled water-year climatic water deficit (CWDwy) and mean August maximum daily temperature (Tmax) to map historical and projected future microclimates suitable for establishment windows of opportunity for Quercus douglasii, a dominant tree species of warm, dry foothill woodlands, and Q.kelloggii, dominant of mesic montane woodlands and forests.
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Validating climate‐change refugia: empirical bottom‐up approaches to support management actions

TL;DR: In this article, the authors reviewed the literature and defined four methods to test refugia predictions and proposed that such bottom-up approaches can lead to improved protected area designations and on-the-ground management actions to reduce influences from non-climate stressors within potential refugias.
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Taxonomic and Life History Bias in Herbicide Resistant Weeds: Implications for Deployment of Resistant Crops

TL;DR: Comparing taxonomic and life history traits of weeds with EHR to a control group (“the world's worst weeds”), it is found weed species that have EHR are significantly over-represented in certain plant families and having certain life history biases.
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Cross-scale modeling of surface temperature and tree seedling establishment in mountain landscapes

TL;DR: In this paper, the authors used a network of field temperature sensors and climate models to estimate microclimate variability of minimum and maximum temperature, which can be applied to improve projections of species' range shifts under climate change.