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Sevilleta plant phenology predicts stability of primary production from 2002 to 2020.

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Environmental Data Initiative Repository2026-04-25 收录
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https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-sev.344.1
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The temporal stability of plant productivity affects species’ access to resources, exposure to stressors, and strength of interactions with other species in the community, including support to the food web. The magnitude of temporal stability may depend on how a species allocates resources across phenological stages, such as vegetative growth versus reproduction. Understanding whether and how plant phenological traits correlate with the long-term stability of plant biomass is particularly important in highly variable environments, such as drylands. We evaluated whether phenological traits predict the temporal stability of plant species productivity by correlating 18 years of monthly phenology observations with biannual estimates of aboveground plant biomass for 98 plant species from semi-arid grasslands and shrublands. We then paired these phenological traits with potential climate drivers to identify abiotic contexts that favor specific phenological strategies among plant species. Phenological traits strongly predicted the stability of plant species biomass. Plant species with longer annual vegetative phenophases had more stable production over time but also failed to fruit in a greater proportion of years, indicating a growth-reproduction trade-off. Earlier leaf- out dates, longer fruiting duration, and longer time lags between leaf and fruit production also predicted greater temporal stability. Species with stability-promoting traits began growing in drier conditions than their counterparts and experienced greater exposure to stress, evaluated by the wider range of temperatures and precipitation during biologically active periods. Our results suggest that bet-hedging strategies which spread resource acquisition and reproduction over longer time periods help to stabilize plant species productivity in variable environments.
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Environmental Data Initiative
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