Earlier spring snowmelt drives arrowleaf balsamroot phenology in montane meadows
Creators: name: Durney, Janice S.
name: Engel, Arden
name: Debinski, Diane
name: Burkle, Laura
Publisher: name: Zenodo
Year: 2022
Subjects: arrowleaf balsamroot
flower duration
flower onset
maximum floral display
Greater Yellowstone Ecosystem
montane meadows
snowpack manipulation
temperature manipulation
phenological event

description: Climate change is causing global shifts in phenology, altering when and how species respond to environmental cues such as temperature and the timing of snowmelt. These shifts may result in phenological mismatches among interacting species, creating cascading effects on community and ecosystem dynamics. Using passive warming structures and snow removal, we examined how experimentally increased temperatures, earlier spring snowmelt, and the poorly understood interaction between warming and earlier spring snowmelt affected flower onset, flowering duration, and maximum floral display of the spring flowering montane species, Arrowleaf Balsamroot (<em>Balsamorhiza sagittata</em>), over a seven-year period. Additionally, potential cumulative effects of treatments were evaluated over the study duration. The combination of heating with snow removal led to earlier flower onset, extended flowering duration, and increased maximum floral display. While there was year-to-year variation in floral phenology, the effect of heating with snow removal on earlier onset and maximum floral display strengthened over time. This suggests that short-term studies likely underestimate the potential for climate change to influence phenological plant traits. Overall, this research indicates that<em> Balsamorhiza sagittata</em>'s flowering onset responded more strongly to snow removal than to heating, but the combination of heating with snow removal allowed plants to bloom earlier, longer, and more profusely, providing more pollinator resources in spring. If warming and early snowmelt cause similar responses in other plant species besides<em> Balsamorhiza sagittata</em>, these patterns could mitigate phenological mismatches with pollinators by providing a wider window of time for interaction and resiliency in the face of change. This example demonstrates that a detailed understanding of how spring-flowering plants respond to specific aspects of predicted climatic scenarios will improve our understanding of the effects of climate change on native plant-pollinator interactions in montane ecosystems. Studies like this help elucidate long-term physiological effects of climate-induced stressors on plant phenology in long-lived forbs.
description: There are 271 missing values in this dataset. Funding provided by: Decagon Devices*<br>Crossref Funder Registry ID: <br>Award Number: Funding provided by: the Xerces Society*<br>Crossref Funder Registry ID: <br>Award Number: Funding provided by: the Center for Global and Regional Environmental Research*<br>Crossref Funder Registry ID: <br>Award Number: Funding provided by: Department of Ecology, Evolution, and Organismal Biology, Iowa State University<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100015300<br>Award Number: Funding provided by: Iowa State University graduate program in Ecology and Evolutionary Biology*<br>Crossref Funder Registry ID: <br>Award Number: Funding provided by: Montana State University<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100009501<br>Award Number: Funding provided by: Disney Conservation Fund<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100012168<br>Award Number: Funding provided by: the University of Wyoming NPS Research Station*<br>Crossref Funder Registry ID: <br>Award Number: Funding provided by: Idaho NSF EPSCoR*<br>Crossref Funder Registry ID: <br>Award Number: EPS 0814387
description: This data was collected from May to July in 2013 through 2019. Individual balsamroot plants were monitored every three days from greening to full senescence. Flowering onset, flowering duration, and maximum floral display data was analyzed using linear mixed effects models and generalized mixed effects models with individuals nested within plots as a random effect.