Triangulating from the cairns…
Climate change is shaping interactions within alpine ecosystems. Increase in global temperatures and decrease in snowpack are shifting phenology and raising concerns about future plant productivity, competition, and community dynamics. Phenological shifts are occurring more prominently in cold, high-elevation environments, making alpine plants the most susceptible to warming. As advanced snowmelt timing is causing alpine plants to flower earlier and changing the length of the growing season, the interactions between spatially clustered and non-clustered, or isolated, plants are becoming more important for understanding alpine community reorganization. While spatially clustered plants can facilitate each other to promote community resilience to climate change, isolated plants face greater risk to spring frost events, topographic exposure, and other variables. Variation in slope and aspect influence incident solar radiation at the soil surface, impacting the patchiness of snowpack, direction of snowmelt streamflow, vegetation growth, and plant distribution. Using field data from the 2026 year, this study addressed the changes in flowering phenology at a NSF-Long Term Research in Environmental Biology (NSF-LTREB) site at Mount Baldy (38.978725°N, 107.042104°W), located northwest of Gothic, Colorado. I predicted that (H1) compared to 2015 and 2025 data, isolated plants will flower earlier than clustered plants due to lack of moisture retention and thermal buffering, (H2A) that due to lack of soil moisture this season’s early snowmelt will shorten flowering duration or alternatively (H2B) that rain and monsoons will extend the length of flowering duration, (H3) plants on south-facing slopes will exhibit earlier first flowering dates than north-facing plants, and (H4) the flowering time of plants has advanced since 2015. Twice a week from June–July, open flowers were counted amongst four species, Heterotheca villosa (HETVIL), Ivesia gordonii (IVEGOR), Lupinus argenteus (LUPARG), and Senecio crassulus (SENCRA). 200 individual plants were counted, 50 per species, and 25 clustered and isolated. With a final sample size of n=38, Gaussian curves were fitted for each species to estimate phenology and to predict when clustered and isolated plants flowered this season in comparison previous years; linear regressions were also run to model effects of clustering and plant size on first flowering date, separately. The 2026 curves were compared with 2015 phenology curves to identify the effects of clustering over time. Snowmelt date and aspect were separately compared with flowering duration and the first flowering dates using a linear regression model. Clustering, plant size, snowmelt date, and topographic aspect did not have a strong effect on the first flowering date or flowering duration for each species. However, within SENCRA, stronger clustering effects were consistent with our hypothesis (H1) that clustered plants would flower earlier than isolated plants. (H1) was supported by SENCRA, but not supported by HETVIL, IVEGOR, and LUPARG; the rest of the hypotheses (H2, H3, H4) were rejected by all species.
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