Material and Methods
We conducted this study in a subalpine meadow at the University of Colorado’s Mountain Research Station (40°01’48”N 105°32’26”W), located at 2900 m of elevation 22 km west of Boulder, CO, USA. The meadow faces east and is surrounded by aspen and spruce-fir forest. We collected interaction data weekly during the entire flowering period from 2015 to 2019. The flowering season at the study site typically starts after snowmelt in late May to early June and extends to late September. Interactions were recorded on 16 – 18 weeks per year. Sampling was done on average 6.95 days apart (SD = 1.17 days).
On each sampling date, we sampled plant–pollinator interactions in six 20-30 m × 2 m plots (five 30 m × 2 m and one 20 m × 2 m) by observing flowers (plant-centred sampling). Sampling was conducted in fair weather during mornings between 8:00 am and 12:00 pm, a time range when pollinator activity is high and before the onset of thunderstorms that often occur at mid-day during the summer in the Rocky Mountains. We sampled plant–pollinator interactions within each plot (in random order) by doing 15-min surveys in which we carefully observed all flowers for visitors while walking the periphery of plots to minimize trampling. When we observed an interaction, defined as a pollinator contacting the reproductive structures of a flower, we recorded the identity of the plant and pollinator species. Insect pollinators were collected with aspirators or aerial nets for later identification in the laboratory. Expert entomologists (see Acknowledgements) assisted with insects that are difficult to identify. To assess the thoroughness of the sampling effort, we compared observed richness of plants, pollinators, and interactions to values from three commonly used richness estimators: Chao2, first-order jackknife (Jack1), and bootstrapped values (Chacoff et al. 2011; Gotelli & Colwell 2011).
We recorded temporal and spatial variation in abiotic conditions. Temperature is fundamental in constraining pollinator foraging (Corbetet al. 1993; Willmer & Stone 2004) and plant flowering (Schemskeet al. 1978). To measure temporal variation in temperature at the site, we compiled air temperature data from every morning (8 am–12 pm) during which sampling was conducted from a weather station located ~1 km away at a similar elevation, 3020 m (SNOTEL Site Niwot, 663). Soil moisture affects plant growth and reproduction (Fayet al. 2000). To quantify aspects of spatial variation in abiotic conditions among plots we measured ground temperature and soil moisture every 2 meters along sampling plots on one occasion at 7:55–8:38 am on 6 July 2018. To measure ground temperature, we used a handheld infrared thermometer. To measure soil moisture, we used a time-domain reflectometry (TDR) moisture sensor.
To test the relationship between the network position of interactions and their temporal or spatial persistence we used Spearman’s rank-order correlation tests between the proximity to the core of the nested network and each variable of temporal or spatial persistence: the number of years, the span of days (phenophase), and the number of plots in which interactions were recorded. The position of each interaction was calculated using a maximally nested matrix. To this end, we first compiled all the observations from the study into a plant–pollinator interaction matrix, sorting rows and columns to maximize matrix binary nestedness. This sorting organizes plant species (in rows) and pollinator species (in columns) from most specialist to most generalist such that generalist species are packed into the top left corner of the matrix. We then used this matrix organization to assess the relationship between proximity to the core of the nested network and persistence values for each temporal and spatial variable. The proximity of interactions to the core of the nested network was calculated as one minus the standardized Euclidean distance of each interaction to the upper-left cell in the nested matrix with the distance between each adjacent cell equal to one (as in Chacoff et al. 2018). We tested for the number of modules present to determine if the network could have multiple cores.
To assess the ecological significance of our results, we compared the observed Spearman’s correlation coefficients for the relationships between proximity to the core of the nested network and temporal or spatial persistence values and compared these coefficients to those from null models. Null models generated 1000 randomized matrices by shuffling persistence values within matrices while fixing marginal totals and connectance, which we judged to represent a conservative null model. To determine how temporal and spatial variables are related to one another, e.g., whether interactions that have longer phenophases tend to be more persistent across years, we tested for correlations between each combination of temporal or spatial persistence variables with Spearman’s rank correlation tests.
We used linear regressions with a 2nd degree polynomial to assess how plant and pollinator species’ proximity to the network core (row or column number in the nested network divided by the total number of rows or columns) relate to inter-annual occurrence), intra-annual occurrence, and inter-plot occurrence. Species proximity to the nested core was correlated with degree, the number of interacting partner species, a common metric of species specialization (for plants: Pearson’s r = 0.87; for pollinators: Pearson’s r = 0.70). Finally, to link species’ phenophases to their environmental tolerances, we correlated the range of days in which pollinator or plant species were recorded as interacting with the range of temperature recorded in the mornings during those ranges of dates.
All analyses were performed in R version 4.0.2 (R Core Team 2020). We used the vegan package version 2.5-6 for calculating richness estimates (Oksanen et al. 2010), the bipartite package version 2.15 for network analyses, visualization, and null models (using the swap.web function) (Dormann et al. 2008), and the reshape2 package version for data formatting (Wickham 2007).