Results
Over the 5 years of study, we observed 4,261 total interactions across
836 links between 267 species of animal visitors to 41 species of
plants. Flower visitor species consisted of 109 Hymenoptera, 63 Diptera,
36 Coleoptera, 35 Hemiptera, 21 Lepidoptera, 3 Orthoptera, 1
Raphidioptera (Agulla sp.), and 1 hummingbird (Selasphorus
platycercus ). Plant species consisted of 14 Asteraceae, 5 Rosaceae, 3
Fabaceae, 3 Orobanchaceae, 2 Ranunculaceae, and 1 of each Apiaceae,
Boraginaceae, Campanulaceae, Crassulaceae, Gentianaceae, Geraniaceae,
Hydrophyllaceae, Melanthiaceae, Onagraceae, Primulaceae, and Rubiaceae.
Removal of flower visitor groups not commonly regarded to be pollinators
(Hemiptera, Orthoptera, Raphidioptera; but see (Wardhaugh 2015)) before
analysis yielded nearly identical results The aggregated network showed
a nested structure (NODF = 25) and connectance values (0.08) that are
typical of plant–pollinator networks (Schwarz et al. 2020). The
network had one module. Observed sampling completeness of interaction
richness was at 52% of the Chao2 estimator, 64% of the Jack1
estimator, and 81% of the bootstrapped estimator. Observed sampling
completeness of pollinator richness was at 62% of the Chao2 estimator,
71% of the Jack1 estimator, and 85% of the bootstrapped estimator.
Observed sampling completeness of plant richness was at 90% of the
Chao2 estimator, 93% of the Jack1 estimator, and 97% of the
bootstrapped estimator. Environmental conditions, such as temperature,
varied temporally throughout the season and across years (Fig S1), and
spatially among plots, as did soil moisture (Fig S2).
Interactions between generalists showed higher temporal and spatial
persistence. Interactions in the core of the nested network tended to
have higher inter-annual persistence (Spearman’s rank correlation
coefficient = 0.35, P << 0.001; Fig 1a),
intra-annual persistence (Spearman’s rank correlation coefficient =
0.22, P << 0.001; Fig 1b), and inter-plot
persistence (Spearman’s rank correlation coefficient = 0.35, P
<< 0.001; Fig 1c). Observed Spearman’s rank
correlation coefficients for all three persistence variables were higher
than those expected under null models (Fig S3).
Temporal and spatial interaction persistence values were interrelated.
That is, interactions with higher interannual persistence tended to have
longer phenophases (Spearman’s rank correlation coefficient: 0.59, P
<< 0.001) and be more widespread among plots
(Spearman’s rank correlation coefficient: 0.73, P <<
0.001). Interactions with longer phenophases tended to be more
widespread among plots (Spearman’s rank correlation coefficient: 0.78, P
<< 0.001).
At the species level, inter-annual persistence, longer phenophases, and
higher plot occurrence were associated with generalization (Fig 2, S4).
For both plants and pollinators, species that had higher inter-annual
persistence were closer to the core of the nested network (Fig 2a, b;
for plants: R2 = 0.73, P << 0.001,
for pollinators R2 = 0.61, P <<
0.001). Both plant and pollinator species with longer phenophases were
closer to the core of the nested network (Fig 2a, b; for plants:
R2 = 0.72, P << 0.001, for
pollinators R2 = 0.70, P <<
0.001). Both plant and pollinator species that were more widespread
among plots were closer to the core of the nested network (Fig 2c, d;
for plants: R2 = 0.58, P << 0.001,
for pollinators R2 = 0.57, P <<
0.001). Using degree (number of links), as a proxy for generalization
showed similar patterns as species proximity to the network core (Fig
S4). Finally, phenophase length for plants and pollinators was
associated with broader ranges of temperatures on the mornings of
sampling days (for plants: Pearson’s r = 0.67; for pollinators Pearson’s
r = 0.81).