Climate-associated phenological advances in bee pollinators and bee-pollinated plants

Bee Phenological Advances.

To evaluate the rate of change in bee phenology over time, we used a general linear mixed model with the day of the year that the specimen was collected as the outcome, year, latitude, and bee sex as predictors, and bee species as a random factor. The overall model including all bee species showed a significant negative slope for collection date as a function of year (Fig. 1A; year estimate ± SE = −0.08 ± 0.01 d⋅y⁻¹, P < 0.001; latitude estimate = 5.42 ± 0.26 d⋅degree latitude⁻¹, P < 0.001; sex estimate = −18.13 ± 0.76 d, P < 0.001, pseudo-R² = 0.45), indicating that spring-active bees are advancing their phenology at a rate of 0.8 d per decade. Over the entire time period studied, from 1880 to 2010, the regression predicts a mean advance of 10.4 ± 1.3 d. Most of this change has occurred during the last 40 y: The slope of bee advance between 1970 and 2010 is more than twice as steep (Fig. 1A; year estimate = −0.18 ± 0.05 d⋅y⁻¹, P < 0.001; latitude estimate = 5.90 ± 0.37 d⋅degree latitude⁻¹, P < 0.001; sex estimate = −18.38 ± 0.93 d, P < 0.001, pseudo-R² = 0.43) as the overall slope, implying that 69% of the total advance, or roughly 7.2 ± 1.9 d, has occurred since 1970. When we analyzed the 1870–1970 period alone, the slope is indistinguishable from 0 (year estimate = −0.01 ± 0.02 d⋅y⁻¹, P = 0.60; latitude estimate = 3.17 ± 0.48 d⋅degree latitude⁻¹, P < 0.001; sex estimate = −16.68 ± 1.31 d, P < 0.001, pseudo-R² = 0.40).

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Fig. 1.

Rates of change in bee phenology and temperature over time. Each point represents a bee specimen used in the analysis. Raw data are shown without correcting for covariates (see text for details). The collection day for each bee specimen, where January first is day 1 (A), and the mean temperature during the month of April (in °C) at the location where the specimen was collected (B). The overall trend (black lines) and the trend from 1970 to 2010 (red lines) are shown. (C) The correlation between collection day and temperature. The overall trend is shown (black line). (D–G) Representative species for the genera in our analysis: A. miserabilis, C. inaequalis, O. lignaria, and B. impatiens queen. (Photos by J.S.A.)

The directional changes we found in bee phenology are likely due to recent climate warming, as has been demonstrated for other organisms (17, 18). Across all of the locations where bee specimens were collected, there is a positive association between mean April temperature and year, when corrected for latitude and longitude (Fig. 1B; year estimate = 0.01 ± 0.001 d⋅y⁻¹, P < 0.001; latitude estimate = −1.30 ± 0.02 d⋅degree latitude⁻¹, P < 0.001; overall model R² = 0.70). As is the case for bee phenology, the slope of this relationship is greater for the last 40 y (year estimate = 0.035 ± 0.003 d⋅y⁻¹, P < 0.001; latitude estimate = −1.30 ± 0.02 d⋅degree latitude⁻¹, P < 0.001; longitude estimate = 0.06 ± 0.02 d⋅degree longitude⁻¹, P < 0.001; overall model R² = 0.74; see also ref. 19). Importantly, when bee specimen collection day was regressed directly against April temperature, there was a significant positive association (Fig. 1C; temperature estimate = −3.61 ± 0.15 d⋅°C⁻¹, P < 0.001; sex estimate = −18.57 ± 0.81 d, P < 0.001, pseudo-R² = 0.46). This supports the idea that climate change is the main factor explaining the observed phenological advances.

When bee specimen collection day was regressed against year for each bee species individually, all 10 species showed negative slopes. In four species this trend was significant (P < 0.05), whereas three other species showed a strong but nonsignificant trend (P < 0.1) (Table 1). Latitude and sex were significant for all species, with bees flying later further north and males of solitary bees flying before females (Table 1). No interaction was detected between sex and year, indicating that flight seasons are shifting earlier at similar rates for male and female solitary bees. Protandry is well-known in solitary bees (20). Thus, detecting both protandry and known latitudinal patterns indicates the appropriateness of our data and methodology for detecting unknown, climate-associated phenological shifts. Last, when bee specimen collection day was regressed directly against April temperature, there was a significant positive association in all species (Table S1). Although in principle bee phenology could respond to climate-associated changes in precipitation as well as temperature, only A. crataegi, the latest-emerging species, showed a model improvement when precipitation was added to the model (Table S1).

Bee natural history traits including nest type and sociality had only weak associations with bee phenological patterns (SI Text, Influence of Bee Natural History Traits in Determining Rates of Advance for Bees). The only significant pattern to emerge was that the bee species that emerge earlier in the season (Fig. S2) are advancing their phenology more quickly (P < 0.001, R² = 0.59; Fig. S3). This same pattern has been observed in plants (14, 21).

Comparing Rates of Advance Between Plants and Bees.

Eighty-seven percent of the world's angiosperm species are pollinated to some degree by animals (22), and recent work has emphasized the potential for negative consequences if phenological mismatches occur between plants and pollinators (23, 24). However, data on changes in pollinator phenology and on corresponding changes in the plants they pollinate are scarce. Two short-term observational studies provide evidence that a focal plant species and its pollinators may experience some mismatch when spring advances (25, 26), but this does not have to be the case (27). Here we used long-term data to compare phenological shifts for 10 bee species to shifts in 106 native plant species that are visited by these same bee species.

Four published studies report long-term data on phenological shifts in entomophilous plants within the broad geographical area of our bee dataset, and we used all four in our analyses. First, Primack and co-workers (15, 28) investigated rates of advance for multiple plant species in Massachusetts, using herbarium records collected between 1885 and 2003 and comparing these to bloom dates for the same species as observed in 2003. Second, Bradley et al. (29) recorded the date of first flowering of 24 plant species common in our study area from 1936 to 1999. They used data collected in Wisconsin, which is geographically outside but at a similar latitude as our study region. Third, Cook et al. (30) recorded the first flowering of 11 plant species in New York State from 1931 to 2008. Finally, Abu-Asab et al. (31) recorded the phenology of 44 plant species in Washington, DC from 1970 to 1999, which corresponds to the period of the greatest temperature changes, both in our bee dataset (Fig. 1B) and in larger climate studies from our region (19).

From each published study, we extracted data only for those plant species that are native to our study region, flower during the flight period of the bee species we studied, and are known to be visited by at least some of these same bee species (Table S2). We compared rates of advance for these plant species to the rates of advance for our 10 bee species as analyzed over the same time period. In all four cases, the mean advance of plants was not different from the mean advance of bees computed over the same time period (all t tests, P > 0.20; Fig. 2). In two studies (28, 31), plants were advancing faster than bees by 23% and 26%, whereas in the other two studies (29, 30), bees advanced faster than plants by 3% and 33%. Our results suggest that there is no clear pattern of divergence between bees and plants. However, it is difficult to know how much of this variability is due to real biological differences in phenology, as opposed to sampling error or methodological differences among studies. For example, in the three plant species that were reported by more than one study (29, 30), the measured rates of phenological advance differ by a mean of 0.14 d⋅y⁻¹ (Fig. S4), which is greater than the mean rate of advance reported by most studies.

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Fig. 2.

Comparison of bee and plant studies. Mean and 95% confidence interval (CI) for the rate of phenological advance for all bee species in combination (filled circles), compared with the mean and 95% CI for plants (hollow circles). The values for bees differ among comparisons because we used only the bee data from the same period over which plants were studied in each comparison. A dotted line is drawn between the bee and plant mean for purposes of visual comparison. From left to right, the plant studies are refs. 28–31.

The lack of a statistically significant difference does not demonstrate that the two groups have similar responses, especially given the large variation in the responses (Fig. 2). Thus, we used an equivalence test (32) to determine the size of the difference between the rates of bee and plant advance within which the observed rates can be considered statistically equivalent. These values ranged from 0.06 to 0.09 d⋅y⁻¹ for the three plant studies covering long-term time periods to 0.17 d⋅y⁻¹ for the plant study covering only the most recent period (Fig. S5). Those equivalence intervals suggest a maximum mismatch of 4–6 d over the ∼70- to 100-y span of the three long-term studies. However, for the most recent period, during which the greatest temperature changes occurred, it suggests a maximum mismatch of 5 d over only 30 y. The observed mismatches are likely to be of little biological significance, given that poor weather can easily prevent plant–pollinator interactions for several days in a row in early spring in our study region. However, if climate warming continues at its recent rapid rate, more significant phenological mismatches could occur in the future.

Overall, results indicate that phenological changes in bees have paralleled changes in the plants that they visit. First, both bees and plants responded to the temperature increases of 1971–1999 (Fig. 1B) by more than doubling their rates of phenological advance (Fig. 2), suggesting a parallel response to climate change. Second, the mean difference between plant and bee responses is small in most cases [Primack et al. (28), 0.024 d⋅y⁻¹; Cook et al. (30), −0.027 d⋅y⁻¹; Bradley et al. (29), −0.002 d⋅y⁻¹; Abu-Asab et al. (31), −0.080 d⋅y⁻¹; Fig. S5]. Third, the directionality of the difference varies among studies (Fig. 2 and Fig. S5). Interestingly, the two plant studies conducted inside cities showed greater advances, suggesting that the potential urban heat-island effect merits further study (33). Fourth, both bees and plants showed greater rates of advance for species that are active earlier in the season, suggesting some correspondence between taxa (14). Fifth, most species of plants and bees are generalists in terms of their interactions (34) (Table S2); thus, most species are not tied to the phenology of a small number of partners. Finally, for most species of plants and bees, an individual is in flower or in flight for at least several weeks. This period is longer than the maximum phenological mismatch predicted to occur by the equivalence test when taking into account the large variability observed. Overall, we conclude that phenological mismatch probably has not occurred already, but that it could occur in the future, as rates of temperature warming increase.

There are several important caveats to our findings. First, our entire approach assumes generalization on the part of both plants and pollinators, as we compared entire distributions rather than specific pairwise interactions. Analogous studies of specialist species are needed, particularly in ecosystem types that are known to be dominated by oligolectic bee species (35, 36) or where precipitation rather than temperature is the main driver for the timing of phenological activities (37, 38). Second, because the most significant climate warming has occurred during the last 40 y, caution is needed when extrapolating from existing data to predict future trends. There is a need to develop more mechanistic, physiological models for bees as well as plants if we want to make predictions beyond the range of our associational data.