Across taxa, the timing of life‐history events (phenology) is changing in response to warming temperatures. However, little is known about drivers of variation in phenological trends among species. We analysed 168 years of museum specimen and sighting data to evaluate the patterns of phenological change in 70 species of solitary bees that varied in three ecological traits: diet breadth (generalist or specialist), seasonality (spring, summer or fall) and nesting location (above‐ground or below‐ground). We estimated changes in onset, median, end and duration of each bee species' annual activity (flight duration) using quantile regression. To determine whether ecological traits could explain phenological trends, we compared average trends across species groups that differed in a single trait. We expected that specialist bees would be constrained by their host plants' phenology and would show weaker phenological change than generalist species. We expected phenological advances in spring and delays in summer and fall. Lastly, we expected stronger shifts in above‐ground versus below‐ground nesters. Across all species, solitary bees have advanced their phenology by 0.43 days/decade. Since 1970, this advancement has increased fourfold to 1.62 days/decade. Solitary bees have also lengthened their flight period by 0.44 days/decade. Seasonality and nesting location explained variation in trends among species. Spring‐ and summer‐active bees tended to advance their phenology, whereas fall‐active bees tended to delay. Above‐ground nesting species experienced stronger advances than below‐ground nesting bees in spring; however, the opposite was true in summer. Diet breadth was not associated with patterns of phenological change. Our study has two key implications. First, an increasing activity period of bees across the flight season means that bee communities will potentially provide pollination services for a longer period of time during the year. And, since phenological trends in solitary bees can be explained by some ecological traits, our study provides insight into mechanisms underpinning population viability of insect pollinators in a changing world.
Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, how these data source compare in phenological analyses is unclear. We modeled butterfly phenology in relation to traits and climate using parallel analyses of incidental and survey data, to explore their shared utility and potential for analytical integration. One workflow aggregated “Pollard” surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. For both datasets, inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species overwintering at more advanced stages. Other traits related to habitat, feeding, dispersal, and voltinism had mixed or no impacts. Our results suggest that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.
more » « less- PAR ID:
- 10381800
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Phenology is one of the most immediate responses to global climate change, but data limitations have made examining phenology patterns across greater taxonomic, spatial and temporal scales challenging. One significant opportunity is leveraging rapidly increasing data resources from digitized museum specimens and community science platforms, but this assumes reliable statistical methods are available to estimate phenology using presence‐only data. Estimating the onset or offset of key events is especially difficult with incidental data, as lower data densities occur towards the tails of an abundance distribution.
The Weibull distribution has been recognized as an appropriate distribution to estimate phenology based on presence‐only data, but Weibull‐informed estimators are only available for onset and offset. We describe the mathematical framework for a new Weibull‐parameterized estimator of phenology appropriate for any percentile of a distribution and make it available in an
r package,phenesse . We use simulations and empirical data on open flower timing and first arrival of monarch butterflies to quantify the accuracy of our estimator and other commonly used phenological estimators for 10 phenological metrics: onset, mean and offset dates, as well as the 1st, 5th, 10th, 50th, 90th, 95th and 99th percentile dates. Root mean squared errors and mean bias of the phenological estimators were calculated for different patterns of abundance and observation processes.Results show a general pattern of decay in performance of estimates when moving from mean estimates towards the tails of the seasonal abundance curve, suggesting that onset and offset continue to be the most difficult phenometrics to estimate. However, with simple phenologies and enough observations, our newly developed estimator can provide useful onset and offset estimates. This is especially true for the start of the season, when incidental observations may be more common.
Our simulation demonstrates the potential of generating accurate phenological estimates from presence‐only data and guides the best use of estimators. The estimator that we developed, phenesse, is the least biased and has the lowest estimation error for onset estimates under most simulated and empirical conditions examined, improving the robustness of these estimates for phenological research.
