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Phenology, the timing of recurrent biological events, is a key mechanism by which species adapt or acclimatize to variable environmental conditions, including those influenced by climate change. Measurable traits, including the onset and end of activity, peak activity, and duration, characterize the phenology of life events, and could be significant predictors of trends in population abundance or stability in a changing climate. Bees provide critical pollination services, and understanding the covariates of bee phenological traits can refine predictions on the vulnerabilities of bees and their services to climate change. We paired 16 years of monthly bee survey data (2002-2019) with climate data for 74 bee species in dryland ecosystems of central New Mexico, USA. Contrary to the current paradigm of temperature as the key driver of insect phenology, twice as many bee species had phenological sensitivity to precipitation (39%) than to temperature (20%). Among phenological traits, the end date of active flying periods was most sensitive to climate. Of the 20% of bee species for which precipitation predicted activity end date, 73% ended activity later in wetter years. Fifteen bee species (~20%) had phenological traits sensitive to temperature, but temperature sensitivity was idiosyncratic, and only four species had earlier onset in warmer years, as expected from results in other biomes. Oligolectic (diet specialist) bee species began, peaked, and ended activity later in the year than polylectic (generalist) species, but phenological traits did not correlate with sociality. All phenological traits showed phylogenetic signal, suggesting evolutionary conservatism of phenology among the common bees of central New Mexico drylands. Finally, species with long activity durations were more common, had greater temporal stability in abundance from year to year, and were less likely to decline over time, perhaps because of their longer window for resource acquisition. Our results suggest that drier climates of the future may shift bee phenological activities toward earlier onset, peak, and end dates, that bees with short activity durations may be among the most sensitive to declines in future climates, and that both generalist and social bees may be able to resist or recover from climate change if they have long durations of flight activity.
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The accuracy of phenology estimators for use with sparsely sampled presence‐only observations
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 a 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.
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- PAR ID:
- 10174916
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- ISSN:
- 2041-210X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/2041-210X.13448
