Biology subjects

Meiners, J. M.

Publications and source records attributed to Meiners, J. M..

3 recordsLinked to original sources

bioRxiv2020

The influence of data type and functional traits on native bee phenology metrics: Opportunistic versus inventory records

Efforts to understand activity patterns of bees, our most important pollinators, often rely on opportunistically collected museum records to model temporal shifts or declines. This type of data, however, may not be suitable for this purpose given high spatiotemporal variability of native bee activity. By comparing phenological metrics calculated from intensive systematic inventory data with those from opportunistic museum records for bee species spanning a range of functional traits, we explored biases and limitations of data types to determine best practices for bee monitoring and assessment. We compiled half a million records of wild bee occurrence from opportunistic museum collections and six systematic inventory efforts, focusing analyses on 45 well-represented species that spanned five functional traits: sociality, nesting habits, floral specialization, voltinism, and body size. We then used permutation tests to evaluate differences between data types in estimating three phenology metrics: flight duration, number of annual abundance peaks, and date of the highest peak. We used GLMs to test for patterns of data type significance across traits. All 45 species differed significantly in the value of at least one phenology metric depending on the data type used. The date of the highest abundance peak differed for 40 species, flight duration for 34 species, and the number of peaks for 15 species. The number of peaks was more likely to differ between data types for larger bees, and flight duration was more likely to differ for larger bees and specialist bees. Our results reveal a strong influence of data type on phenology metrics that necessitates consideration of data source when evaluating changes in phenological activity, possibly applicable to many taxa. Accurately assessing phenological change may require expanding wild bee monitoring and data sharing.

ecology↗

bioRxiv2018

Comparison of large-scale citizen science data and long-term study data for phenology modeling

Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and inter-annual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science National Phenology Network (NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built process based phenology models for species common to each dataset. From these models we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two datasets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for NPN data had the lowest error when using models built from the NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. Accordingly, the choice of dataset depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale NPN data, which has high spatial representation and a large regional species pool. LTER datasets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models which integrate the strengths of both datasets.

ecology↗

bioRxiv2016

Bees Without Flowers: Before Peak Bloom, Diverse Native Bees Visit Insect-produced Honeydew Sugars

Bee foragers respond to complex visual, olfactory, and extrasensory cues to optimize searches for floral rewards. Their abilities to detect and distinguish floral colors, shapes, volatiles, and ultraviolet signals, and even gauge nectar availability from changes in floral humidity or electric fields are well studied. Bee foraging behaviors in the absence of floral cues, however, are rarely considered. We observed forty-four species of wild bees visiting inconspicuous, non-flowering shrubs during early spring in a protected, Mediterranean habitat. We determined experimentally that these bees were accessing sugary honeydew secretions from scale insects without the aid of standard cues. While honeydew use is known among some social Hymenoptera, its use across a diverse community of mostly solitary bees is a novel observation. The widespread ability of native bees to locate and use unadvertised, non-floral sugars suggests unappreciated sensory mechanisms and/or the existence of a social foraging network among solitary bees that may influence how native bee communities cope with increasing environmental change.

ecology↗