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Abstract Recent evidence suggests that community science and herbarium datasets yield similar estimates of species' phenological sensitivities to temperature. Despite this, two recent studies by Alecrim et al. (2023) and Miller et al. (2022) found very different results when using different data sources (community science and herbarium specimens, respectively) to investigate whether warming threatens wildflowers with phenological mismatch in relation to shading by deciduous trees.Here, we investigated whether differences between the two studies' results could be reconciled by testing four hypotheses related to model design, species, spatiotemporal data extent and phenophase.Hybrid model structures brought results from the two datasets closer together but did not fully reconcile the differences between the studies. Neither the species nor the phenophase selected for analysis seemed to be responsible for differences in results. Cropping the datasets to match spatial and temporal extents appeared to reconcile most differences but only at the cost of much higher uncertainty associated with reduced sample size.Synthesis: Our analysis suggests that although species‐level estimates of phenological sensitivity may be similar between community science and herbarium datasets, inherent differences in the types and extent of data may lead to contradictory inference about complex biotic interactions. We conclude that, until community science data repositories expand to match the range of climate conditions present in herbarium collections or until herbarium collections match the spatial extent and temporal frequency of community science repositories, ecological studies should ideally be evaluated using both datasets to test the possibility of biased results from either.
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From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets
Abstract Monthly and daily gridded precipitation datasets are one of the most demanded products in climatology and hydrology. These datasets describe the high spatial and temporal variability of precipitation as a continuous surface and for defined periods. However, due to the complex characteristics of precipitation, it is difficult to obtain accurate estimations. Thus, the creation of a gridded dataset from observations requires the comprehensive and precise application of quality control, reconstruction, and gridding procedures. Yet, despite multiple advances, most of the gridded datasets created and published since the mid‐1990s to the present use a wide variety of techniques, methods, and outputs, which can completely change the final representativity of the data. It is, therefore, critical to provide general guidelines for the development of future and more robust gridded datasets based on the data characteristics, geographical factors, and advanced statistical techniques. We identified gaps and challenges for near‐future perspectives and provide guidelines for implementing improved approaches based on the performance of 48 products. Finally, we concluded that, despite better spatial and temporal resolutions, data access, and data processing capabilities, observational coverage remains a challenge. Moreover, scientists should adopt tailored strategies to improve the representativity and uncertainty of the estimates. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water ExtremesScience of Water > Methods
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- Award ID(s):
- 1743738
- PAR ID:
- 10360736
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- WIREs Water
- Volume:
- 8
- Issue:
- 6
- ISSN:
- 2049-1948
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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