Heritability and variance components of seed size in wild species: influences of breeding design and the number of genotypes tested.

Seed size affects individual fitness in wild plant populations, but its ability to evolve may be limited by low narrow-sense heritability (h2). h2 is estimated as the proportion of total phenotypic variance (σ2P) attributable to additive genetic variance (σ2A), so low values of h2 may be due to low σ2A (potentially eroded by natural selection) or to high values of the other factors that contribute to σ2P, such as extranuclear maternal effects (m2) and environmental variance effects (e2). Here, we reviewed the published literature and performed a meta-analysis to determine whether h2 of seed size is routinely low in wild populations and, if so, which components of σ2P contribute most strongly to total phenotypic variance. We analyzed available estimates of narrow-sense heritability (h2) of seed size, as well as the variance components contributing to these parameters. Maternal and environmental components of σ2P were significantly greater than σ2A, dominance, paternal, and epistatic components. These results suggest that low h2 of seed size in wild populations (the mean value observed in this study was 0.13) is due to both high values of maternally derived and environmental (residual) σ2, and low values of σ2A in seed size. The type of breeding design used to estimate h2 and m2 also influenced their values, with studies using diallel designs generating lower variance ratios than nested and other designs. e2 was not influenced by breeding design. For some breeding designs, the number of genotypes included in a study also influenced the resulting h2 and e2 estimates, but not m2. Our data support the view that a diallel design is better suited than the alternatives for the accurate estimation of σ2A in seed size due to its factorial design and the inclusion of reciprocal crosses, which allows the independent estimation of both additive and non-additive components of variance

Advancing frost dates have reduced frost risk among most North American angiosperms since 1980.

In recent decades, the final frost dates of winter have advanced throughout North America, and many angiosperm taxa have simultaneously advanced their flowering times as the climate has warmed. Phenological advancement may reduce plant fitness, as flowering prior to the final frost date of the winter/spring transition may damage flower buds or open flowers, limiting fruit and seed production. The risk of floral exposure to frost in the recent past and in the future, however, also depends on whether the last day of winter frost is advancing more rapidly, or less rapidly, than the date of onset of flowering in response to climate warming. This study presents the first continental-scale assessment of recent changes in frost risk to floral tissues, using digital records of 475,694 herbarium specimens representing 1,653 angiosperm species collected across North America from 1920 to 2015. For most species, among sites from which they have been collected, dates of last frost have advanced much more rapidly than flowering dates. As a result, frost risk has declined in 66% of sampled species. Moreover, exotic species consistently exhibit lower frost risk than native species, primarily because the former occupy warmer habitats where the annual frost-free period begins earlier. While reducing the probability of exposure to frost has clear benefits for the survival of flower buds and flowers, such phenological advancement may disrupt other ecological processes across North America, including pollination, herbivory, and disease transmission

Herbarium-Derived Phenological Data in North America

We present infrastructure for developing large-scale and long-term phenological datasets across multiple herbaria, as well as a sample dataset that has been acquired from the digital archives of 440 distinct herbaria across North America and further processed to evaluate phenological status. This dataset contains 2,319,672 specimen records of plants collected while reproductively active. These data have been modified to explicitly codify the observed phenological status of each specimen at the time of collection, and to remove specimens for which information essential to assessing their phenology or the corresponding climate conditions in the year and location of collection were missing. As different collectors have used distinct taxonomic schema over space and time in documenting the specimens being collected, these data were also rectified into a single unified taxonomic schema to ensure that consistent taxon names were used throughout the dataset. Further, this data has been united with long-term and annual climate conditions in the year and location of collection, as derived from PRISM climate data (https://www.prism.oregonstate.edu/). To date, this data includes 2,319,672 specimens across 25,429 plant taxa. However, this represents a living dataset that will continue to be updated as digitization efforts proceed and additional digital specimen records become available.Code was written in python 3.7. Multiple python packages are required to run these packages (see attached .yml file for full list). We recommend the usage of Anaconda for constructing the python environment and installing the python packages required to produce this dataset, including the PhenoColl package that was developed for this project (https://doi.org/10.5281/zenodo.8323153) Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DEB-2105932Phenological data pertaining to flowering times in this dataset consist of 2,319,672 specimen records of plant species collected in flower, while strobilating, or while fertile (this last category primarily applied to graminoids). These data were derived from the digital archives of 440 herbaria (see Readme for full listing), and subsequently cleaned and modified using several criteria described below to facilitate their use in phenological assessment. To ensure the quality of the data used in this study, specimens were included in the dataset analyzed here only if, at the time of digitization, herbarium personnel had: verified that the specimens were collected when in flower, strobilating, or fertile; recorded GPS coordinates of the location from which the specimen was collected; and provided the precise date of collection (including month, date, and year). Only those specimens that were explicitly recorded reproductive status within either the DarwinCore "reproductivecondition" or "lifestage" fields of their source's database were included in this study. The taxonomic nomenclature used to describe each specimen was standardized using the Taxonomic Name Resolution Service iPlant Collaborative, Version 4.0 (Boyle et al., 2013, Accessed: 30 August 2021; https://tnrs.biendata.org/). Duplicate collections of a species at the same location, DOY, year, and location were also removed. The resulting dataset included 2,319,672 specimens distributed throughout North America. Climate data associated with the year and location of each specimen collection was then integrated into this data. All climate data was drawn from PRISM climate data (https://www.prism.oregonstate.edu/) and incorporated both long-term normal conditions at the location of each collection as well as the predicted conditions in the year and location of each collection

Herbarium-Derived Phenological Data in North America

We present infrastructure for developing large-scale and long-term phenological datasets across multiple herbaria, as well as a sample dataset that has been acquired from the digital archives of 440 distinct herbaria across North America and further processed to evaluate phenological status. This dataset contains 2,319,672 specimen records of plants collected while reproductively active. These data have been modified to explicitly codify the observed phenological status of each specimen at the time of collection, and to remove specimens for which information essential to assessing their phenology or the corresponding climate conditions in the year and location of collection were missing. As different collectors have used distinct taxonomic schema over space and time in documenting the specimens being collected, these data were also rectified into a single unified taxonomic schema to ensure that consistent taxon names were used throughout the dataset. Further, this data has been united with long-term and annual climate conditions in the year and location of collection, as derived from PRISM climate data (https://www.prism.oregonstate.edu/). To date, this data includes 2,319,672 specimens across 25,429 plant taxa. However, this represents a living dataset that will continue to be updated as digitization efforts proceed and additional digital specimen records become available.Code was written in python 3.7. Multiple python packages are required to run these packages (see attached .yml file for full list). We recommend the usage of Anaconda for constructing the python environment and installing the python packages required to produce this dataset, including the PhenoColl package that was developed for this project (https://doi.org/10.5281/zenodo.8323153) Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DEB-2105932Phenological data pertaining to flowering times in this dataset consist of 2,319,672 specimen records of plant species collected in flower, while strobilating, or while fertile (this last category primarily applied to graminoids). These data were derived from the digital archives of 440 herbaria (see Readme for full listing), and subsequently cleaned and modified using several criteria described below to facilitate their use in phenological assessment. To ensure the quality of the data used in this study, specimens were included in the dataset analyzed here only if, at the time of digitization, herbarium personnel had: verified that the specimens were collected when in flower, strobilating, or fertile; recorded GPS coordinates of the location from which the specimen was collected; and provided the precise date of collection (including month, date, and year). Only those specimens that were explicitly recorded reproductive status within either the DarwinCore "reproductivecondition" or "lifestage" fields of their source's database were included in this study. The taxonomic nomenclature used to describe each specimen was standardized using the Taxonomic Name Resolution Service iPlant Collaborative, Version 4.0 (Boyle et al., 2013, Accessed: 30 August 2021; https://tnrs.biendata.org/). Duplicate collections of a species at the same location, DOY, year, and location were also removed. The resulting dataset included 2,319,672 specimens distributed throughout North America. Climate data associated with the year and location of each specimen collection was then integrated into this data. All climate data was drawn from PRISM climate data (https://www.prism.oregonstate.edu/) and incorporated both long-term normal conditions at the location of each collection as well as the predicted conditions in the year and location of each collection

Herbarium-Derived Phenological Data in North America

We present infrastructure for developing large-scale and long-term phenological datasets across multiple herbaria, as well as a sample dataset that has been acquired from the digital archives of 440 distinct herbaria across North America and further processed to evaluate phenological status. This dataset contains 2,319,672 specimen records of plants collected while reproductively active. These data have been modified to explicitly codify the observed phenological status of each specimen at the time of collection, and to remove specimens for which information essential to assessing their phenology or the corresponding climate conditions in the year and location of collection were missing. As different collectors have used distinct taxonomic schema over space and time in documenting the specimens being collected, these data were also rectified into a single unified taxonomic schema to ensure that consistent taxon names were used throughout the dataset. Further, this data has been united with long-term and annual climate conditions in the year and location of collection, as derived from PRISM climate data (https://www.prism.oregonstate.edu/). To date, this data includes 2,319,672 specimens across 25,429 plant taxa. However, this represents a living dataset that will continue to be updated as digitization efforts proceed and additional digital specimen records become available.Code was written in python 3.7. Multiple python packages are required to run these packages (see attached .yml file for full list). We recommend the usage of Anaconda for constructing the python environment and installing the python packages required to produce this dataset, including the PhenoColl package that was developed for this project (https://doi.org/10.5281/zenodo.8323153) Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DEB-2105932Phenological data pertaining to flowering times in this dataset consist of 2,319,672 specimen records of plant species collected in flower, while strobilating, or while fertile (this last category primarily applied to graminoids). These data were derived from the digital archives of 440 herbaria (see Readme for full listing), and subsequently cleaned and modified using several criteria described below to facilitate their use in phenological assessment. To ensure the quality of the data used in this study, specimens were included in the dataset analyzed here only if, at the time of digitization, herbarium personnel had: verified that the specimens were collected when in flower, strobilating, or fertile; recorded GPS coordinates of the location from which the specimen was collected; and provided the precise date of collection (including month, date, and year). Only those specimens that were explicitly recorded reproductive status within either the DarwinCore "reproductivecondition" or "lifestage" fields of their source's database were included in this study. The taxonomic nomenclature used to describe each specimen was standardized using the Taxonomic Name Resolution Service iPlant Collaborative, Version 4.0 (Boyle et al., 2013, Accessed: 30 August 2021; https://tnrs.biendata.org/). Duplicate collections of a species at the same location, DOY, year, and location were also removed. The resulting dataset included 2,319,672 specimens distributed throughout North America. Climate data associated with the year and location of each specimen collection was then integrated into this data. All climate data was drawn from PRISM climate data (https://www.prism.oregonstate.edu/) and incorporated both long-term normal conditions at the location of each collection as well as the predicted conditions in the year and location of each collection

Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America

Phenology varies widely over space and time because of its sensitivity to climate. However, whether phenological variation is primarily generated by rapid organismal responses (plasticity) or local adaptation remains unresolved. Here we used 1,038,027 herbarium specimens representing 1,605 species from the continental United States to measure flowering-time sensitivity to temperature over time (Stime) and space (Sspace). By comparing these estimates, we inferred how adaptation and plasticity historically influenced phenology along temperature gradients and how their contributions vary among species with different phenology and native climates and among ecoregions differing in species composition. Parameters Sspace and Stime were positively correlated (r = 0.87), of similar magnitude and more frequently consistent with plasticity than adaptation. Apparent plasticity and adaptation generated earlier flowering in spring, limited responsiveness in late summer and delayed flowering in autumn in response to temperature increases. Nonetheless, ecoregions differed in the relative contributions of adaptation and plasticity, from consistently greater importance of plasticity (for example, southeastern United States plains) to their nearly equal importance throughout the season (for example, Western Sierra Madre Piedmont). Our results support the hypothesis that plasticity is the primary driver of flowering-time variation along temperature gradients, with local adaptation having a widespread but comparatively limited role