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Bioactive composition analysisThe main bioactive components in the three products are listed in Table 1. The main chemical constitutes of DL and LT were quite similar; although significant differences were noted in indicators such as protein (DL  > LT, difference = 8.22, P  DL, difference = 1.79, P  LT, difference = 4.07, P  0.05) of the samples. Among the bioactive constitutes, only POL contents in LT and DL were significantly lower (P  DR (1.90%). Compared with DL and LT, the DR exhibited significantly higher POL, increased by 50.21% (P  More

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In addition to the raw data, we provide two means of exploring the Avian Diet Database and extracting species- or prey-specific summaries. The first is through the website https://aviandiet.unc.edu where users can enter a bird species name to explore a summary of diet information known for that species, or a prey name to explore which bird species are known to eat that prey taxon. We also provide an R package (‘aviandietdb’) for exploring the database, which should be loaded in R by typing:
install.packages(“devtools”)

library(devtools)

devtools::install_github(“ahhurlbert/aviandietdb”)

library(aviandietdb)
Three useful R functions for summarizing records in the database are detailed below.dbSummary().Example usage:
dbSummary()
This function returns the total number of database records, the unique number of bird species, and the unique number of publications summarized in the Diet Database. In addition, it provides a tally of the number of records by bird species listed in alphabetical order, as well as a summary for each bird family in the American Birding Association (ABA) Checklist (version 8.0.6a) of 1) the number of species in the family in the database, 2) the total number of species in the family based on the ABA checklist, and 3) the percent of the family represented based on the species expected in North America. This information on taxonomic coverage is also provided in Online-only Table 2.speciesSummary().Example usage:
speciesSummary(“Bald Eagle”, by = ”Order”)
This function provides a summary of the total number of records and total number of studies available in the database for this species, along with a summary of how those records are distributed across seasons, years, and geographic regions. The number of records are also summarized by taxonomic level to which prey were identified and by analysis type (by number of items, weight or volume, occurrence, or unspecified). Finally, for each analysis type, the mean fraction of diet is given for each prey category at the hierarchical taxonomic level specified with the “by” argument. This is an overall mean, averaged across year, region, and season. If the original data source indicated that specific parts of the prey taxon were consumed (e.g. fruit, seed, vegetation, etc.) then they are listed in the Prey_Part field.dietSummary().Example usage:
dietSummary(“Bald Eagle”, season = ”summer”, region = ”California”, yearRange = c(1940, 1970), by = ”Order”, dietType = ”Items”)
This function allows one to specify season, region, a year range, analysis type, and taxonomic level for prey summarization, and then provides the mean fraction of diet information based on all studies meeting the stated criteria.dietSummaryByPrey().Example usage:
dietSummaryByPrey(“Lepidoptera”, preyLevel = ”Order”, dietType = ”Items”, yearRange = c(1985, 2000), season = ”summer”, preyStage = ”larva”, speciesMean = TRUE)
This function provides a list of all bird species that consume a particular prey taxon in decreasing order of importance. In addition to providing the prey taxon name, you must also specify the taxonomic level (preyLevel) of that name. Like dietSummary(), this function allows one to specify season, region, a year range, and analysis type. There are two additional arguments not present in dietSummary(). One is preyStage, which specifies the life stage of the prey item (if applicable) for which a summary should be conducted. By default (‘any’), diet records will be included regardless of prey stage. Alternatively, one can specify that the summary should only be conducted for records including the terms ‘larva’, ‘adult’, or ‘pupa’ in the Diet Database’s ‘Prey_Stage’ field. This is most relevant for Lepidoptera and a few other insect groups, where one might want to single out the importance of caterpillars or other larvae, for example.By specifying speciesMean = TRUE, only a single value is returned for each bird species that is known to consume a specified prey taxon which represents the average across all analyses meeting the season, region, and year criteria. If speciesMean is FALSE, then each analysis of a bird species which meets the specified criteria will be listed separately.Example code and output is available in the Github README.md document (https://github.com/ahhurlbert/aviandietdb). More