This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 166 data sources, representing a total of 1676 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site. The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. all references to the original data sources can be found in the pdf with references, and a Google Earth file (kml) file presents the locations (including metadata) of all datasets. When using (parts of) this data set, please respect the original open access licenses. This data set underlies all analyses performed in the paper 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes, and is accompanied by a data paper entitled 'InsectChange – a global database of temporal changes in insect and arachnid assemblages'. Consulting the data paper before use is recommended. Tables that can be used to calculate trends of specific taxa and for species richness will be added as they become available. The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research. In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References_to_original_data_sources.pdf'. In the table 'PlotData' more details on each site within each dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status. The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here, but more details are provided in the data paper. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details. The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add. Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses. Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper. WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).

Statistically robust monitoring of threatened populations is essential for effective conservation management because the population trend data that monitoring generates is often used to make decisions about when and how to take action. Despite representing the highest proportion of threatened animals globally, the development of best practice methods for monitoring populations of threatened insects is relatively uncommon. Traditionally, population trend data for the Nationally Endangered New Zealand grasshopper Brachaspis robustus has been determined by counting all adults and nymphs seen on a single ~1.5 km transect searched once annually. This method lacks spatial and temporal replication, both of which are essential to overcome detection errors in highly cryptic species like B. robustus. It also provides no information about changes in the grasshopper's distribution throughout its range. Here, we design and test new population density and site occupancy monitoring protocols by comparing a) comprehensive plot and transect searches at one site and b) transect searches at two sites representing two different habitats (gravel road and natural riverbed) occupied by the species across its remaining range. Using power analyses, we determined a) the number of transects, b) the number of repeated visits and c) the grasshopper demographic to count to accurately detect long term change in relative population density. To inform a monitoring protocol design to track trends in grasshopper distribution, we estimated the probability of detecting an individual with respect to a) search area, b) weather and c) the grasshopper demographic counted at each of the two sites. Density estimates from plots and transects did not differ significantly. Population density monitoring was found to be most informative when large adult females present in early summer were used to index population size. To detect a significant change in relative density with power > 0.8 at the gravel road habitat, at least seventeen spatial replicates (transects) and four temporal replicates (visits) were required. Density estimates at the natural braided river site performed poorly and likely require a much higher survey effort. Detection of grasshopper presence was highest (p_g > 0.6) using a 100 m x 1 m transect at both sites in February under optimal (no cloud) conditions. At least three visits to a transect should be conducted per season for distribution monitoring. Monitoring protocols that inform the management of threatened species are crucial for better understanding and mitigation of the current global trends of insect decline. This study provides an exemplar of how appropriate monitoring protocols can be developed for threatened insect species.

This repository contains data from “The effect of insect food availability on songbird reproductive success and chick body condition: evidence from a systematic review and meta-analysis” by Eliza M. Grames1,2, Graham A. Montgomery3, Casey Youngflesh3,4, Morgan W. Tingley3, and Chris S. Elphick1

University of Connecticut, Department of Ecology and Evolutionary Biology, Storrs, CT, USA University of Nevada Reno, Department of Biology, Reno, NV, USA University of California Los Angeles, Department of Ecology and Evolutionary Biology, Los Angeles, CA, USA Michigan State University, Ecology, Evolution, and Behavior Program, East Lansing, MI, USA

Abstract: Reports of declines in abundance and biomass of insects and other invertebrates from around the world have raised concerns about food limitation that could have profound impacts for insectivorous species. Food availability can clearly affect species; however, there is considerable variation among studies in whether this effect is evident, and thus a lack of clarity over the generality of the relationship. To understand how decreased food availability due to invertebrate declines will affect bird populations, we conducted a systematic review and used meta-analytic structural equation modeling, which allowed us to treat our core variables of interest as latent variables estimated by the diverse ways in which researchers measure fecundity and chick body condition. We found a moderate positive effect of food availability on chick body condition and a strong positive effect on reproductive success. We also found a negative relationship between chick body condition and reproductive success. Our results demonstrate that food is generally a limiting factor for breeding songbirds. Our analysis also provides evidence for a consistent trade-off between chick body condition and reproductive success, demonstrating the complexity of trophic dynamics important for these vital rates.

File List README.txt A plain text file containing information about the data files and variables they contain.

effect-calculation.R R script to calculate effect sizes for studies included in the meta-analysis based on the extracted statistics and data contained in raw_effect_data.xlsx and multiple-effects.csv

study-characteristics-and-meta-analysis.R R script to reproduce the analyses and figures from the manuscript including the descriptive characteristics of studies, meta-regressions, and meta-analytic structural equation model. Script imports study_characteristics.xlsx, effect_sizes.csv, effect-measurements.csv, output.nex, and BirdFuncDat.txt from https://doi.org/10.6084/m9.figshare.3559887.v1

effect-measurements.csv Data file containing the list of ways in which researchers described bird body condition or reproductive success and the ways in which we reclassified those measures to more standardized measures

effect_sizes.csv Data file containing the calculated effect sizes from effect-calculation.R merged with study metadata

habitat_groups.csv Data file containing IUCN habitat groupings for the habitat types represented by studies included in the meta-analysis; used for plotting habitat types in a sensible order

multiple-effects.csv Data file indicating which effect should be used for studies where the same effect could be calculated different ways from the statistics and data reported in the original paper and effect sizes differed; the most reliable or unambiguous measure from each paper is selected

output.nex Phylogenetic tree output from VertLife for birds used as the backbone for plotting species phylogenetically

raw_effect_data.csv Data file containing test and summary statistics extracted from studies included in the meta-analysis; used to calculate effect sizes with effect-calculation.R

study_characteristics.xlsx Data file containing characteristics of studies included in the meta-analysis: citation, publication type, years of data collection, approximate latitude and longitude, study location, habitat classification and type, and focal species family, common name, and scientific name

Extending Anthophila research through image and trait digitization (Big-Bee) indexed biotic interactions and review summary. Declining populations of bees impact plant-pollinator interactions in both natural and agricultural systems. While bees and other insects pollinate most wild plants and are critical to sustaining a large proportion of global food production, they are decreasing in both numbers and diversity. Our understanding of the factors driving these declines is limited because we lack sufficient data on the distribution of bee species, and on the behavioral and anatomical traits that may make them either vulnerable or resilient to human-induced environmental changes, such as habitat loss and climate change. Fortunately, wild bees have been collected by researchers and deposited in natural history collections for over 100 years, retaining a wealth of associated attributes that can be extracted from specimen images. This project will digitally capture data and images from these historic specimens, develop tools to measure bee traits from these images and generate a comprehensive bee trait and image dataset to measure changes through time. This will increase our understanding of specific traits that put bee species at risk of decline - a critical need for both sustaining our agricultural economy and the conservation of our natural resources. In addition, the large image datasets created by this project can be used for new artificial intelligence identification tools that will help improve our future pollinator observation and monitoring efforts. The Big-Bee project began in 2021 and is funded by the National Science Foundation to mobilize data about worldwide bee species to data aggregators (e.g., iDigBio, GBIF). The Big-Bee Thematic Collection Network (Big-Bee) will create over one million high-resolution 2D and 3D images of bee specimens, representing over 5,000 worldwide bee species, including all of the major pollinating species of the United States. The Big-Bee network includes 13 institutions and partnerships with US government agencies. Novel mechanisms for sharing image datasets will be developed and datasets of bee traits will be available through an open data portal, the Bee Library, for research and education. The Big-Bee project will engage the general public in research through community science via crowdsourcing trait measurements and data transcription from images. In addition, training and professional development for natural history collection staff, researchers, and university students in data science will be provided through the creation and implementation of workshops focusing on bee traits and species identification. All data resulting from this award will be shared with and publicly available through the national digitized biocollections resource, iDigBio.org. This is the first archive of Big-Bee data indexed by Global Biotic Interactions (GloBI). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open-source software. This version of the Big Bee dataset includes interactions that are not just bees. Also in this version, the datasets included in this publication are specifically those institutions in the Big Bee project network and do not represent all bee interaction data found at Global Biotic Interactions. Bee Library Information - Statistics about Big Bee data providers The specimens indexed by GloBI are also found in the Bee Library. To date, the number of specimens and images in the library are listed below. The Bee Library taxonomic backbone is not yet complete, so information regarding the number of species is not yet available. Further summary statistics are available in the Big Bee Metrics from the Bee Library and GloBI - July 27 2022.pdf file. From Bee Library (partner indexed records) 1,186,629 occurrence records 969,695 (82%) georeferenced 328,742 (28%) occurrences imaged 628,198 (53%) identified to species 9 families 324 genera 5,694 species 5,955 total taxa (including subsp. and var.) Collection Occurrences Georeferenced Imaged Total Taxa Interactions indexed in GloBI (bees) ASU Hasbrouck Insect Collection - Bee Records 11561 11560 1002 213 3830 Bee Biology and Systematics Laboratory, USDA-ARS Pollinating Insect-Biology, Management, Systematics Research 561820 547461 0 4698 N/A California Academy of Sciences 805 254 0 5 99 California Academy of Sciences - Type Collection 1838 52 77 1433 N/A Essig Museum of Entomology, University of California Berkeley 58543 55018 0 535 0 Florida State Collection of Arthropods 8798 8761 8252 280 0 Museum of Comparative Zoology, Harvard University 10040 9953 835 11 83 Natural History Museum of Los Angeles County 9812 4796 2546 345 0 San Diego Natural History Museum Entomology Department 2490 1616 278 173 78 University of California Santa Barbara Invertebrate Zoology Collection 8339 8103 2254 135 539...