DOI: 10.5061/dryad.37pvmcvmp
title: Data from: Phenological mismatches and the demography of solitary bees

description: Species respond idiosyncratically to environmental variation, which may
 generate phenological mismatches. We assess the consequences of such
 mismatches for solitary bees. During nine years, we studied flowering
 phenology and nesting phenology, and demography of five wood-nesting
 solitary bee species representing a broad gradient of
 specialization/generalization in the use of floral resources. We found
 that the reproductive performance and population growth rate of bees
 tended to be lower with increasing nesting–flowering mismatches, except
 for the most generalized bee species. Our findings help elucidate the role
 of phenological mismatches for the demography of wild pollinators, which
 perform key ecosystem functions and provide important services for
 humanity. Furthermore, if climate change increases phenological mismatches
 in this system, we expect negative consequences of climate change for
 specialist bees.
description: <strong>Methods</strong>
 <strong>Study area and sites</strong>
 The study was conducted in ttwo 2-ha sites located at ca. 1250 m
 above sea level in Villavicencio Nature Reserve and its vicinity (32°
 32' S, 68° 57' W), Las Heras Department, Mendoza Province,
 Argentina. Each site consisted of a 100 m × 200 m plot. Because data come
 from several projects running consecutively at the same sites, there are
 minor differences in the sampling methods used in different
 years. <strong>Flower
 abundance</strong> Our estimate of flowering
 phenology of the plant species present in our study sites is based on
 weekly estimates of the floral density in fixed plots and transects,
 conducted during the flowering season of the majority of plant species
 (September–December). During the flowering season, floral density was
 measured weekly at fixed quadrats/transects: forty regularly spaced 2 m ×
 2 m quadrats in 2006, five fixed 50 m × 2 m transect belts in 2007, and
 four 8 m × 20 m plots plus two 2 m × 50 m transects in the remaining years
 (2008–2014). Flower density was estimated multiplying the mean number of
 flowers per individual by the total number of flowering individuals in the
 transect or plot when individuals could be distinguished (shrubs and some
 herbs); we estimated the number of flowers per individual in at least ten
 individuals of the site. When it was not possible to identify flowering
 individuals (some herbaceous species), all flowers in a plot or transect
 were counted. We included in the study all flowering plant species that
 were assumed to be animal pollinated (excluding only grass
 species). <strong>Bee nesting, bee reproductive
 success, and nest pollen identification</strong>
 We used wooden trap nests to study bee nesting phenology and
 reproductive success. Each trap nest consisted of two wood pieces bound
 together with paper tape with a longitudinal hole drilled in between them.
 We used this design so that we could open the nests regularly to examine
 their contents without damaging the brood cells. We used trap-nests of 5
 mm in internal diameter and 14 cm in tunnel length, 8 mm diameter and 14
 cm length, and of 11 mm diameter and 28 cm length; the latter diameter was
 not used during the first study year. These trap nests were placed in the
 field in bundles with eight trap-nests of each diameter (24 total) at
 multiple sampling points in each study site. Trap bundles were attached to
 shrubs in the first two study years, and to metal poles in the remaining
 years. In each site, we placed trap nests at 30 points separated at least
 by 20 m from each other (2006 and 2007) or at twelve paired points
 separated 100 m from each other (2008–2014). Although the study design
 varied slightly between the first two and the remaining seven study years,
 the number of trap nests of each size per site was similar among years,
 and so this variation is unlikely to affect our estimate of the number of
 brood cell per nest, the main reproductive variable used in our
 study. Bees included in this study belonged to four
 genera and five species: the carder bee Anthidium vigintipunctatum; the
 petal-cutting bees Megachile leucographa and M. nigella (identified as M.
 ctenophora in previous publications in the same study area); the
 wood-borer bee Trichothurgus laticeps; and the carpenter bee Xylocopa
 atamisquensis. Other bee species also nested in our trap-nests, but their
 abundance was too low to allow conducting the study at the broad temporal
 and spatial scales reported here. Trap nests were
 checked weekly during the bee breeding season. Once the construction of a
 nest had ended, the occupied traps were removed from the trap bundles and
 replaced by empty traps. During the first three study years, we took to
 the lab all traps with nests for rearing and identification purposes,
 while during the remaining years we took to the lab only traps for which
 identification was not possible in the field; other nests were removed
 from the bundles but left in the field, a few meters away from the
 original nesting site. For the nests taken to the laboratory, traps were
 opened to record the number of cells; whenever the nest had more than one
 brood cell, during the first three study years one cell was extracted for
 pollen identification, and the remaining cells were kept until adult
 emergence. Taxonomic identification of pollen was conducted by light
 microscopy, in comparison with a reference collection. The latter included
 all flowering plant species recorded in our study plots. For taxonomic
 identification of pollen grains we used the natural method, comparing with
 a reference collection that included all flowering plant species occurring
 in our sites. We kept some emerged adults of each nest for identification
 and returned the remaining individuals to their site of origin in the
 field. After the first three study years (i.e., from 2009 onwards),
 whenever possible, we identified the bee species in the field without
 taking the nests to the laboratory. The number of brood cells and their
 taxonomic identity were recorded in all nests. 
 <strong>Estimation of bee
 generalization</strong> Bee generalization was
 defined as Shannon’s diversity index of the pollen found in all nests of
 each species in the three years in which pollen studies were conducted
 (2006–2008). To calculate Shannon’s diversity index we started from the
 number of pollen grains of each plant species in each nest. To this end,
 pollen from each extracted brood cell was suspended in an ethanol-water
 solution, from which three samples were taken for pollen quantification
 under a microscope. Fifteen microscope fields were counted for each
 sample, and 45 fields total for each brood cell. With these data we
 calculated the proportion of pollen grains of each plant species in each
 nest, which we used as a standardized measure of plant abundance per nest,
 so as to remove the effect of the total number of pollen grains counted
 per nest. We then summed the standardized abundance of each plant species
 across all nests as a measure of the abundance of that plant species in
 the population of a bee species. This is the abundance we used to
 calculate Shannon’s diversity index.
 <strong>Estimation of phenologies and nesting–flowering
 phenological mismatch</strong> We used the weekly
 data on bee nest and flower abundance for the pooled data for the two
 study sites. At the level of individual nests, we estimated phenology
 based on the end date of nest construction, which was the most accurate
 date we could record in our weekly visits to the sites for three of our
 study species (A. vigintipunctatum, the two Megachile species), as females
 usually build the nests in less than a week. T. laticeps appears to take
 slightly longer than a week, as we sometimes found nests under
 construction, while X. atamisquensis takes substantially longer (several
 weeks), but we decided to use also the end date of nest construction for
 these species to make data more comparable across species. At the
 population level, we estimated phenology using the abundance-weighted
 arithmetic mean date (WMD) of nesting or flowering, respectively,
 calculated with function weighted.mean of the stats package of R
 statistical software [34]. WMD is considered one of the best phenological
 estimators and has been widely used in previous studies. Dates were
 represented as the number of days from July 1 each year, and the weighted
 average of flowering date was calculated by multiplying each date by the
 relative abundance of flowers in that date. For example, if flower
 abundance was 10, 60, and 30 flowers on days 60, 67, and 74 from July 1,
 the WMD of flowering is calculated as 0.1 × 60 + 0.6 × 67 + 0.3 × 74 =
 68.4. We used the above phenological estimates to
 estimate nesting–flowering phenological mismatches. Phenological mismatch
 was calculated as the absolute difference between the end date of the nest
 construction and the flowering WMD of the main floral resource species
 (those representing 85% of the pollen found in nests of of a particular
 bee species). For the population-level analysis, we calculated the number
 of days between the nesting WMD and the flowering WMD to estimate the
 nesting–flowering phenological mismatch for each bee species and its main
 floral resource species. Because we needed to record at least one nest to
 estimate the nesting–flowering phenological mismatch for a given year, we
 excluded from the analyses years in which no nests of a particular species
 were recorded. Under the above definition of phenological mismatches it is
 reasonable to expect mismatches both for specialist and generalist bee
 species, as in both cases the nesting and flowering dates are likely to
 shift among years. <strong>Estimation of bee
 reproductive success</strong> Reproductive
 success for individual nests was calculated as the number of brood cells
 per nest, whereas at the population level, reproductive success was
 calculated as the average number of brood cells per nest; number of brood
 cells per nest is commonly used as a measure of reproductive success of
 female solitary bees. One species, <em>Trichothurgus
 laticeps</em>, lacks organized brood cells, as females lay bare eggs
 amidst a pollen mass. In turn, in some nests of <em>Xylocopa
 atamisquensis</em> adults emerged in the field before we could count
 the number of brood cells, and although some nests bore clear marks of the
 former brood cell divisions, in other nests the marks were less clear.
 Thus, for these species we used the length of the trap cavity occupied by
 pollen to estimate of the number of brood cells in nests where a direct
 count was not possible. To this end, we first used a subset of nests for
 which we could count the number of pupal cocoons (<em>T.
 laticeps</em>) or brood cells (<em>X.
 atamisquensis</em>) to fit a linear regression model between number
 of brood cells or pupal cocoons and length of the cavity occupied by
 pollen (<em>T. laticeps</em>) or formerly occupied by brood
 cells (<em>X. atamisquensis</em>), and then used this
 relationship to estimate the number of brood cells in all nests from the
 length of the cavity occupied by pollen (see Supplementary Methods for
 further information on the calculations).
description: # Data from: Phenological mismatches and the demography of solitary bees
 The dataset includes a series of data files with flower abundance, trap
 nests, insect adult emergence. A series of R codes accompany the data
 files to process the raw data to produce the data tables that we have used
 for analyses. We describe below the contents of each class of data files.
 ## Description of the Data and file structure ### Flower abundance The
 flower abundance data are stored in the file `plants.csv`. The R script
 `plant_data.r` reads this file and makes a few additional calculations on
 these data. The file `plants.csv` contains the following six columns: *
 **sitio:** The study site code. * **Fecha:** The date in which the data
 were collected. * **fecha.rel:** The relative date in days, i.e., the
 number of days in each study years begining on July 1 (e.g., July 15 would
 be day 15, whereas August 1 would be day 32). * **ano:** The study year,
 defined as the year in which the flowering season started. So, for
 example, `ano=2007` includes all data collected between the sampling in
 the flowering season 2006 started in October 2006 and ended in January
 2007. * **no.esp.p:** The plant species code. This is a five character
 code that identifies each plant species in our database. This code allows
 linking across different data tables. * **flores.tot:** Total number of
 flowers recorded at a particular site and date combination. ### Trap nests
 The file `trampas.csv` contains the data on the nests found in the trap
 nests. Another file named `emerg.csv` includes information on the
 individual bees and nest parasites emerged from each nest. The
 `trampas.csv` file contains the following columns: * **sitio:** The study
 site code. * **punto:** A code that identifies each pole holding a bundle
 of trap nests. * **id.trampa:** A unique code identifying each bee nest.
 This code allows combining different tables with bee nest information. *
 **codigo.i:** A code identifying each bee species. * **diam.aguj:** The
 hole diameter of each trap nest, in milimeters. * **largo.libre.mm:** The
 space in the trap nest not occupied by brood cells, in milimeters. *
 **fecha.campo:** The date in which the finished nest was recorded in the
 field. * **fecha.campo.rel:** The date in which the finished nest was
 recorded in the field, relative to July 1 each year. * **fecha.apert:**
 The date in which the nest was first open to record brood cell
 information. * **no.celd:** Number of brood cells in the nest. - **ano:**
 The study year. - **elevation:** The elevation (meters above sea level) of
 the study site. In turn, the `emerg.csv` file contains the following
 columns: * **no.esp.i:** A code identifying each insect species emerging
 from a nest. * **sitio:** The study site code. * **id.trampa:** A unique
 code identifying each bee nest. This code allows combining different
 tables with bee nest information. * **fecha.emerg:** The date in which the
 emergence of an individual insect was recorded. * **fecha.emerg.rel:** The
 date in which the emergence of an individual insect was recorded, relative
 to July 1 of each year. * **ano:** The study year. * **codigo.i:** A code
 identifying each bee species. ### Nest pollen The files
 `pollen_megach_2006.csv`, `pollen_megach_2007.csv`, and
 `pollen_megach_2008.csv` contain the data on nest pollen for the five bee
 species for years 2006-2008. The files contain the following columns: *
 **sitio:** The study site code. * **id.trampa:** A unique code identifying
 each bee nest. This code allows combining different tables with bee nest
 information. * **fecha:** Date in which pollen sample was collected from
 nest. * **posicion.celdilla:** The position of the brood cell from which
 pollen was extracted for analysis (1 is the first brood cell built by the
 female, larger numbers are progressively closer to the nest entrance). *
 **no.celd.tot:** Total number of brood cells in nest. * **alicuota:**
 Pollen subsample for a nest. * **campo:** Microscope field in which a
 given pollen count was made. * **n.granos:** Number of pollen grains
 counted in a particular microscope field (campo). ## Calculation of number
 of brood cells for *Trichothurgus laticeps* and *Xylocopa atamisquensis*
 For two bee species, *Trichothurgus laticeps* and *Xylocopa
 atamisquensis*, we lacked precise records of the number of brood cells
 original built by the female building the nest (the mother). *T.
 laticeps*, lacks organized brood cells, as females lay bare eggs amidst a
 pollen mass. In turn, in some nests of *Xylocopa atamisquensis* adults
 emerged in the field before we could count the number of brood cells, and
 although some nests bore clear marks of the former brood cell divisions,
 in other nests the marks were less clear. Thus, for these species we used
 the length of the trap cavity occupied by pollen to estimate of the number
 of brood cells in nests where a direct count was not possible. To this
 end, we first used a subset of nests for which we could count the number
 of pupal cocoons (*T. laticeps*) or brood cells (*X. atamisquensis*) to
 fit a linear regression model between number of brood cells or pupal
 cocoons and length of the cavity occupied by pollen (*T. laticeps*) or
 formerly occupied by brood cells (*X. atamisquensis*), and then used this
 relationship to estimate the number of brood cells in all nests from the
 length of the cavity occupied by pollen (see Supplementary Methods for
 further information on the calculations). The R scripts
 `lm.largo.ocup-no.celd.tri.lat.r` and `lm.largo.ocup-no.celd.xyl.ata.r` do
 these calculations. ## Calculation of bee generalization We used pollen
 records in bee nests from the first three study years to estimate bee
 generalization in pollen use. The pollen data are stored in three files,
 `polen_megach_2006.csv`, `polen_megach_2007.csv`, and
 `polen_megach_2008.csv`. We then used an R code for each bee species to
 calculate generalization in pollen use:
 `prop.pollen.ant.vig.r`, `prop.pollen.meg.a.r`, `prop.pollen.meg.cte.r`, `prop.pollen.tri.lat.r`, and `prop.pollen.xyl.ata.r`. The results of these calculations are stored in the file `specialization_vs_r.csv`, which contains several measures of pollen diversity in the nests, plus a number of statistics relating pollen generalization with the slopes of the relationship between phenological mismatch and reproductive success for each bee species. ## Species catalogs The species identity information is stored in two data files, one for plants (`plantas_catalogo.csv`) and one for insects (`insectos_catalogo_resumido.csv`). The plant catalog has the following columns: * **no.herbario**: A number for the herbarium specimen. * **familia**, **genero**, **especie**: The taxonomic family, genus, and species to which a plant species belongs, respectively. * **no.esp.p:** An alphanumeric plant species code. * **codigo.p:** A six-letter code identifying each plant species. * **autoridad:** The authority who described a plant species. * **especialista:** The specialist/researcher who identified a plant specimen. The insect catalog has the following columns: * **codigo.i:** A code identifying each bee species. * **no.esp.i:** A code identifying each insect species emerging from a nest. ## Analysis code In addition to the scripts mentioned above, the following R scripts were used for analyses and are included as part of the Dryad dataset: **mismatch_phenol_indiv_nests.r:** This script runs nest-level analyses and draws a figure with the results (Fig. 1 in the main manuscript). **mismatch_phenol_mean_cells.r:** This script runs population-level analyses and draws a figure with the results (Fig. 2 in the main manuscript). **mismatch_phenol_lambda.r:** This script calculates the population growth rate (lambda) and draws a figure with the results (Fig. 3 in the main manuscript). **mismatch_phenol_lambda_bootstrap.r:** This script is used by the script `mismatch_phenol_lambda.r` to calculate the bootstrap confidence interval of the population growth rate (lambda). **emerg_trampas_phenology.r:** This script calculates phenological statistics used for analyses in `mismatch_phenol_indiv_nests.r` and `mismatch_phenol_mean_cells.r`. **emerg_trampas_phenology_bootstrap.r:** This script calculates phenological statistics used for analyses using bootstrapped data. It is used by the script `mismatch_phenol_lambda_bootstrap.r`. **list2matr.R:** Ad hoc function to convert data from list format to matrix format, used for calculations of nest pollen data to estimate bee generalization in pollen use. For the scripts to run and access the data properly, scripts should be located in a subdirectory (subfolder) called `R` within your working directory (which can be defined in R with function `setwd`). Data should be located in another subdirectory called `data` within your working directory, and there should be another subdirectory called `figs` to save figures. In other words, your working directory should have three subdirectories: `data`, `figs`, and `R`.