DOI: 10.5061/dryad.v41ns1rxv
title: Climate warming changes synchrony of plants and pollinators

description: Climate warming changes the phenology of many species. When interacting
 organisms respond differently, climate change may disrupt their
 interactions and affect the stability of ecosystems. Here, we used GBIF
 occurrence records to examine phenology trends in plants and their
 associated insect pollinators in Germany since the 1980s. We found strong
 phenological advances in plants, but differences in the extent of shifts
 among pollinator groups. The temporal trends in plant and insect
 phenologies were generally associated with interannual temperature
 variation, and thus likely driven by climate change. When examining the
 synchrony of species-level plant-pollinator interactions, their temporal
 trends differed among pollinator groups. Overall, plant-pollinator
 interactions become more synchronized, mainly because the phenology of
 plants, which historically lagged behind that of the pollinators,
 responded more strongly to climate change. However, if the observed trends
 continue, many interactions may become more asynchronous again in the
 future. Our study suggests that climate change affects the phenologies of
 both plants and insects, and that it also influences the synchrony of
 plant-pollinator interactions.
description: The given datasets contain
 estimated species phenology shifts for time and temperature. The way the
 slopes were estimated is given below. We worked with occurrence records of plants and
 insects available from the GBIF database (GBIF.org 2021c, 2021a, 2021b,
 2021d). For the plants, we
 restricted ourselves to species covered by the BiolFlor database of plant
 traits (Klotz et al. 2002), because we originally intended to classify
 plants by their level of pollinator dependence – an idea we later
 abandoned. For the insects we restricted ourselves to beetles
 (Coleoptera), flies (Diptera), bees (Hymenoptera) as well as butterflies
 and moths (Lepidoptera), as these groups contain most insect pollinators
 (Kevan and Baker 1983). We used the R package
 <em>rgbif</em>  (Chamberlain and Boettiger
 2017) to download all available records of the above taxa
 from GBIF. Our basic criteria for including records were that they
 originated from Germany, and that their basis of record (as defined in
 GBIF) was either a living specimen (e.g., a captured insect), a human
 observation (i.e., an observation of a species made without collecting
 it), just an observation (i.e., when the exact type of observation was not
 clear), or a preserved specimen (e.g., an herbarium record or a collected
 specimen). If names of plant species were not accepted names, we used the
 R package <em>taxsize</em> (Chamberlain and Szöcs
 2013) to check the names against the GBIF backbone taxonomy
 and determine the actual accepted name. Prior to
 the data analyses, we subjected the data to several steps of quality
 control. First, we removed all records from before 1980 as these turned out to be too inconsistent,
 with few records per year and large gaps due to
 consecutive years
 without records. We also removed the records from 2021
 as the year had not been complete at the time of our analysis. Second, we
 removed all records from the days of year (DOY)
 1, 95, 121, 163, 164, 166
 and 181, and in particular DOY
 365 and 366 from the
 National Museum of Natural History in Luxembourg because the high
 numbers of records on these
 days indicated that either
 records without collecting date had been assigned
 these
 by default, or the dates were used by BioBlitz events where
 very large numbers of records are taken on a specific
 day of the year. Including these data would have strongly biased the
 intra-annual distributions of our records. Finally, we removed
 some records for which no elevation or temperature
 data could be obtained (see below). To
 ensure reasonable coverage of the studied
 time interval, we then
 restricted the records to species which had at least 10 occurrence records
 in every decade covered (with the year 2020 included in the last decade).
 After these data
 curation steps, we
 maintained just above 12 million occurrence records that covered
 altogether 1,764 species,
 with 11.4 million records of 1,438
 plant species, around
 590,000 records of 207 species of butterflies and
 moths, some 76,000 records of 20 bee
 species and
 30,000 records of 22 fly
 species, and
 almost 25,000 records of 77 species of beetles
 (<strong>Table
 1</strong>). There were large differences between plants
 and insects not only in the numbers of records but also in their temporal
 distribution across the studied period (<strong>Figure S
 1</strong>). While plants had relatively even record numbers across
 years, the insect groups, in particular flies and bees, were strongly
 underrepresented in the earlier decades, and record numbers increased
 rapidly in the last 20 years, probably due to the advent of platforms like
 iNaturalist.org and naturgucker.de, which allow recording
 of species occurrences by
 citizen naturalists, and which made
 up most of the insect
 occurrence data for Germany in GBIF.   Temperature and elevation data, and individual
 interactions Besides
 the main phenology data from GBIF records, we obtained several other data
 sets required for our analyses. To test for associations with climate, we
 used temperature data from the Climate Research Unit (CRU,
 https://crudata.uea.ac.uk) at the University of East Anglia, specifically the
 Time-Series dataset version 4.05 (University of East Anglia Climatic
 Research Unit et al. 2021), which contains gridded temperature data
 from 1901-2020
 at a resolution of 0.5°
 longitude by 0.5° latitude. From this dataset we extracted
 the monthly mean temperatures and averaged them to
 obtain the annual mean temperatures at the sites of occurrence records. To
 be able to control for elevation at the locations of occurrence records,
 we used elevation data
 at a 90 m resolution from
 the NASA Shuttle Radar
 Topography Mission (SRTM), obtained from the SRTM 90m DEM Digital Elevation Database
 (Jarvis et al. 2021) and accessed through the <em>raster</em> package
 (Hijmans
 2020) in R. <br>
 Data
 analysis All data wrangling and analysis was done in R (R
 Core Team 2008). Before analysing phenology data, we examined patterns of
 climate change in Germany through a linear model that regressed the annual
 mean temperature values at the collection sites (= the corresponding 0.5°
 × 0.5° grid cells) over time.
 To understand phenology changes in plants
 and insects, we first estimated the phenological
 shifts in each taxonomic group (i.e., plants, beetles, flies, bees, and
 butterflies/moths) as the slopes of linear regressions linking activity, i.e., the DOY of
 a record to its year of observation. We estimated
 taxonomic group-specific phenological shifts in two linear mixed-effect models:
 one that
 estimated shifts over time
 and one that related phenology variation to
 temperature. Both models
 included the DOY of a record as the response variable,
 and the latitude,
 longitude, and elevation of a record as fixed effects.
 The
 temporal-change model
 additionally included the year of a record as a fixed effect and as
 a random effect across
 species; the temperature-change
 model
 instead included
 the annual
 mean temperature at the
 site of a record as a fixed effect and as a random effect across species.
 We used the <em>lme4 </em>package (Bates et
 al. 2015) in R to fit these models,
 and assessed model
 fits by visually inspecting the
 relationships between
 residuals
 and fitted values, and
 between residuals and
 covariates (<strong>Supplementary diagnostic
 plots</strong>). As the random effects from the models agreed well
 enough with more complex generalized additive models,
 we considered our linear model
 reasonably robust.
   Literature Literature Bates, Douglas; Mächler, Martin; Bolker, Ben; Walker, Steve (2015): Fitting Linear Mixed-Effects Models Using lme4. In <em>J. Stat. Soft. </em>67 (1). DOI: 10.18637/jss.v067.i01. Chamberlain, Scott A.; Boettiger, Carl (2017): R Python, and Ruby clients for GBIF species occurrence data. DOI: 10.7287/peerj.preprints.3304v1.   Chamberlain, Scott A.; Szöcs, Eduard (2013): taxize: taxonomic search and retrieval in R. In <em>F1000Research </em>2, p. 191. DOI: 10.12688/f1000research.2-191.v2. GBIF.org (2021a): Occurrence Download. DOI: 10.15468/dl.z9wz76.   GBIF.org (2021b): Occurrence Download. DOI: 10.15468/dl.3anj3a.   GBIF.org (2021c): Occurrence Download. DOI: 10.15468/dl.t2x9cj.   GBIF.org (2021d): Occurrence Download. DOI: 10.15468/dl.hb2fgs.   Hijmans, Robert J. (2020): raster: Geographic Data Analysis and Modeling. Available online at https://CRAN.R-project.org/package=raster.   Jarvis, Andy; Reuter, Hannes I.; Nelson, Andy; Guevara, E. (2021): Hole-filled seamless SRTM data V4. International Centre for Tropical Agriculture (CIAT). Available online at https://srtm.csi.cgiar.org, updated on 9/7/2021.   Kevan, P. G.; Baker, H G (1983): Insects as Flower Visitors and Pollinators. In <em>Annu. Rev. Entomol. </em>28 (1), pp. 407–453. DOI: 10.1146/annurev.en.28.010183.002203. Klotz, Stefan; Kühn, Ingolf; Durka, Walter (2002): BIOLFLOR – Eine Datenbank zu Biologisch-Ökologischen Merkmalen der Gefäßpflanzen in Deutschland. In : Schriftenreihe für Vegetationskunde, vol. 38, pp. 1–333.   R Core Team (2008): R: A language and environment for statistical computing. Vienna, Austria. Available online at http://www.r-project.org./.   University of East Anglia Climatic Research Unit; Harris, I. C.; Jones, P. D.; Osborn, T. (2021): CRU TS4.05. Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis. Available online at https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681, checked on 9/1/2021.  
description: <strong>Data
 description</strong> The data
 consists of two files of comma separated values with header rows, one
 containing the random effects of the time shifts model and one containing
 the random effects of the temperature shifts model. The
 column names are the same across both files: species =
 Genus and species epithet for each species for which phenology shift was
 estimated<br>group = name of the random variable the parameters were
 estimate for (all "species")<br>intercept = estimated
 random intercept<br>slope = estimated random
 slope<br>intercept_std_err = standard error of the intercept
 estimate<br>slope_std_err = standard error of the slope
 estimate<br>main_var = main fixed variable of the model, either
 "year" or "temp"