DOI: 10.5061/dryad.573n5tbgg
title: Data from: Pollinator competition and the contingency of nectar depletion during an early spring resource pulse

description: Concerns about competition between pollinators are predicated on the
 assumption of floral resource limitation. Floral resource limitation,
 however, is a complex phenomenon involving the interplay of resource
 production by plants, resource demand by pollinators, and exogenous
 factors — like weather conditions — that constrain both plants and
 pollinators. In this study, we examine nectar limitation during the mass
 flowering of rosaceous fruit trees in early spring. Our study is set in
 the same region as a previous study that found extremely severe nectar
 limitation in summer grasslands. We use this seasonal contrast to evaluate
 two alternative hypotheses concerning the seasonal dynamics of floral
 resource limitation: either (H1) rates of resource production and
 consumption are matched through seasonal time to maintain a consistent
 degree of resource limitation or (H2) a mismatch of high floral resource
 production and low pollinator activity in early spring creates a period of
 relaxed resource limitation that intensifies later in the year. We found
 generally much lower depletion in our study compared to the near 100%
 depletion found in the summer study, but depletion rates varied markedly
 through diel time and across sampling days, with afternoon depletion rates
 sometimes exceeding 80%. In some cases, there were also pronounced
 differences in depletion rate across simultaneously sampled floral
 species, indicating different degrees of nectar exploitation. These
 findings generally support the seasonal mismatch hypothesis (H2) but
 underscore the complex contingency of nectar depletion. The challenge of
 future work is to discern how the fluctuation of resource limitation
 across diel, inter-diel, and seasonal time scales translates into
 population-level fitness outcomes for pollinators.
description: Sampling began on March 20th, 2023, and continued for one month,
 concluding on April 21st. Sampling was conducted opportunistically within
 that interval on days without precipitation during sampling hours and with
 midday temperatures above 10C, resulting in a total of nine sampling days.
   On each day of sampling, five mesh bags, large enough
 to cover the distal end of a tree branch for length of about 20 cm, were
 installed on each tree selected to be sampled. Bagging was done before
 8:00, while temperatures were cool enough to preclude pollinator activity.
 Bags were constructed of synthetic fabric similar to mosquito netting,
 which has a minimal influence on the microclimate of the flower. Sampling
 was conducted at intervals beginning at 9:00 and repeated every two hours
 until a 15:00 unless interrupted by rain, and on one day we did an
 additional round at 17:00. In each round, we sampled five bagged flowers
 and five open flowers from each tree. Since the bags we used covered
 multiple flowers, only one bag was removed per sampling round, leaving the
 remaining bagged flowers covered to prevent extraneous visitation during
 sampling. Both bagged and open flowers were chosen according to the
 criteria of having an intact stigma (i.e. not senescent) and actively
 dehiscing anthers. The second of these criteria was adopted after the
 second day of sampling, when we noticed that nectar yields appeared to be
 higher in flowers that were visibly dehiscing. Open flowers were selected
 in close proximity to bagged flowers to minimize any confounding effects
 of within-tree spatial variation in nectar availability. Because
 blackthorn occurs in dense hedges in which individual shrubs cannot
 readily be distinguished, we sampled one pair of flowers (i.e. one bagged,
 one open) from a given branch and then repeated this for a total of five
 branches separated by enough distance along the hedge that we could be
 sure they belonged to different shrubs. Nectar was sampled using 0.5, 1.0,
 or 5 $\mu$L microcapillary tubes, depending on the expected volume of
 nectar. When necessary, multiple tubes were used to extract the full
 volume of nectar from a given flower. The concave shape of the nectary in
 cherry flowers required that flowers be split cross-sectionally to access
 nectar.  We sampled 3-4 trees per day, except for the
 first day of sampling in which only one tree could be sampled, and with
 the exception of blackthorn, due to the methodological accommodation
 explained above that required the sampling of five individual trees per
 sampling round. In total, we sampled 12 plum trees, 27 blackthorn shrubs,
 four cherry trees, and four pear trees, including a total of 621 bagged
 flowers and 627 open flowers. We also obtained hourly temperature and
 precipitation data from a public weather station located about 3.2 km from
 our study area. Trees were selected opportunistically in accordance with
 phenological progression, i.e. we selected trees that were in full bloom
 and sampled them until their flowers began to appear senescent. This
 resulted in a taxonomic sequence that began with plum, then proceeded to
 blackthorn, then a second wave of plum accompanied by cherry, and finally
 pear. Note that the blooming of blackthorn actually extended well beyond
 the period during which we sampled it, but we opted to prioritize the
 sampling of plum and cherry once these became available.
 On April 14th and 21st, we measured the sugar concentration of
 sampled nectar with a handheld refractometer (Bellingham &
 Stanley, UK) whenever we collected a sufficient volume to yield a reliable
 reading. For concentration measurements, we pooled the five nectar samples
 collected from each treatment (i.e. bagged vs. open) for a given tree in a
 given sampling round. Because bagged flowers typically yielded a higher
 volume of nectar due to the exclusion of insect visitors, we more often
 obtained concentration readings from the bagged treatment. On April 21st,
 we noted that, after a cool night with light precipitation, flowers were
 covered with a heavy coat of dew. This provided an opportunity to explore
 the effect of dew on nectar concentration. Similarly, on March 22, flowers
 were too wet to sample reliably at the 9:00 sampling round due to early
 morning rain; on this date, though, we did not have a refractometer
 prepared to measure nectar concentration. 
description: # Data from: Pollinator competition and the contingency of nectar
 depletion during an early spring resource pulse Date:
 "2024-05-23"
 [https://doi.org/10.5061/dryad.573n5tbgg](https://doi.org/10.5061/dryad.573n5tbgg) # Description of the data and file structure All data files are provided in `.rds` format for convenient handling in `R` (and to avoid any tricks Excel might try to play with a regular spreadsheet file). To load an `.rds` files into your `R` environment as a data frame, use the `readRDS()` command. Data frames can easily be exported to `.csv` format using `write.csv()`. Each `.rds` data file is used in one or more of the accompanying RMarkdown files listed in the Code/Software section. # Data files ## `hubland_nectar.rds` Nectar sampling data 1. `datetime`: Date and time of sampling 2. `date`: Date of sampling 3. `time`: Time of sampling 4. `round`: Diel sampling round 5. `hour`: Approximate hour of sampling round 6. `sampler`: Person who did the sampling 7. `species`: Species of sampled flower 8. `tree`: Individual tree sampled 9. `treatment`: Whether the sampled flower was bagged or open 10. `nectar.ul`: Volume of nectar collected from flower ($\mu$L) 11. `meanbagged`: Mean volume ($\mu$L) of nectar in bagged flowers for a given species-tree-date combo 12. `nectar.mc`: Mean-centered nectar volume calculated by dividing `nectar.ul` by `meanbagged` ## `hubland_concentrations.rds` Nectar concentrations 1. `date`: Date of sampling 2. `time`: Time of sampling 3. `round`: Diel sampling round 4. `hour`: Approximate hour of sampling round 5. `tree`: Individual tree sampled 6. `treatment`: Whether the sampled flower was bagged or open 7. `concentration`: Measured nectar concentration in brix units ## `hubland_weather.rds` Weather data 1. `date`: Date 2. `time`: Time of day 3. `temp.mean`: Mean air temperature 4. `precip.mm`: Amount of precipitation (mm) 5. `sun.wh`: Sun intensity (watt-hours) 6. `foraging`: A binary index of forgaing conditions; 0 when `temp.mean` is below 10C, `precip.mm` is above 0, or `sun.wh` equals zero. 7. `hour`: Time of day rounded to nearest hour 8. `hour_std_time`: Hour in standard time (correcting the mid-study onset of daylight savings time in our region) # Model objects ## `brm_hubland_00.rds` Bayesian hierarchical model (see code files `hubland_nectar_modeling.rmd` and `hubland_nectar_visualization.rmd`) ## `brm_hubland_00.pp.rds` Prior-probability check for `brm_hubland_00` (see code files `hubland_nectar_modeling.rmd` and `hubland_nectar_visualization.rmd`) # Figures ## `figS1.png` Map of study area (Supplemental Figure 1) ## `fig1.png` Sampling methods (Figure 1) ## `figS2.png` Nectary morphology of sampled species (Supplemental Figure 2) ## `fig.S3.png` Temporal pattern of sampling (Supplemental Figure 3) # Code/Software ## `hubland_nectar_modeling_rev1.rmd` This script provides a reproducible workflow for model fitting and validation; uses data file `hubland_nectar.rds`. ## `hubland_nectar_visualization_rev1.rmd` This script provides a reproducible workflow for model visualization, generating all figures used in the text; uses data files `hubland_nectar.rds`, `hubland_concentrations.rds`, `hubland_weather.rds` `figS1.png`. `fig1.png`, `figS2.png`, and `fig.S3.png`.