Data from: Pollinator competition and the contingency of nectar depletion during an early spring resource pulse
Data files
May 27, 2024 version files 227.49 KB
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hubland_concentrations.rds
3.49 KB
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hubland_nectar.rds
146.14 KB
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hubland_weather.rds
74.54 KB
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README.md
3.32 KB
Abstract
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.
README: 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
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
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datetime
: Date and time of sampling -
date
: Date of sampling -
time
: Time of sampling -
round
: Diel sampling round -
hour
: Approximate hour of sampling round -
sampler
: Person who did the sampling -
species
: Species of sampled flower -
tree
: Individual tree sampled -
treatment
: Whether the sampled flower was bagged or open nectar.ul
: Volume of nectar collected from flower ($\mu$L)meanbagged
: Mean volume ($\mu$L) of nectar in bagged flowers for a given species-tree-date combonectar.mc
: Mean-centered nectar volume calculated by dividingnectar.ul
bymeanbagged
hubland_concentrations.rds
Nectar concentrations
-
date
: Date of sampling -
time
: Time of sampling -
round
: Diel sampling round -
hour
: Approximate hour of sampling round -
tree
: Individual tree sampled -
treatment
: Whether the sampled flower was bagged or open -
concentration
: Measured nectar concentration in brix units
hubland_weather.rds
Weather data
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date
: Date -
time
: Time of day -
temp.mean
: Mean air temperature -
precip.mm
: Amount of precipitation (mm) -
sun.wh
: Sun intensity (watt-hours) -
foraging
: A binary index of forgaing conditions; 0 whentemp.mean
is below 10C,precip.mm
is above 0, orsun.wh
equals zero. -
hour
: Time of day rounded to nearest hour -
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
.
Methods
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.