DOI: 10.5061/dryad.1c59zw42f
title: Phenological overlap between crop and pollinators - Dataset

description: Phenological overlap between crop flowering and pollinators is a crucial
 trait for the pollination of more than 75% of the world’s crops. However,
 crop management rarely considers the seasonal aspect of plant-pollinator
 mutualism. Here, we explore the phenological overlap between crops and
 pollinators and how it affects pollination and fruit production. We
 measured the abundance and richness of native and non-native pollinators
 visiting raspberry flowers at two different times during the flowering
 season (i.e., early and late flowering periods) and examined their effects
 on crop yield in 16 fields. The community of pollinators foraging on
 raspberry flowers was more diverse and dominated by native pollinators
 during the early flowering period when most native plants were in flower.
 Later in the season, when native flower resources in the environment
 declined, raspberry flowers were visited mainly by two non-native bees:
 managed honeybees and the invasive bumblebee Bombus terrestris. Pollinator
 contribution to raspberry yield was twice as high in the early flowering
 period compared to the late period (61% vs. 31% increase in drupelet set,
 respectively). Flower damage caused by extremely high visitation frequency
 by non-native bees was also six times lower in the early than in the late
 flowering period (5% vs. 30% of damaged flowers, respectively). Synthesis
 and applications: Providing sufficient pollen and nectar resources to
 support wild pollinators over extended periods in agricultural landscapes
 can contribute to crop pollination and ensure high fruit weight and
 quality. This can be achieved by restoring natural and semi-natural areas
 next to crop fields with native, long-flowering plant species.
 Additionally, growers and crop breeding programmes should consider
 selection of flowering time to coincide with the period of high diversity
 or abundance of native pollinators in order to reduce dependence on
 managed pollinators.
description: In each field, we studied
 raspberry flower visitors along 20, ~25m transects: 8 to estimate
 pollinator abundance (i.e., number of pollinators in 200 flowers) and 12
 to estimate pollinator richness (i.e., number of pollinator species in 200
 flowers) per flowering period (i.e., 20 transects x 2 periods = 40
 transects per field in total) (Vaissière et al. 2011). Along each transect, we observed a total of 200
 focal randomly-selected flowers while walking at a regular pace, and
 recorded the presence and identity of a flower visitor following the
 methodology proposed by Vaissière et al. (2011). Although these authors do not consider the
 time spent in each census as a variable to account for, we were careful
 not to spend more time surveying the 200 flowers in the transects in which
 we observed higher pollinator activity. Pollinators were censused under
 sunny and moderate or no windy conditions. Half of the transects were
 monitored in the morning, between 10:00 and13:00, and the second half in
 the afternoon, between 14:00 and 17:00. In each field, we surveyed flower
 visitors over two non-consecutive days within each flowering period (early
 and late). We quantified reproductive success in terms of
 “drupelet set” (i.e., the proportion of ovules that set a drupelet) in
 naturally (open) pollinated and isolated plants (i.e., plants surrounded
 with fine mesh precluding pollinator visitation). Drupelet set is a close
 predictor of fruit weight. In each field, six to eight flowering stems
 were tagged and randomly assigned to one of two treatments: (a) open
 (i.e., 3 to 4 stems per field/period), or (b) isolated (i.e., 3 to 4 stems
 per field/period). Later, approximately four weeks after the survey of
 flower visitors, we randomly collected fruits from each tagged raspberry
 stem, totalling 2,668 fruits. Fruits were harvested close to maturity (a
 few days before they easily detached from the receptacle), transported to
 the laboratory in an electric cooler, and stored in a freezer until
 processing. We used a magnifying glass (20x) to count (1) the number of
 developed drupelets; (2) the number of undeveloped drupelets (i.e.,
 presence of a dried stigma without a drupelet); and, in the case of fruits
 from the open-pollination treatment, (3) the presence/absence of flower
 damage, measured as the presence of holes in the vestigial
 sepals. Finally, each
 fruit was weighed using an electronic scale. The proportion of ovules that
 developed into a drupelet (i.e., drupelet set) is strongly correlated with
 fresh fruit weight and can therefore be used as a proxy of crop
 yield. 
description: # Phenological overlap between crop and pollinators - Dataset
 [https://doi.org/10.5061/dryad.1c59zw42f](https://doi.org/10.5061/dryad.1c59zw42f) Fieldwork was conducted during austral spring (November - December) 2013 and summer (January - March) 2014, in 16 raspberry fields, “Autumn bliss” variety, located in northwestern Patagonia, Argentina. In each field, we studied raspberry flower visitors along 20, \~25m transects: 8 to estimate pollinator abundance (i.e., number of pollinators in 200 flowers) and 12 to estimate pollinator richness (i.e., number of pollinator species in 200 flowers) per flowering period (i.e., 20 transects x 2 flowering periods = 40 transects per field in total). Along each transect, we observed a total of 200 focal randomly-selected flowers while walking at a regular pace, and recorded the presence and identity of a flower visitor. Pollinators were censused under sunny and moderate or no windy conditions. We also quantified reproductive success in terms of “drupelet set” (i.e., the proportion of ovules that set a drupelet) in naturally (open) pollinated and isolated plants (i.e., plants surrounded with fine mesh precluding pollinator visitation). In each field, six to eight flowering stems were tagged and randomly assigned to one of two treatments: (a) open (i.e., 3 to 4 stems per field/period), or (b) isolated (i.e., 3 to 4 stems per field/period). Later, approximately four weeks after the survey of flower visitors, we randomly collected fruits from each tagged raspberry stem, totalling 2,635 fruits. Fruits were harvested close to maturity, transported to the laboratory in an electric cooler, and stored in a freezer until processing. We used a magnifying glass (20x) to count (1) the number of developed drupelets; (2) the number of undeveloped drupelets (i.e., presence of a dried stigma without a drupelet); and, in the case of fruits from the open-pollination treatment, (3) the presence/absence of flower damage, measured as the presence of holes in the vestigial sepals. ## Description of the data and file structure 1\. Abundance dataset\. It contains the abundance of raspberry flower visitors in 200 flowers along 8 transects per field\, during the early and late flowering periods\. 2\. Richness dataset\. It contains the richness of raspberry flower visitors in 200 flowers along 12 transects per field\, during the early and late flowering periods\. 3\. Fruits dataset\. It contains the number of drupelets\, undeveloped drupelets \(pistils\)\, and the presence/absence of damage in each collected fruit from different plants \(isolated or open to pollinators\) in each field during the early and late flowering periods\. ##