CropPol: A dynamic, open and global database on crop pollination

Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open, and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e., berry mass, number of fruits, and fruit density [kg/ha], among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), North America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001–2005 (21 studies), 2006–2010 (40), 2011–2015 (88), and 2016–2020 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA).Fil: Allen Perkins, Alfonso. Universidad Politécnica de Madrid; España. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Magrach, Ainhoa. Universidad Politécnica de Madrid; EspañaFil: Dainese, Matteo. Eurac Research. Institute for Alpine Environment; ItaliaFil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Río Negro; ArgentinaFil: Kleijn, David. Wageningen University & Research; Países BajosFil: Rader, Romina. University of New England; AustraliaFil: Reilly, James R.. Rutgers University; Estados UnidosFil: Winfree, Rachael. Rutgers University; Estados UnidosFil: Lundin, Ola. Swedish University of Agricultural Sciences; SueciaFil: McGrady, Carley M.. North Carolina State University; Estados UnidosFil: Brittain, Claire. University of California at Davis; Estados UnidosFil: Biddinger, David J.. University of California Davis; Estados UnidosFil: Artz, Derek R.. United States Department of Agriculture. Agriculture Research Service; Estados UnidosFil: Elle, Elizabeth. University Fraser Simon; CanadáFil: Hoffman, George. State University of Oregon; Estados UnidosFil: Ellis, James D.. University of Florida; Estados UnidosFil: Daniels, Jaret. University of Florida; Estados Unidos. University Of Florida. Florida Museum Of History; Estados UnidosFil: Gibbs, Jason. University of Manitoba; CanadáFil: Campbell, Joshua W.. University of Florida; Estados Unidos. Usda Ars Northern Plains Agricultural Research Laboratory; Estados UnidosFil: Brokaw, Julia. University of Minnesota; Estados UnidosFil: Wilson, Julianna K.. Michigan State University; Estados UnidosFil: Mason, Keith. Michigan State University; Estados UnidosFil: Ward, Kimiora L.. University of California at Davis; Estados UnidosFil: Gundersen, Knute B.. Michigan State University; Estados UnidosFil: Bobiwash, Kyle. University of Manitoba; Canadá. University Fraser Simon; CanadáFil: Gut, Larry. Michigan State University; Estados UnidosFil: Rowe, Logan M.. Michigan State University; Estados UnidosFil: Boyle, Natalie K.. United States Department of Agriculture. Agriculture Research Service; Estados UnidosFil: Williams, Neal M.. University of California at Davis; Estados UnidosFil: Chacoff, Natacha Paola. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentin

CropPol: a dynamic, open and global database on crop pollination

Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved

CropPol: a dynamic, open and global database on crop pollination

This is the final version. Available from Wiley via the DOI in this record The original dataset (v1.1.0) of the CropPol database can be accessed from the ECOLOGY repository. Main upgrades of these datasets will be versioned and deposited in Zenodo (DOI: 10.5281/zenodo.5546600)Data availability. V.C. Computer programs and data-processing algorithms: The algorithms used in deriving, processing, or transforming data can be accessed in the DataS1.zip file and the Zenodo repository (DOI: 10.5281/zenodo.5546600). V.D. Archiving: The data is archived for long-term storage and access in Zenodo (DOI: 10.5281/zenodo.5546600)Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved.OBServ Projec

Data from: Mitigation of pollen limitation in the lowbush blueberry agroecosystem: effect of augmenting natural pollinators

Growers of small fruit crops often supplement the natural pollinator community by introducing pollinators into commercial orchards and fields, but there are relatively few studies that test the extent to which such interventions increase fruit yield. To test whether plants are limited by pollen availability, inflorescences in 78 commercial lowbush blueberry fields during three years were hand-pollinated either with supplemental outcross pollen, or marked and left as controls (open-pollination). Maximum fruit set with supplemental pollination was in the range 50–65%, whereas with open-pollination, it was slightly, but significantly lower, in the range of 47–57%, suggesting that pollen limitation can affect fruit set. In a two-year experiment, we augmented native pollinators with introduced honey bees, bumble bees, and leaf cutter bees and all combinations in 48 fields. Stigmatic pollen loads were influenced by the total numbers of managed bees in a field in 2010 but not 2011. The presence of leaf cutter bees had a small but positive effect on seed set in 2010, and honey bees had a small but negative influence on seed set in 2011. There was a strong correlation between fruit set reproductive output of supplementally pollinated and open-pollinated plants, suggesting that plant health or plant resources influence reproductive success. Temperature variation among regional groupings of fields was minimal and is unlikely to have accounted for differences among fields in fruit set. We propose several reasons why, despite pollen-limitation of fruit and seed set of blueberry plants, augmentation of the pollinator community of lowbush blueberry did not significantly increase reproduction. These include pollinator-mediated transfer of self-pollen followed by subsequent fruit abortion due to inbreeding depression and resource limitation for fruit maturation. Management practices that focus on increasing outcross pollen receipt, or plant resources for fruit set may have greater overall benefit than pollinator augmentation alone

PathAnalysisData

Data used in path analysis and figure 3 & 4 of Fulton et al. Column codes are in the readme file. Data set is from 24 fields in Neguac in each of two years

BlueberrySeedsetAllYearsAllLocations

Mean field level fruit set, sd and n for 78 blueberry fields with pollination treatment. Column codes are: Year (2009, 2010, 2011), Location (Neguac or St. Stephen), Field (unique field identifiy), Honey (present or absent), Bumble (present or absent), Leaf (present or absent), CSeedset%.mn (mean % seedset of open pollinated shoots for each field), CSeedset.sd (standard deviation of seedset of open pollinated shoots for each field) , CSeedset.n(number of open pollinated shoots per field that seeds were counted for), SSeedset%.mn (mean % seedset of supplementally pollinated shoots for each field),SSeedset.sd (standard deviation seedet of open pollinated shoots for each field), SSeedset.n (number of supplementally pollinated shoots per field

beedistance2010

Data from figure 5 Fulton et al. Column codes are: Individual (unique bee identifier) Date (date)Site (field identifier)Bee (species)Flower Number (number of flowers visited by each individual) Inflorescence (number of inflorescences visited) Distance(cm

negtemp

Temperature data from hobo dataloggers used in fig. 6 in Fulton et al. Data are from 2010 only. Field codes are: Site (field identifier), Day (day of year), Mean (mean recorded temperature), Min (minimum recorded temperature, Max (maximum recorded temperature)

Mean fruitset of open and supplementally pollinated shoots

Mean field level fruit set, sd and n for 78 blueberry fields with pollination treatment. Column codes are: Year (2009,2010,2011),Location (Neguac or St. Stephen), Field (unique field identifier, Honey (honey bee present or absent),Bumble (bumble bee present or absent), Leaf (leaf cutter bee present or absent), CFruitset%.mn (mean % fruitset of open pollinated shoots for each field), CFruitset.sd (standard deviation fruitset of open pollinated shoots for each field) , CFruitset.n(number of open pollinated shoots per field), SFruitset%.mn (mean % fruitset of supplementally pollinated shoots for each field),SFruitset.sd (standard deviation fruitset of open pollinated shoots for each field), SFruitset.n (number of supplementally pollinated shoots per field