Influence of Honey Bee, Apis mellifera, Hives and Field Size on Foraging Activity of Native Bee Species in Pumpkin Fields

The purpose of this study was to identify bee species active in pumpkin fields in New York and to estimate their potential as pollinators by examining their foraging activity. In addition, we examined whether foraging activity was affected by either the addition of hives of the honey bee, Apis mellifera L., or by field size. Thirty-five pumpkin (Cucurbita spp.) fields ranging from 0.6 to 26.3 ha, 12 supplemented with A. mellifera hives and 23 not supplemented, were sampled during peak flowering over three successive weeks in 2008 and 2009. Flowers from 300 plants per field were visually sampled for bees on each sampling date. A. mellifera, Bombus impatiens Cresson, and Peponapis pruinosa (Say) accounted for 99% of all bee visits to flowers. A. mellifera and B. impatiens visited significantly more pistillate flowers than would be expected by chance, whereas P. pruinosa showed no preference for visiting pistillate flowers. There were significantly more A. mellifera visits per flower in fields supplemented with A. mellifera hives than in fields not supplemented, but there were significantly fewer P. pruinosa visits in supplemented fields. The number of B. impatiens visits was not affected by supplementation, but was affected by number of flowers per field. A. mellifera and P. pruinosa visits were not affected by field size, but B. impatiens visited fewer flowers as field size increased in fields that were not supplemented with A. mellifera hives. Declining A. mellifera populations may increase the relative importance of B. impatiens in pollinating pumpkins in New Yor

Ecology and economics of using native managed bees for almond pollination

Native managed bees can improve crop pollination, but a general framework for evaluating the associated economic costs and benefits has not been developed. We conducted a cost–benefit analysis to assess how managing blue orchard bees (Osmia lignaria Say [Hymenoptera: Megachildae]) alongside honey bees (Apis mellifera Linnaeus [Hymenoptera: Apidae]) can affect profits for almond growers in California. Specifically, we studied how adjusting three strategies can influence profits: (1) number of released O. lignaria bees, (2) density of artificial nest boxes, and (3) number of nest cavities (tubes) per box. We developed an ecological model for the effects of pollinator activity on almond yields, validated the model with published data, and then estimated changes in profits for different management strategies. Our model shows that almond yields increase with O. lignaria foraging density, even where honey bees are already in use. Our cost–benefit analysis shows that profit ranged from −US$1,800 to US$2,800/ acre given different combinations of the three strategies. Adding nest boxes had the greatest effect; we predict an increase in profit between low and high nest box density strategies (2.5 and 10 boxes/acre). In fact, the number of released bees and the availability of nest tubes had relatively small effects in the high nest box density strategies. This suggests that growers could improve profits by simply adding more nest boxes with moderate number of tubes in each. Our approach can support grower decisions regarding integrated crop pollination and highlight the importance of a comprehensive ecological economic framework for assessing these decisions

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

Pollination supply models from a local to global scale

Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-the-art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply models perform best at different spatial scales – the first step towards bridging the stakeholder–academia gap in modelling ecosystem service delivery under ecological intensification