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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 mode

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 mode

Weather can be treated as an important factor in the nesting pattern of several solitary wasps species. Nesting pattern of wasp Monobia angulosa Saussure (Vespidae: Eumeninae) was accessed in present study in the agricultural area of Mucugê-Ibicoara, Chapada Diamantina, Bahia, during an extended periodof dry season. In here we also presented the first record of a natural nest in pre-existing cavities prior used by Xylocopa(Neoxylocopa) cearensis Ducke (Apidae: Xylocopinae). For a description of the M. angulosa’s natural nest a single nest found in apple orchard (Malusdomestica Borkh) was used. For the nesting pattern study we used the trap nests technique with 25 points along the agricultural sampled area; traps were placed in remaining native vegetation. Each sample point was at least 4 km apart. Emerged in the laboratory a total of 19 females and 23 males for a total of 19 established nests, males emerged prior females. Unlike the horizontal trap nests, the natural one was built in vertical linear series and a wooden stake was used as a substrate. Nesting concentrated in the warmer months, between February and April, 2012, with very low precipitation, evidencing influence on nesting pattern of this species, probably due fast response to climatic changes, Alguns estudos evidenciam que condições climáticas são fatores relevantes no padrão de nidificação de várias espécies de vespas solitárias. O presente estudo avaliou o padrão de nidificação da vespa Monobia angulosa Saussure (Vespidae: Eumeninae) no pólo agrícola Mucugê-Ibicoara, Chapada Diamantina, Bahia, durante período de seca prolongada, bem como, a descrição do ninho natural da espécie como o primeiro registro de ninho de M.angulosa construído em ninho abandonado da abelha Xylocopa(Neoxylocopa) cearensis Ducke (Apidae: Xylocopinae). Para o registro e descrição do ninho natural, um único ninho de M. angulosa foi estudado em um pomar de maçã (Malusdomestica Borkh). Para o estudo do padrão de nidificação foi utilizada a técnica de ninhos armadilha, sendo amostrados 25 pontos ao longo do pólo agrícola, com as armadilhas postas em remanescentes de vegetação nativa. Cada ponto amostral apresentou uma distância mínima de 4 km do ponto mais próximo. Emergiram em laboratório um total de 19 fêmeas e 23 machos de um total de 19 ninhos fundados, com a emergência dos machos precedendo a das fêmeas. O ninho natural, ao contrário dos ninhos armadilha que foram dispostos horizontalmente, foi construído em série linear vertical sendo utilizada, como substrato, uma estaca de madeira. As nidificações se concentraram nos meses quentes, entre fevereiro e abril de 2012, porém com baixa precipitação, evidenciando influência no padrão de nidificação da espécie, indicando uma rápida resposta às alterações climáticas