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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

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 da

Mitigating pollinator declines in agriculturally dominated landscapes to safeguard pollination services requires the involvement of farmers and their willingness to adopt pollinator-friendly management. However, farmer knowledge, perceptions, and actions to support on-farm pollinators and their alignment with science-based knowledge and recommendations are rarely evaluated. To close this knowledge gap, we interviewed 560 farmers from 11 countries around the world, cultivating at least one of four widely grown pollinator-dependent crops (apple, avocado, kiwifruit, oilseed rape). We particularly focused on non-bee crop pollinators which, despite being important pollinators of many crops, received less research attention than bees. We found that farmers perceived bees to be more important pollinators than other flower-visiting insects. However, around 75% of the farmers acknowledged that non-bees contributed to the pollination of their crops, seeing them as additional pollinators rather than substitutes for bees. Despite farmers rating their own observations as being most important in how they perceived the contribution of different crop pollinator taxa, their perception aligned closely with results from available scientific studies across crops and countries. Farmer perceptions were also linked with their pollinator management practices, e.g. farmers who used managed bees for crop pollination services (more than half the farmers) rated these managed bees as particularly important. Interestingly, their willingness to establish wildflower strips or manage hedgerows to enhance pollinator visitation was linked to their ecological knowledge of non-bees or to government subsidies. Farmers adapted practices to enhance pollination services depending on the crop, which indicates an understanding of differences in the pollination ecology of crops. Almost half of the farmers had changed on-farm pollination management in the past 10 years and farm practices differed greatly betwe

Functional traits mediate the response of communities to disturbances (response traits) and their contribution to ecosystem functions (effect traits). To predict how anthropogenic disturbances influence ecosystem services requires a dual approach including both trait concepts. Here, we used a response–effect trait conceptual framework to understand how local and landscape features affect pollinator functional diversity and pollination services in apple orchards. We worked in 110 apple orchards across four European regions. Orchards differed in management practices. Low-intensity (LI) orchards were certified organic or followed close-to-organic practices. High-intensity (HI) orchards followed integrated pest management practices. Within each management type, orchards encompassed a range of local (flower diversity, agri-environmental structures) and landscape features (orchard and pollinator-friendly habitat cover). We measured pollinator visitation rates and calculated trait composition metrics based on 10 pollinator traits. We used initial fruit set as a measure of pollination service. Some pollinator traits (body size and hairiness) were negatively related to orchard cover and positively affected by pollinator-friendly habitat cover. Bee functional diversity was lower in HI orchards and decreased with increased landscape orchard cover. Pollination service was not associated with any particular trait but increased with pollinator trait diversity in LI orchards. As a result, LI orchards with high pollinator trait diversity reached levels of pollination service similar to those of HI orchards. Synthesis and applications. Pollinator functional diversity enables pollinator communities to respond to agricultural intensification and to increase pollination function. Our results show that efforts to promote biodiversity provide greater returns in low-intensity than in high-intensity orchards. The fact that low-intensity orchards with high pollinator functional diversity re

Pollinators face multiple pressures and there is evidence of populations in decline. As demand for insect-pollinated crops increases, crop production is threatened by shortfalls in pollination services. Understanding the extent of current yield deficits due to pollination and identifying opportunities to protect or improve crop yield and quality through pollination management is therefore of international importance. To explore the extent of “pollination deficits,” where maximum yield is not being achieved due to insufficient pollination, we used an extensive dataset on a globally important crop, apples. We quantified how these deficits vary between orchards and countries and we compared “pollinator dependence” across different apple varieties. We found evidence of pollination deficits and, in some cases, risks of overpollination were even apparent for which fruit quality could be reduced by too much pollination. In almost all regions studied we found some orchards performing significantly better than others in terms of avoiding a pollination deficit and crop yield shortfalls due to suboptimal pollination. This represents an opportunity to improve production through better pollinator and crop management. Our findings also demonstrated that pollinator dependence varies considerably between apple varieties in terms of fruit number and fruit quality. We propose that assessments of pollination service and deficits in crops can be used to quantify supply and demand for pollinators and help to target local management to address deficits although crop variety has a strong influence on the role of pollinators.

Pollination is an ecosystem service that directly contributes to agricultural production, and can therefore provide a strong incentive to conserve natural habitats that support pollinator populations. However, we have yet to provide consistent and convincing pollination service valuations to effectively slow the conversion of natural habitats. We use coffee in Kodagu, India, to illustrate the uncertainties involved in estimating costs and benefits of pollination services. First, we fully account for the benefits obtained by coffee agroforests that are attributable to pollination from wild bees nesting in forest habitats. Second, we compare these benefits to the opportunity cost of conserving forest habitats and forgoing conversion to coffee production. Throughout, we systematically quantify the uncertainties in our accounting exercise and identify the parameters that contribute most to uncertainty in pollination service valuation. We find the value of pollination services provided by one hectare of forest to be 25% lower than the profits obtained from converting that same surface to coffee production using average values for all parameters. However, our results show this value is not robust to moderate uncertainty in parameter values, particularly that driven by variability in pollinator density. Synthesis and applications. Our findings emphasize the need to develop robust estimates of both value and opportunity costs of pollination services that take into account landscape and management variables. Our analysis contributes to strengthening pollination service arguments used to help stakeholders make informed decisions on land use and conservation practices. © 2019 The Authors. Journal of Applied Ecology © 2019 British Ecological Society

Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society.

Knowledge sharing between scientists and nonscientist stakeholders is necessary to implement research findings in an appropriate and effective manner within the context of the environment and conservation sectors. Yet scientific ecological knowledge is rarely shared and transferred effectively. This special section has addressed a number of opportunities and barriers to the improvement of scientific communication and knowledge transfer with respect to environmental management in tropical settings. A main challenge is seen in creating a research `impact-metric' system, which is fundamental to foster knowledge sharing with institutional research incentives. Partnering with local institutions and research centers as well as participatory research methods will promote effective knowledge exchange. Research relevance and impact will be improved by matching interdisciplinary research with local capacity building and support through research activities. We conclude that training the next generation of tropical biologists through more effective knowledge sharing will be crucial to the long term success of scientifically based environmental management in tropical regions.

Conserving tropical ecosystems is one of the foremost challenges of the 21st century. Lately, conservation efforts have focused on advancing conservation through dialogue and interaction with and among diverse stakeholders. Knowledge sharing, and specifically the transfer of scientific knowledge, can raise ecological awareness among stakeholders and is necessary to facilitate dialogue, yet the communication of tropical scientific research to local stakeholders is rarely effectively implemented. Such a communication gap potentially undermines the conservation and management of natural resources. The papers in this special section highlight the difficulties and benefits of sharing ecological knowledge, as well as the underlying reasons for why a research-implementation gap has arisen.