WorldFAIR Project (D10.1) Agriculture-related pollinator data standards use cases report

Although pollination is an essential ecosystem service that sustains life on Earth, data on this vital process is largely scattered or unavailable, limiting our understanding of the current state of pollinators and hindering effective actions for their conservation and sustainable management. In addition to the well-known challenges of biodiversity data management, such as taxonomic accuracy, the recording of biotic interactions like pollination presents further difficulties in proper representation and sharing. Currently, the widely-used standard for representing biodiversity data, Darwin Core, lacks properties that allow for adequately handling biotic interaction data, and there is a need for FAIR vocabularies for properly representing plant-pollinator interactions. Given the importance of mobilising plant-pollinator interaction data also for food production and security, the Research Data Alliance Improving Global Agricultural Data Community of Practice has brought together partners from representative groups to address the challenges of advancing interoperability and mobilising plant-pollinator data for reuse. This report presents an overview of projects, good practices, tools, and examples for creating, managing and sharing data related to plant-pollinator interactions, along with a work plan for conducting pilots in the next phase of the project. We present the main existing data indexing systems and aggregators for plant-pollinator interaction data, as well as citizen science and community-based sourcing initiatives. We also describe current challenges for taxonomic knowledge and present two data models and one semantic tool that will be explored in the next phase. In preparation for the next phase, which will provide best practices and FAIR-aligned guidelines for documenting and sharing plant-pollinator interactions based on pilot efforts with data, this Case Study comprehensively examined the methods and platforms used to create and share such data. By understanding the nature of data from various sources and authors, the alignment of the retrieved datasets with the FAIR principles was also taken into consideration. We discovered that a large amount of data on plant-pollinator interaction is made available as supplementary files of research articles in a diversity of formats and that there are opportunities for improving current practices for data mobilisation in this domain. The diversity of approaches and the absence of appropriate data vocabularies causes confusion, information loss, and the need for complex data interpretation and transformation. Our explorations and analyses provided valuable insights for structuring the next phase of the project, including the selection of the pilot use cases and the development of a ‘FAIR best practices’ guide for sharing plant-pollinator interaction data. This work primarily focuses on enhancing the interoperability of data on plant-pollinator interactions, envisioning its connection with the effort WorldFAIR is undertaking to develop a Cross-Domain Interoperability Framework. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393

The role of asymmetric interactions on the effect of habitat destruction in mutualistic networks

Plant-pollinator mutualistic networks are asymmetric in their interactions: specialist plants are pollinated by generalist animals, while generalist plants are pollinated by a broad involving specialists and generalists. It has been suggested that this asymmetric ---or disassortative--- assemblage could play an important role in determining the equal susceptibility of specialist and generalist plants under habitat destruction. At the core of the argument lies the observation that specialist plants, otherwise candidates to extinction, could cope with the disruption thanks to their interaction with generalist pollinators. We present a theoretical framework that supports this thesis. We analyze a dynamical model of a system of mutualistic plants and pollinators, subject to the destruction of their habitat. We analyze and compare two families of interaction topologies, ranging from highly assortative to highly disassortative ones, as well as real pollination networks. We found that several features observed in natural systems are predicted by the mathematical model. First, there is a tendency to increase the asymmetry of the network as a result of the extinctions. Second, an entropy measure of the differential susceptibility to extinction of specialist and generalist species show that they tend to balance when the network is disassortative. Finally, the disappearance of links in the network, as a result of extinctions, shows that specialist plants preserve more connections than the corresponding plants in an assortative system, enabling them to resist the disruption.Comment: 14 pages, 7 figure

WorldFAIR (D10.2) Agricultural Biodiversity Standards, Best Practices and Guidelines Recommendations

The WorldFAIR Case Study on Agricultural Biodiversity (WP10) addresses the challenges of advancing interoperability and mobilising plant-pollinator interactions data for reuse. Previous efforts, reported in Deliverable 10.1 - from our discovery phase - provided an overview of projects, best practices, tools, and examples for creating, managing and sharing data related to plant-pollinator interactions, along with a work plan for conducting pilot studies. The current report presents the results from the pilot phase of the Case Study, which involved six pilot studies adopting standards and recommendations from the discovery phase. The pilots enabled the handling  of concrete examples and the generation of reusable materials tailored to this domain, as well as providing better estimates for the overall costs of adoption for future projects. Our approach for plant-pollinator data standardisation is based on the widely-used standard for representing biodiversity data, Darwin Core, developed and maintained by the Biodiversity Information Standards (TDWG), in conjunction with a data model and vocabulary proposed by the Brazilian Network of Plant-Pollinator Interactions (REBIPP). The pilot studies also underwent a process of “FAIRification” (i.e., transforming data into a format that adheres to the FAIR data principles) using the Global Biotic Interactions (GloBI, Poelen et al. 2014) platform. Additionally, we present the publishing model for Biotic Interactions developed in collaboration with the Global Biodiversity Information Facility (GBIF), which leads the WorldFAIR Case Study on Biodiversity, as part of the proposed GBIF New Data Model, along with a concrete example of its use by one of the pilots. This effort led to the development of ‘FAIR best practices’ guidelines for sharing plant-pollinator interaction data. The primary focus of this work is to enhance the interoperability of data on plant-pollinator interactions, aligning with WorldFAIR efforts  to develop a Cross-Domain Interoperability Framework. We have successfully promoted the adoption of standards and increased the interoperability of plant-pollinator interactions data, resulting in a process that allows for tracing the provenance of the data, as well as facilitating the reuse of datasets crucial for understanding this essential ecosystem service and its changes due to human impact. Our effort demonstrates there are several possible paths for FAIRification, tailored to institutional needs, and we have shown that different approaches can contribute to promoting data interoperability and data availability for reuse, which is the ultimate goal of this initiative. Consequently, we have successfully ensured FAIR data for understanding plant-pollinator interactions at biologically-relevant scales for crops, with broad participation from initiatives in Europe, South America, Africa, North America, and elsewhere. We have also established concrete guidelines on FAIR data best practices customised for pollination data, metadata, and other digital objects, promoting the scalable adoption of these standards and FAIR data best practices by multiple initiatives. We believe this effort can assist similar initiatives in adopting interoperability standards for this domain and contribute to our understanding of how plant-pollinator interactions contribute to sustain life on Earth. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393. 

Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition

Functional Diversity of Plant–Pollinator Interaction Webs Enhances the Persistence of Plant Communities

Pollination is exclusively or mainly animal mediated for 70% to 90% of angiosperm species. Thus, pollinators provide an essential ecosystem service to humankind. However, the impact of human-induced biodiversity loss on the functioning of plant–pollinator interactions has not been tested experimentally. To understand how plant communities respond to diversity changes in their pollinating fauna, we manipulated the functional diversity of both plants and pollinators under natural conditions. Increasing the functional diversity of both plants and pollinators led to the recruitment of more diverse plant communities. After two years the plant communities pollinated by the most functionally diverse pollinator assemblage contained about 50% more plant species than did plant communities pollinated by less-diverse pollinator assemblages. Moreover, the positive effect of functional diversity was explained by a complementarity between functional groups of pollinators and plants. Thus, the functional diversity of pollination networks may be critical to ecosystem sustainability

Rhododendrons Beyond just beautiful flowers

The alarm went off, as always, at 4 a.m. The first thing I did was to head for the door to check out the day’s weather so I could plan my schedule, which would inevitably be determined by the nature of the clouds. In Sikkim, a small region in the Eastern Himalaya, everyone’s life had to be adjusted to unpredictable weather. In my case, I quickly discovered that at high-altitudes, a field researcher had to take advantage of every sunny day, particularly if she was studying plant-pollinator interaction involving Rhododendrons, possibly the most exquisite flowers on the plane

La diversité des interactions plantes-pollinisateurs : Un pré-requis indispensable à la stabilité des écosystèmes

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Tracking Climate Effects on Plant-Pollinator Interaction Phenology with Satellites and Honey Bee Hives

Background/Question/Methods: The complexity of plant-pollinator interactions, the large number of species involved, and the lack of species response functions present challenges to understanding how these critical interactions may be impacted by climate and land cover change on large scales. Given the importance of this interaction for terrestrial ecosystems, it is desirable to develop new approaches. We monitor the daily weight change of honey bee (Apis mellifera) colonies to record the phenology of the Honey Bee Nectar Flow (HBNF) in a volunteer network (honeybeenet.gsfc.nasa.gov). The records document the successful interaction of a generalist pollinator with a variety of plant resources. We extract useful HBNF phenology metrics for three seasons. Sites currently exist in 35 states/provinces in North America, with a concentration in the Mid-Atlantic region. HBNF metrics are compared to standard phenology metrics derived from remotely sensed vegetation indices from NASA's MODIS sensor and published results from NOAA's A VHRR. At any given time the percentage of plants producing nectar is usually a sma11 fraction of the total satellite sensor signal. We are interested in determining how well the 'bulk' satellite vegetation parameters relate to the phenology of the HBNF, and how it varies spatially on landscape to continental scales. Results/Conclusions: We found the median and peak seasonal HBNF dates to be robust, with variation between replicate scale hives of only a few days. We developed quality assessment protocols to identify abnormal colony artifacts. Temporally, the peak and median of the HBNF in the Mid-Atlantic show a significant advance of 0.58 d/y beginning about 1970, very similar to that observed by the A VHRR since 1982 (0.57 d/y). Spatially, the HBNF metrics are highly correlated with elevation and winter minimum temperature distribution, and exhibit significant but regionally coherent inter-annual variation. The relationship between median of the spring HBNF with the "Green-up" metric from the 500 meter MODIS NDVI phenology product, for sites throughout the Eastern US 2000-2009, is well described by a single linear fit (r(exp 2) = 0.72). We conclude.that for the tree-dominated areas of the Eastern US at least the spring HBNF can be tracked very well by MODIS phenology. Analysis of other regions and seasons is presently underway but with more limited data. Spatial patterns in the eastern US and management implications will be presented and discussed