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Tools to predict pollinator activity at regional scales generally rely on land cover maps, combined with human-inferred mechanistic rules and/or expert knowledge. Recently, Giménez-García et al. (2023) showed that, using large pollinator datasets, different environmental variables, and machine learning models, those predictions can be enhanced but at the cost of losing model interpretability. Here, we complement this work by exploring the potential of using advanced machine learning techniques to directly infer wild-bee visitation rates across different biomes only from land cover maps and available pollinator data while maintaining a mechanistic interpretation. In particular, we assess the ability of convolutional neural networks (CNNs), which are deep learning models, to infer mechanistic rules able to predict pollinator habitat use. At a global scale, our CNNs achieved a rank correlation coefficient of 0.44 between predictions and observations of pollinator visitation rates, doubling that of the previous human-inferred mechanistic models presented in Giménez-García et al. (2023) (0.17). Most interestingly, we show that the predictions depend on both landscape composition and configuration variables, with prediction rules being more complex than those of traditional mechanistic processes. We also demonstrate how CNNs can improve the predictions of our previous data-driven models that did not use land cover maps by creating a new model that combined the predictions of our CNN with those of our best regression model based on environmental variables, a Bayesian ridge regressor. This new ensemble model improved the overall rank correlation from 0.56 to 0.64.

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.

Objectives: Tai Chi (TC) shows some beneficial effects in reducing pain in knee osteoarthritis (OA). However, the selection of criteria TC forms in previous studies were unclear and inconsistent, possibly accounting for the varying outcomes and rendering the training effects suboptimal. We have selected four optimal TC (OTC) forms based on the knee joint load and its association with pain. This pilot study sought to examine the effect of the OTC forms on reducing knee pain in individuals with knee OA. Methods: Fifteen knee OA participants were recruited. Their knee joint pain level was rated by using the Visual Analogue Scale before and after two weeks of OTC training and compared between these two assessments. Results: The two-week OTC training course was well accepted by our participants. The knee OA pain showed a significant reduction (median pain score: 5 ​cm before training and 1 ​cm post-training, Wilcoxon p ​

In recent years, there have been increasing demands for public policy design to be guided by empirical evidence, especially in programmes addressing social inclusion. In this scenario, qualitative approaches are often subordinated to the macro results obtained from quantitative instruments. Based on the results of a mixed-methods RCT research design, this article explores how qualitative research tools might provide qualitative significance – information as valuable as numerical data obtained using standardised scales. This proposal is based on the results of a research project addressing problems associated with barriers to employment and mental health among its participants. The impact of gender-sensitive issues – i.e., the unequal distribution of care responsibilities – or the development of mental health problems such as anxiety are often underestimated in studies based purely on statistical data. Our approach underscores the value of the testimonies and narratives obtained through qualitative tools – e.g., semi-structured interviews and focus groups – and how these can provide first-class evidence for the design and impact assessment of public policies tackling social inclusion.

Este trabajo discute el concepto de algoritmo desde una mirada antropológica, considerando la centralidad de la noción de agencia y tecnología desarrollada en los estudios de ciencia y tecnología. En primer lugar, se revisan las principales áreas que han estudiado las ecologías digitales y el lugar que ocupan los algoritmos en ellas. A continuación, se propone una definición de los sistemas algorítmicos, en tanto que ensamblajes socio-técnicos abiertos al entorno y constituidos en las redes de relaciones de las que emergen. En tercer lugar, se apuntan algunas aproximaciones metodológicas desde las que abordar la «opacidad» con la que generalmente se caracterizan los sistemas algorítmicos. Por último, se presentan los textos de este monográfico tomando el par «opacidad-transparencia» como eje vertebrador.

Pollinator choices when selecting flowers for nectar or pollen collection are crucial in determining the effectiveness of pollination services provided to plants. From the plant's perspective, this effectiveness is a phenomenon shaped by factors at both the species‐ (e.g. pollinator density and flower morphology) and community‐level, including pollinator diversity and plant competition for pollinators. At the species level, individual pollinator effectiveness is influenced by foraging choices, plant identity, and the resulting pollen flow within and between plant species. In natural ecosystems, these species coexist within a complex community, where various interactions can modify foraging choices and alter pollen flows, giving rise to community‐level effectiveness, a less explored aspect of pollinator effectiveness. This study investigates the drivers of individual pollinator foraging choices across two study areas and two flowering seasons. It also assesses the community‐level effectiveness of pollination services received by different plant species, considering indirect interactions between plants through shared pollinators and evaluating their impact on plant reproductive success. Our results show that the determinants of pollinator foraging choices are consistent across different habitats, with floral constancy and flower abundance playing pivotal roles across all species and sites. Foraging choices can shift throughout the flowering season as plant and pollinator composition changes, significantly impacting pollination effectiveness. The overlap in pollination service use by individuals of the same plant species decreases their fruit set, whereas sharing pollinator services with individuals of other plant species increases fruit set. Our results support significant, positive biodiversity–ecosystem functioning associations driven by both plant and pollinator species richness, suggesting that the overlap in pollination service use by different plant species fosters facilitative interactions rather than competition. This is likely influenced by more stable pollination supplies under high plant species diversity conditions and the existence of mechanisms to mitigate the negative impacts of heterospecific pollen deposition. [ABSTRACT FROM AUTHOR]

Interaction networks are a widely used tool to understand the dynamics of plant–pollinator ecological communities. However, while most mutualistic networks have been defined at the species level, ecological processes such as pollination take place at different scales, including the individual or patch levels. Yet, current approaches studying fine‐grain sub‐specific plant–pollinator networks only account for interactions among nodes belonging to a single plant species due to the conceptual and mathematical limitations of modeling simultaneously several plant species each composed of several nodes. Here, we introduce a multilayer diffusion network framework that allows modeling simple diffusion processes between nodes pertaining to the same or different layers (i.e. species). It is designed to depict from the network structure the potential conspecific and heterospecific pollen flows among plant individuals or patches. This potential pollen flow is modeled as a transport‐like system, in which pollen grain movements are represented as random‐walkers that diffuse on an ensemble of bipartite layers of conspecific plants and their shared pollinators. We exemplify this physical conceptualization using a dataset of nine fine‐grain sub‐specific plant–pollinator networks from a Mediterranean grassland of annual plants, where plant nodes represent groups of conspecifics within patches of 1 m2. The diffusion networks show pollinators effectively connecting sets of patches of the same and different plant species, forming a modular structure. Interestingly, different properties of the network structure, such as the conspecific pollen arrival probability and the number of conspecific subgraphs in which plants are embedded, are critical for the seed production of different plant species. We provide a simple but robust set of metrics to calculate potential pollen flow and scale down network ecology to functioning properties at the individual or patch level, where most ecological processes take place, hence moving forward the description and interpretation of species‐rich communities across scales. [ABSTRACT FROM AUTHOR]

Tools to predict pollinator activity at regional scales generally rely on land cover maps, combined with human-inferred mechanistic rules and/or expert knowledge. Recently, showed that, using large pollinator datasets, different environmental variables, and machine learning models, those predictions can be enhanced but at the cost of losing model interpretability. Here, we complement this work by exploring the potential of using advanced machine learning techniques to directly infer wild-bee visitation rates across different biomes only from land cover maps and available pollinator data while maintaining a mechanistic interpretation. In particular, we assess the ability of convolutional neural networks (CNNs), which are deep learning models, to infer mechanistic rules able to predict pollinator habitat use. At a global scale, our CNNs achieved a rank correlation coefficient of 0.44 between predictions and observations of pollinator visitation rates, doubling that of the previous human-inferred mechanistic models presented in (0.17). Most interestingly, we show that the predictions depend on both landscape composition and configuration variables, with prediction rules being more complex than those of traditional mechanistic processes. We also demonstrate how CNNs can improve the predictions of our previous data-driven models that did not use land cover maps by creating a new model that combined the predictions of our CNN with those of our best regression model based on environmental variables, a Bayesian ridge regressor. This new ensemble model improved the overall rank correlation from 0.56 to 0.64. [ABSTRACT FROM AUTHOR]

In recent years, there have been increasing demands for public policy design to be guided by empirical evidence, especially in programmes addressing social inclusion. In this scenario, qualitative approaches are often subordinated to the macro results obtained from quantitative instruments. Based on the results of a mixed-methods RCT research design, this article explores how qualitative research tools might provide qualitative significance – information as valuable as numerical data obtained using standardised scales. This proposal is based on the results of a research project addressing problems associated with barriers to employment and mental health among its participants. The impact of gender-sensitive issues – i.e., the unequal distribution of care responsibilities – or the development of mental health problems such as anxiety are often underestimated in studies based purely on statistical data. Our approach underscores the value of the testimonies and narratives obtained through qualitative tools – e.g., semi-structured interviews and focus groups – and how these can provide first-class evidence for the design and impact assessment of public policies tackling social inclusion. [ABSTRACT FROM AUTHOR]