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1. World-wide declines in pollinators, including bumblebees, are attributed to a multitude of Stressors such as habitat loss, resource availability, emerging viruses and parasites, exposure to pesticides, and climate change, operating at various spatial and temporal scales. Disentangling individual and interacting effects of these Stressors, and understanding their impact at the individual, colony and population level are a challenge for systems ecology. Empirical testing of all combinations and contexts is not feasible. A mechanistic multilevel systems model (individual-colony-population-community) is required to explore resilience mechanisms of populations and communities under stress. 2. We present a model which can simulate the growth, behaviour and survival of six UK bumblebee species living in any mapped landscape. Bumble-BEEHAVE simulates, in an agent-based approach, the colony development of bumblebees in a realistic landscape to study how multiple Stressors affect bee numbers and population dynamics. We provide extensive documentation, including sensitivity analysis and validation, based on data from literature. The model is freely available, has flexible settings and includes a user manual to ensure it can be used by researchers, farmers, policy-makers, NGOs or other interested parties. 3. Model outcomes compare well with empirical data for individual foraging behaviour, colony growth and reproduction, and estimated nest densities. 4. Simulating the impact of reproductive depression caused by pesticide exposure shows that the complex feedback mechanisms captured in this model predict higher colony resilience to stress than suggested by a previous, simpler model. 5. Synthesis and applications. The Bumble-BEEHAVE model represents a significant step towards predicting bumblebee population dynamics in a spatially explicit way. It enables researchers to understand the individual and interacting effects of the multiple Stressors affecting bumblebee survival and the feedback mechanisms that may buffer a colony against environmental stress, or indeed lead to spiralling colony collapse. The model can be used to aid the design of field experiments, for risk assessments, to inform conservation and farming decisions and for assigning bespoke management recommendations at a landscape scale.

A notable increase in failure of managed European honeybee Apis mellifera L. colonies has been reported in various regions in recent years. Although the underlying causes remain unclear, it is likely that a combination of stressors act together, particularly varroa mites and other pathogens, forage availability and potentially pesticides. It is experimentally challenging to address causality at the colony scale when multiple factors interact. In silico experiments offer a fast and cost‐effective way to begin to address these challenges and inform experiments. However, none of the published bee models combine colony dynamics with foraging patterns and varroa dynamics. We have developed a honeybee model, BEEHAVE, which integrates colony dynamics, population dynamics of the varroa mite, epidemiology of varroa‐transmitted viruses and allows foragers in an agent‐based foraging model to collect food from a representation of a spatially explicit landscape. We describe the model, which is freely available online (www.beehave-model.net). Extensive sensitivity analyses and tests illustrate the model's robustness and realism. Simulation experiments with various combinations of stressors demonstrate, in simplified landscape settings, the model's potential: predicting colony dynamics and potential losses with and without varroa mites under different foraging conditions and under pesticide application. We also show how mitigation measures can be tested. Synthesis and applications. BEEHAVE offers a valuable tool for researchers to design and focus field experiments, for regulators to explore the relative importance of stressors to devise management and policy advice and for beekeepers to understand and predict varroa dynamics and effects of management interventions. We expect that scientists and stakeholders will find a variety of applications for BEEHAVE, stimulating further model development and the possible inclusion of other stressors of potential importance to honeybee colony dynamics.

The concept of ecosystem diversity essential variables (EEVs) offers a foundation for ecosystem studies. Identification of EEVs continues to be a challenge in the field of ecology, due to the lack of a conceptual and applied framework. This paper develops a conceptual framework, offering theoretical foundation and a methodology for identifying EEVs, reflecting essential biodiversity and geodiversity variables. We start with a conceptual model of ecosystem essential variables linking biodiversity and geodiversity processes into ecosystem diversity as a web of interactions (WoI). The WoI components and interactions enable the identification of EEVs and their essentiality by relating interactions among diversities to variables that identify them. We tested our conceptual pass way by analyzing drivers and feedbacks of ecosystem processes in the Negev Highlands. Based on the general models and research of the Negev Highlands, we present four steps for EEVs: (1) developing a general conceptual model of the abiotic and biotic components of the ecosystem that links both biodiversity and geodiversity and their interactions; (2) testing the validity of the general model for a specific ecosystem to find out the hydro‐geo‐ecological drivers and feedbacks controlling ecosystem diversity; (3) constructing a WoI that adds to the regular analysis of an ecosystem as an interaction among geodiversity and biodiversity by breaking down the two components of diversities into subcomponent and their interactions; and (4) translating of the WoI components and interactions to EEVs. We suggest that EEVs should be related not only to the components but also to the interactions among diversities. These steps are essential for developing a scientific framework that allows for systematic identification of EEVs and justification regarding the final selection of the essential variables. We suggest that the approach can potentially be applied to all global terrestrial system.

Many manuscripts submitted to the Journal of International Business Studies propose an interaction effect in their models in an effort to explain the complexity and contingency of relationships across borders. In this article, we provide guidance on how best to explain the interaction effects theoretically within and across levels of analysis. First, in the case of interactions within the same level of analysis, we suggest that authors provide an explanation of the mechanisms that link the main independent variable to the dependent variable, and then explain how the interaction variable modifies these mechanisms. Moreover, to ensure that the arguments are theoretically complete, we suggest that authors theoretically rule out the potential reverse interaction effect between the main variable and moderating variable. Second, in the case of interactions across levels of analysis, we suggest that authors identify the cross-level nature of the moderating relationships, specify the level of analysis of the main relationship and the nested nature of the cross-level influences, and theoretically explain these cross-level influences. Additionally, we suggest that authors pay particular attention to nesting in order to theoretically rule out reverse interactions.

Multilevel models with persons nested in countries are increasingly popular in cross-country research. Recently, social scientists have started to analyze data with a three-level structure: persons at level 1, nested in year-specific country samples at level 2, nested in countries at level 3. By using a country fixed-effects estimator, or an alternative equivalent specification in a random-effects framework, this structure is increasingly used to estimate within-country effects in order to control for unobserved heterogeneity. For the main effects of country-level characteristics, such estimators have been shown to have desirable statistical properties. However, estimators of cross-level interactions in these models are not exhibiting these attractive properties: as algebraic transformations show, they are not independent of between-country variation and thus carry country-specific heterogeneity. Monte Carlo experiments consistently reveal the standard approaches to within estimation to provide biased estimates of cross-level interactions in the presence of an unobserved correlated moderator at the country level. To obtain an unbiased within-country estimator of a cross-level interaction, effect heterogeneity must be systematically controlled. By replicating a published analysis, we demonstrate the relevance of this extended country fixed-effects estimator in research practice. The intent of this article is to provide advice for multilevel practitioners, who will be increasingly confronted with the availability of pooled cross-sectional survey data.

This piece is meant to help you understand and master two-level linear modeling in an accessible, swift, and fun way (while being based on rigorous and up-to-date research). It is divided into four parts:* PART 1 presents the three key principles of two-level linear modeling.* PART 2 presents a three-step procedure for conducting two-level linear modeling using SPSS, Stata, R, or Mplus (from centering variables to interpreting the cross-level interactions).* PART 3 presents the results from a series of simulations comparing the performances of SPSS, Stata, R, and Mplus.* PART 4 gives a Q&A addressing multilevel modeling issues pertaining to statistical power, effect sizes, complex design, and nonlinear two-level regression.The empirical example used in this tutorial is based on genuine data pertaining to ʼ90s and post-ʼ00s boy band member hotness and Instagram popularity. In reading this paper, you will have the opportunity to win a signed picture of Justin Timberlake.

The literature on geography of innovation suggests that innovation outcomes depend on a diversity of knowledge inputs, which can be captured with the differentiated knowledge base approach. While knowledge bases are distinct theoretical categories, existing studies stress that innovation often involves combinations of analytical, synthetic, and symbolic knowledge. It remains unclear, though, which combinations are most conducive to innovation at the level of the firm and how this is influenced by the knowledge bases available in the region. This article fills this gap by reviewing the conceptual arguments on how and why certain firm and regional knowledge base combinations relate to firm innovativeness and by investigating these relationships econometrically. The knowledge base is captured using detailed occupational data derived from linked employer-employee data sets merged at the firm level with information from Community Innovation Surveys in Sweden. The results indicate that analytical knowledge outweighs the importance of synthetic and symbolic knowledge and that, however, firms benefit most from being located in a region with a balanced mix of all three knowledge bases.

Although graph convolutional networks have found application in polarimetric synthetic aperture radar (PolSAR) image classification tasks, the available approaches cannot operate on multiple graphs, which hinders their potential to generalize effective feature representations across different datasets. To overcome this limitation and achieve robust PolSAR image classification, this paper proposes a novel end-to-end cross-level interaction graph U-Net (CLIGUNet), where weighted max-relative spatial convolution is proposed to enable simultaneous learning of latent features from batch input. Moreover, it integrates weighted adjacency matrices, derived from the symmetric revised Wishart distance, to encode polarimetric similarity into weighted max-relative spatial graph convolution. Employing end-to-end trainable residual transformers with multi-head attention, our proposed cross-level interactions enable the decoder to fuse multi-scale graph feature representations, enhancing effective features from various scales through a deep supervision strategy. Additionally, multi-scale dynamic graphs are introduced to expand the receptive field, enabling trainable adjacency matrices with refined connectivity relationships and edge weights within each resolution. Experiments undertaken on real PolSAR datasets show the superiority of our CLIGUNet with respect to state-of-the-art networks in classification accuracy and robustness in handling unknown imagery with similar land covers.

Objectives Affective stress reactivity is a vulnerability factor for mental health. The central mindfulness facets, monitoring and acceptance, are associated with differential patterns of affective stress reactivity. This study investigated whether monitoring amplifies and whether acceptance attenuates affective stress reactivity in daily life. Additionally, we tested a central tenet of Monitor and Acceptance Theory, namely, whether monitoring and acceptance interact in predicting affective stress reactivity. Method A total of 211 healthy adult participants completed self-reported measures of trait monitoring and trait acceptance in person during a group session using computers. They then used a smartphone app for a 7-day ambulatory assessment of momentary stress and negative affect in their daily lives. Results We calculated separate models for acceptance facets — nonjudgment and nonreactivity. Multilevel modeling revealed cross-level interactions for both monitoring (i.e., Observing) and acceptance (i.e., Nonjudgment and Nonreactivity). Monitoring was linked to an amplification in affective stress reactivity. When monitoring was high, stress was associated with a greater increase in negative affect than when monitoring was low. Acceptance, by contrast, was linked to a buffering in affective stress reactivity: When acceptance was high, stress was associated with an attenuated increase in negative affect compared to when acceptance was low. Monitoring did not interact with either of the acceptance subscales in predicting affective stress reactivity. Conclusions The findings indicate that monitoring one’s experiences is associated with increased stress reactivity, while acceptance correlates with reduced reactivity, highlighting their relevant roles in this process. We discuss implications of these findings. Preregistration This study is not preregistered.