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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.

In this study we raise the question how a nation’s income inequality affects subjective well-being. Using information on 195,091 individuals from 85 different countries from the World Value Surveys and the European Value Surveys, we established that in general, people living in more unequal countries report higher well-being than people from more equal countries. This association however does not apply to all people similarly. First, the positive effect of a nation’s income inequality is weaker when individuals express more social and institutional trust, and underscore egalitarian norms to a larger extent. Second, the positive association between national income inequality and subjective well-being is less strong for people from countries with high levels of social and institutional trust. So, our research predominantly indicates that there are far-reaching effects of an individual’s and a nation’s trust on people’s well-being.

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.

Truck-involved crashes in Thailand frequently lead to severe consequences due to the vehicles’ large size, heavy loads, and high-speed operations. Despite growing concerns, most previous studies have used single-level models that overlook the hierarchical structure of crash data and fail to account for spatial and contextual variations across regions. This study applies a Multilevel Ordered Logit Model to examine factors influencing truck crash severity by integrating individual-level variables (e.g., driver behavior, vehicle condition, environmental factors) with province-level contextual factors (e.g., population size, AADT, Highway length). The model captures both direct effects and cross-level interactions to assess how regional characteristics shape the relationship between individual risk factors and crash severity. The results reveal substantial provincial variation and demonstrate that contextual factors significantly moderate the impact of driver behavior on crash outcomes. These findings emphasize the importance of adopting multilevel analytical frameworks in road safety research, especially in developing countries. The study contributes to a more comprehensive understanding of truck-related crash mechanisms and provides practical insights for designing targeted, context-sensitive safety policies that align with the unique characteristics of each province.