Aim: Mountains are biodiversity hotspots, and their astonishing diversity is attributed mainly to high diversification rates and longer times for speciation. Although these historical factors help explain how species accumulate, it is unclear how ecological interactions mediate species maintenance over evolutionary time. As regional diversity increases, it may reach a point where niche space limits the number of species. By implementing a multiscale framework, we tested the relative contributions of ecological and historical processes to the build-up of diversity in tropical mountains. Location: Neotropics. Time Period: Present. Major Taxa Studied: Birds. Methods: To map spatial patterns of species richness in Neotropical mountains, we choose three avian radiations (hummingbirds, nine-primaried oscines, and Tyranni), which collectively comprise similar to 2210 spp. (54% of the Neotropical avifauna). We combined phylogenetic information of these clades with geographical and morphological data as proxies for historical (diversification rates and average assemblage age) and ecological (morphological disparity and species segregation) factors and their effect on species richness using structural equation models (SEMs). Results: Even though we observed similar patterns of species richness along the elevation gradient in the three avian radiations, we found differential support for the contribution of ecological factors among clades. In the nine-primaried oscines, for example, morphological disparity was associated with species coexistence at elevations with higher diversification. In contrast, Tyranni and hummingbirds exhibited decoupling among elevational patterns of spatial and morphological divergence and historical species accumulation, suggesting how species richness is unbounded by species interactions in these clades. Main Conclusions: We found that both historical and ecological factors interact to generate and maintain high avian diversity in Neotropical mountains, but regional diversity may or may not be constrained by ecological interactions. These findings provide a link among ecological and historical factors in explaining species-richness patterns by revealing potential ecoevolutionary paths driving the maintenance of exceptional diversity in tropical mountains.
Dispersion in turbulent flows is of broad interest in engineering and environmental processes, particularly for rivers, lakes and oceanic water bodies. Based on our streamwise dispersion model grounded in a Lagrangian perspective of convection-diffusion dynamics (Guan & Chen, 2024, J. Fluid Mech., vol. 980, A33), this work presents a comprehensive solution that consistently unifies dispersion across the Reynolds number spectrum, bridging laminar and turbulent regimes. The streamwise dispersion mechanism is general across time scales, yet its statistical behaviour cannot be fully described using conventional coarse-grained moments averaged over cross-sections. While classical drift-diffusion models that are effective for long-time asymptotics fail to capture the turbulent dynamics of the pre-asymptotic phase, our analytical model enables a complete spatio-temporal characterisation of concentration, and reveals how local statistics evolve towards their asymptotic, coarse-grained limits. Through asymptotic expansions and eigenfunction analysis, we quantify the time-dependent behaviour of phenomenological dispersion coefficients, and distinguish between local and mean statistics, which diverge significantly during the pre-asymptotic phase. The early regime exhibits robust features, including an overshoot in local dispersivity, asymmetric long tails in mean concentration, and island-shaped solute accumulation near the free surface. Three regimes are identified in the evolution of the local concentration: (i) an initially uniform line source, (ii) a transitional logarithmic profile shaped by vertical shear, and (iii) an emergent Gaussian dispersion regime approaching vertical uniformity. Comparisons of both local and mean concentration demonstrate quantitative agreement with finite difference and Monte Carlo simulations across all regimes. These findings clarify the interplay between shear and turbulent diffusion, laying a foundation for addressing more intricate and physically significant transport problems.
Research has demonstrated mixed findings on the relationship between mood valence and creativity. Although two prominent theories--the dual pathway to creativity model (DPCM) and the dual-tuning model (DTM)--have emerged to explain this link, there are several challenges to understanding how mood valence and creativity operate together in the real world. Specifically, the DPCM and DTM differ in their core propositions, originate from distinct research traditions and evidence bases (i.e., DPCM is based in experimentation and DTM has roots in correlational research), blend cognitive and motivational logic, and contain predictions that differ slightly with meta-analytic evidence summarized following their conception. The present research aims to clarify the relationships between mood valence and real-world creativity using a profile-centered approach. To that end, we formed predictions based on the DPCM, DTM, and other research evidence. Using day-level data from three daily diary studies collected by different investigators (Study 1 N = 3,210; Study 2 N = 5,868; Study 3 N = 11,384), we identified four daily mood profiles (positive, negative, ambivalent, and devoid). We found consistent evidence across all three studies that daily positive and ambivalent mood states benefited creativity (measured as creative performance and creative behavior) in comparison to daily negative or devoid mood states. Implications for theory generalization and refinement are discussed.
The group of seven (G7) economies remains central to global ecological pressure despite rapid technological progress. However, the extent to which energy innovation, artificial intelligence, and fossil fuel dependence shape this pressure remains poorly understood, particularly when their effects are uneven across countries and emission intensities. To investigate the heterogeneous policy effects on the ecological footprint (ECF) among the G7 countries, this study employed the methods of moments quantile regression (MMQR) and parametric and nonparametric robustness tests to understand how the drivers influence ECF outcomes on the basis of different levels of ECF among the countries. The research relied on the G7 countries' dataset from 2000 to 2021, which was collected from trusted sources, and explored some valuable insights. The MMQR results revealed that both renewable energy innovation and its intensity effectively lower ecological pressure in high-footprint countries but have a limited impact on low-footprint economies. Artificial intelligence innovation generates a nonlinear pattern, with modest ECF reductions in low quantiles but reversed to substantial increases in high-footprint nations, which points to energy-intensive data infrastructure. The GDP per capita consistently intensifies minor ECF across all quantiles, which supports the environmental Kuznets curve pattern. These heterogeneous patterns challenge the assumption of uniform environmental responses to innovation and growth and reveal structural trade-offs between technological modernization and ecological performance. We show that effective climate strategies in advanced economies require differentiated interventions that accelerate clean innovation while actively managing the environmental rebound of digital and fossil fuel systems rather than relying on uniform market-based instruments or green growth dynamics.
PurposeThere has been a growing emphasis in both academic and industry spheres on the importance of efficient supply chain management. However, supply chains are facing unprecedented levels of upheaval and unforeseen events. From natural disasters to artificial crises, political and economic turmoil and the ever-changing landscape, uncertainties abound. Therefore, this study investigates the effects of structural risk sharing, structural collaboration, supply chain ambidexterity, dynamic reconfiguration, dynamic sensing and supply chain flexibility on supply chain resilience.Design/methodology/approachData were collected from 2,301 manufacturers across various industries in the USA. The hypotheses were evaluated using Structural Equation Modeling through Smart PLS version 4.0.FindingsAll direct and sequential mediation hypotheses were supported. Structural collaboration exerted a stronger effect on supply chain ambidexterity than structural risk sharing. Supply chain ambidexterity strongly enhanced dynamic sensing and reconfiguration, thereby improving supply chain flexibility. Notably, supply chain flexibility emerged as the strongest direct predictor of resilience. The most influential indirect pathway was structural collaboration -> ambidexterity -> dynamic sensing -> flexibility -> resilience, underscoring flexibility as the primary mechanism through which structural practices translate into resilience outcomes.Originality/valueSequential mediation introduces innovation by highlighting the impact of supply chain ambidexterity, which benefits both the firm and the broader supply chain network. Prior investigations concentrated on the internal implications of supply chain ambidexterity within a firm. Nonetheless, informed by the dynamic capability view, the variables studied indicate that supply chain ambidexterity empowers firms to navigate intricate supply chain challenges and capitalize on opportunities. This implies that firms can adapt to evolving market conditions, harmonize their operations with their objectives and enhance the resilience of their supply chains against disruptions.
