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The benefits and costs of wildlife are contingent on the spatial overlap of animal populations with economic and recreational human activities. By using a production function approach with dynamic spatial panel data models, we analyze the effects of human hunting and carnivore predation pressure on the value of ungulate game harvests. The results show evidence of dynamic spatial dependence in the harvests of roe deer and wild boar, but not in those of moose, which is likely explained by the presence of harvesting quotas for the latter. Results suggest the impact of lynx on roe deer harvesting values is reduced by 75% when spatial effects are taken into account. The spatial analysis confirms that policymakers’ aim to reduce wild boar populations through increased hunting has been successful, an effect that was only visible when considering spatial effects.

Innovation and investment are critical to economic growth. In this article, we address the complex task of evaluating the capacity of regional innovation to increase investment and generate spillovers in regions of the European Union (EU) from both spatial and temporal perspectives. Using panel data estimation methods and exploring the effects of dynamic spatial autocorrelation, our findings show a positive spatial autocorrelation at the level of EU regions. We also observed spatial competition, both in terms of the distribution of investments and in terms of the diffusion of short-term innovation gains. We argue that, in the short term, EU regions tend to behave as competitors for investment fixing, but in the long run, innovation has the potential to generate spillover effects on neighbouring regions. Furthermore, we find that investment patterns were characterized by a significant temporal autocorrelation, showing that shocks to investment in regions tend to be absorbed in a few periods. This paper attempts to fill existing gaps by using estimation methods for dynamic spatial panel data to identify and explore the effects of regional innovation on investment for the 154 European Union regions, and reports original findings as regards the knowledge spillover across European regions.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel‐wise changes within and between brain networks. Spatially dynamic brain networks show significant volumetric coupling with synchronized growth and shrinkage, referred to as dynamic spatial network connectivity (dSNC). Dynamic variability in such networks and coupling between network pairs are positively associated with cognitive performance, while showing negative association with schizophrenia, highlighting their possible role in cognitive impairment.

Aim: We combined genetic sequence data and ecological niche modelling to resolve the impacts of past climatic fluctuations on the distribution, genetic diversification, and demographic dynamics of an East Asian montane bird, the green-backed tit (Parus monticolus). Location: East Asia. Methods: Phylogenetic analyses were carried out using four mitochondrial fragments and seven nuclear loci from 161 birds sampled from 29 localities spanning the entire geographical range of the green-backed tit. We used BEAST to estimate the species tree and calculate divergence times. Extended Bayesian skyline plots were used to infer potential historical shifts in population size. We used MaxEnt to predict potential distributions during three periods: the present day, the Last Glacial Maximum and the Last Interglacial. Results: The mitochondrial DNA (mtDNA) gene tree showed strong support for three reciprocally monophyletic groups: a south-western clade, a central clade and a Taiwanese clade. Taiwanese and Vietnamese samples had fixed differences at several nuclear loci, but the south-western and central samples shared haplotypes at all nuclear loci. The mtDNA gene tree topology differed from the species tree topology. The species tree suggested sister relationships between Taiwanese and Vietnamese operational taxonomic units (OTUs) and between south-western and central OTUs. Diversification within the green-backed tit was relatively recent, probably within the last 0.9 million years. Extended Bayesian skyline plots suggested rapid population expansion in the south-western and central phylogroups after the Last Interglacial, and this result was consistent with ecological niche models. Main conclusions: Our results suggest that genetic diversification within the green-backed tit was affected by the later Pleistocene climate fluctuations. Ecological niche models indicated that the present-day vegetation distribution was, in many ways, more similar to that of the Last Glacial Maximum than it was to that of the Last Interglacial. Continental populations of the green-backed tit experienced unusual demographic and range expansion that is likely to have occurred during the cooling transition between the Last Interglacial and the Last Glacial Maximum. We found incongruence between the mtDNA gene tree and the species tree, which underscores the importance of using both mitochondrial and nuclear markers when estimating the evolutionary history of populations.

Trade-offs exist between the point of early detection and the future cost of controlling any invasive species. Finding optimal levels of early detection, with post-border active surveillance, where time, space and randomness are explicitly considered, is computationally challenging. We use a stochastic programming model to find the optimal level of surveillance and predict damages, easing the computational challenge by combining a sample average approximation (SAA) approach and parallel processing techniques. The model is applied to the case of Asian Papaya Fruit Fly (PFF), a highly destructive pest, in Queensland, Australia. To capture the non-linearity in PFF spread, we use an agent-based model (ABM), which is calibrated to a highly detailed land-use raster map (50 m × 50 m) and weather-related data, validated against a historical outbreak. The combination of SAA and ABM sets our work apart from the existing literature. Indeed, despite its increasing popularity as a powerful analytical tool, given its granularity and capability to model the system of interest adequately, the complexity of ABM limits its application in optimizing frameworks due to considerable uncertainty about solution quality. In this light, the use of SAA ensures quality in the optimal solution (with a measured optimality gap) while still being able to handle large-scale decision-making problems. With this combination, our application suggests that the optimal (economic) trap grid size for PFF in Queensland is ~0.7 km, much smaller than the currently implemented level of 5 km. Although the current policy implies a much lower surveillance cost per year, compared with the $2.08 million under our optimal policy, the expected total cost of an outbreak is $23.92 million, much higher than the optimal policy of roughly $7.74 million.

Abstract Short-term power load forecasting is essential for ensuring power system stability and facilitating market planning. However, the multi-periodic nature of load data and its complex correlations with external factors pose significant challenges to accurate predictions. To address these issues, we propose a novel spatial-temporal dynamic graph Transformer (SDGT), which integrates a multi-scale Transformer module with a patch-based multi-scale encoder to capture multiple periodic patterns and extract temporal dependencies. Additionally, a spatial-temporal correlation graph (STCG) is constructed based on shape similarity and semantic relevance, and further enhanced using a graph convolution module to model dynamic spatial correlations between load data and external influencing factors. Experimental results on two public benchmark datasets demonstrate that SDGT surpasses state-of-the-art forecasting models, exhibiting superior predictive accuracy and robustness. The findings validate the effectiveness of SDGT in capturing multi-periodic patterns and spatial-temporal dependencies, making it a promising approach for improving short-term power load forecasting and supporting real-world power system operations and energy market planning. Graphical Abstract Graphical Abstract

Social and social-spatial inequality are on the rise in the Global North. This has resulted in increasing segmentation between population groups with different social and ethnic backgrounds, and in differentiated access to cultural and material assets. With these changes, the relation between segregation in the educational sphere and segregation in the residential sphere has become crucial for understanding social reproduction and intergenerational social mobility. However, knowledge about this relation is still limited. We argue that the institutional and spatial contexts are key dimensions to consider if we want to expand this knowledge. The institutional context regards the extent of public funding, the degree to which parental choice and/or geographical proximity drive school selection, the role and status of private schools and the religious and pedagogical pluralism of the educational system. The spatial context refers to the geographies of education: the ethnic and social composition of school populations and their reputations; the underlying levels and trends of residential segregation; and the spatial distribution of schools in urban space. In this introduction to the special issue we will address these interrelated dimensions, with reference to theoretical and empirical contributions from the existing body of literature; and with reference to the contributions in this special issue. School segregation emerges from the studies included in this special issue as a relevant issue, differently framed according to the institutional and spatial contexts. A comparative typology will be proposed to illustrate how school segregation is peculiarly shaped in different national and local contexts. 全球北方的社会和社会空间不平等正在加剧。这导致具有不同社会和种族背景的人口群体之间的日益分割，以及文化和物质资产获取方面的差异。伴随着这些变化，教育领域的隔离和居住领域的隔离之间的关系对于理解社会再生产和代际社会流动变得至关重要。然而，关于这种关系的知识仍然有限。我们认为，如果我们想扩展这种知识，制度和空间环境是需要考虑的关键因素。制度环境涉及公共资金的规模、父母选择和/或地理邻近程度决定学校选择的程度、私立学校的作用和地位、以及教育系统的宗教和教育多元化。空间环境指的是教育方面的地理学：学校人口的种族和社会构成及其声誉；居住隔离的潜在水平和趋势；以及学校在城市空间中的空间分布。在本期特刊的导言中，我们将参考现有文献的理论和经验贡献以及本期特刊的贡献，探讨这些相互关联的方面。学校隔离作为一个相关问题出现在本期特刊的研究中，并根据制度和空间环境的不同而构建。我们将提出一种比较类型学来说明学校隔离是如何在不同的国家和地方背景下形成的。

Research questions in resource economics increasingly have incorporated both the field's traditional dynamic considerations and the spatial concerns that often are the focus of regional economics. Spatial heterogeneity in costs, benefits, or connectivity often interact with intertemporal change in contexts such as fisheries management, water allocation, invasive species control, and land use change, making spatially- or temporally-uniform polices less likely to be efficient. In this article, we examine how climate change is likely to enhance the need to incorporate spatialdynamic approaches to address natural resource challenges. We focus our discussion on rural areas, which are typically highly dependent on natural resources and particularly vulnerable to climate change. Following a brief review of existing spatial-dynamic models in natural resource economics and the insights derived from them, we describe how climate change can bring new spatial or temporal aspects to resource management problems, or exacerbate existing resource challenges that are best characterized as spatial-dynamic processes. We conclude with three case studies that highlight how integrating resource and regional economics through spatial-dynamic modeling may improve the analysis of climate change impacts in rural areas, considering the effects on rangeland management, groundwater policy, and land use management in floodplains and coastal areas.

Hand-foot-mouth disease (HFMD) is the most common and widespread infectious disease in the People's Republic of China. Although there has been a substantial increase of HFMD in many parts of the country in recent years, its spatial dynamics and determinants remain unclear. When we collected and analysed weekly data on HFMD cases from 1,456 counties and the corresponding meteorological factors from 1 May 2008 to 27 March 2009, it was found that HFMD was spatially dispersed across the country in the summer and winter, while clustered in the spring and autumn. The spatial variation of HFMD was found to be affected by a combination of climate variables, while its spatio-temporal transmission was largely driven by temperature variations with a 7-week lag implying that (i) the dispersal of the disease can be anticipated based on the variation of the temperature and other climate variables; and (ii) the spatial dynamics of HFMD can be robustly predicted 7 weeks ahead of time using temperature data only. The findings reported allow prompt preparation and implementation of appropriate public health interventions to control and prevent disease outbreaks.

Coupled human and natural systems (CHANS) are complex, dynamic, interconnected systems with feedback across social and environmental dimensions. This feedback leads to formidable challenges for causal inference. Two significant challenges involve assumptions about excludability and the absence of interference. These two assumptions have been largely unexplored in the CHANS literature, but when either is violated, causal inferences from observable data are difficult to interpret. To explore their plausibility, structural knowledge of the system is requisite, as is an explicit recognition that most causal variables in CHANS affect a coupled pairing of environmental and human elements. In a large CHANS literature that evaluates marine protected areas, nearly 200 studies attempt to make causal claims, but few address the excludability assumption. To examine the relevance of interference in CHANS, we develop a stylized simulation of a marine CHANS with shocks that can represent policy interventions, ecological disturbances, and technological disasters. Human and capital mobility in CHANS is both a cause of interference, which biases inferences about causal effects, and a moderator of the causal effects themselves. No perfect solutions exist for satisfying excludability and interference assumptions in CHANS. To elucidate causal relationships in CHANS, multiple approaches will be needed for a given causal question, with the aim of identifying sources of bias in each approach and then triangulating on credible inferences. Within CHANS research, and sustainability science more generally, the path to accumulating an evidence base on causal relationships requires skills and knowledge from many disciplines and effective academic-practitioner collaborations.